Using Predictive Modeling to Reduce Claims Losses in Auto Physical Damage

Transcription

1 Using Predictive Modeling to Reduce Claims Losses in Auto Physical Damage CAS Loss Reserve Seminar 23 Session 3 Private Passenger Automobile Insurance Frank Cacchione Carlos Ariza September 8, 23

2 Today s Agenda Defining the opportunity Approach to Auto Physical Damage claims leakage reduction Modeling methodology Technical Design Typical Process Appendix: Application to Bodily Injury Claims PA Knowledge Limited 23-2

3 Powerful new predictive modeling tools are available to capture operational improvement across most elements of the claims process Technology enabling value drivers for Total Incident Management Fraud Management Litigation Management Predictive modeling can provide fraud "flags Technology can enable insurers to better determine litigation strategy Notification and Assignment Early notification Predictive triage Direct to network Verification of coverage Customer service UM pursuit Investigation and evaluation Predictive modeling Image engineering Adjuster variance Negotiation and settlement Colossus/ICE Lawyer profile Source law firms Motor repair network Salvage/Subrogation Total loss assessment Significant operational improvement requires rapid determination of how a claim should be handled and intensive effort to reduce the variance in adjustors evaluation of loss Predictive models can aid the claim representative in determining the likelihood of overpayment and suggest strategy options Assignment Triage Adjuster Calibration Case Management Assignment of Claim to Appropriate Track SIU Fast Track Regular BI Staff Examiners Typical Situation Target End- State Investigation and Evaluation Invest. Eval. Negot. Assignment of Claim to Appropriate Rep Sue Pete Independent Examiners & Preferred Body Shops # of Claims $ Estimate # of Claims $ Estimate Key Overpayment Drivers What to investigate Settlement offer range Manager Review Mary PA Knowledge Limited 23-3

4 Recent studies estimate that Auto Physical Damage represents over 2% of all claims leakage. Losses from Claim Leakage Property and Casualty Insurance $ Million 1% = $3 Billion Estimates Other Coverages (Mainly: workers compensation, health insurance, auto BI, homeowners insurance) Public Public Perceptions Perceptions (All (All Americans, Americans, 2 2 Survey): Survey): 24% 24% think think it it is is okay okay to to overstate overstate insurance insurance claims claims to to make make up up for for past past premiums premiums 35% 35% think think it it is is acceptable acceptable to to pad pad claims claims to to make make up up for for deductible deductible Physical Damage $6 7 Billion In In California*: California*: 43% 43% of of all all vehicles vehicles inspected inspected showed showed evidence evidence of of overwrite overwrite by by an an auto auto body body shop shop Fraudulent Fraudulent repairs repairs equaled equaled $586 $586 per per vehicle vehicle Depending on on the the location, about about a third third to toa half half of of all all physical damage claims claims have have substantial leakage Losses from from Hard Hard fraud fraud represents a small small part part of of this this leakage Efforts Efforts to to date date to to eliminate this this leakage have have had had only only limited success Source: National Insurance Crime Bureau; California Department of Consumer Affairs; California Bureau of Automotive Repair * California Bureau of Automotive Repair, ongoing auto-repair re-inspection program. Numbers are as of December 31, 21 PA Knowledge Limited 23-4

5 PA s experience indicates that, despite efforts to improve performance, insurers generally continue significantly overpaying Auto Physical Damage claims by up to 9%. Motor Physical Damage: Distribution of Estimate Amounts Illustrative Performance Challenges Inadequate Controls (I.e., field reinspections and desk reviews) Inconsistent estimates across appraisers and appraiser types, due to broad range of skills Wide range of accident types and physical damage profiles Staff Examiners Preferred Body Shops # of Claims Typical Situation Independent Examiners Emphasis on customer service issues, sometimes at the expense of proper control and safeguards $ Estimate Desired Situation Significant overpayment (up (up to to 9%*) continues in in Auto Physical Damage for for most insurers # of Claims $ Estimate * Based on reinspections by client teams of estimates by top front-line claims personnel PA Knowledge Limited 23-5

6 Today s Agenda Defining the opportunity Approach to Auto Physical Damage claims leakage reduction Modeling methodology Technical Design Typical Process Appendix: Application to Bodily Injury Claims PA Knowledge Limited 23-6

7 Despite global use of training and corrective tools such as reinspections, significant differences still exist between adjusters estimating the claim from the same auto damage event. Estimate Breakdown: Examples of Differences Disguised Estimator 1 Estimator 2 Estimator 3 Bumper Overhaul (1.1 hours), LKQ assembly for $187.5,2.5 paint hours Replace (.7 hours) LKQ assembly for $13, 3.5 paint hours Replace (.7 hours) LKQ assembly for $16, inspect LKQ (.3 hours) 2.5 paint hours Lamps Replace (.3 hours), LKQ tail lamp for $62.5 Replace (.5 hours), LKQ tail lamp for $1 Rear Body Panel Not written Repair body panel (4 hours), 1.7 paint hours Replace (.5 hours), LKQ tail lamp for $6. Replace 5.2 hours), OEM body panel ($133.27), 1.4 paint hours Real Floor Pan Not written Not written Repair (2.5 hours) floor pan assembly, 2 paint hours Quarter Panel Repair body panel (7 hours) 2.2 paint hours Other Pinstripe ($1), Flex Material ($12), Car Cover ($5), Hazmat ($4) Estimate Total (Indexed) Repair body panel (6 hours), 2.2 paint hours Set up & Pull (2 hours), stripe tape ($1), blend (.5 hours), seam sealer ($8), undercoating ($15) Replace (9.4 hours), LKQ quarter panel($16), 2.6 paint hours Paint fuel door (.3 hours), flex coat ($12), Hazmat ($4), stripe tape ($15), corrosion protection ($15), seam sealer ($8) Note: LKQ refers to salvaged parts Source: Test of sample claims with representative claims organization PA Knowledge Limited 23-7

8 When several adjusters were compared in a blind, quantitative process, startling variations were found in the appraisal performances. Measuring Estimator Performance Disguised Example Damaged Damaged vehicles vehicles representing representing different different ages, ages, type type and and damage damage locations locations were were collected collected in in a a central central location location Repair Estimates Proportional Scale Adjuster A Adjuster B Adjuster C Estimators Estimators were were asked asked to to provide provide repair repair estimates estimates for for every every vehicle. vehicle. Different Different types types of of estimators estimators (i.e., (i.e., insurer, insurer, DRP, DRP, non-drp non-drp and and independent) independent) were were chosen chosen Estimators Estimators were were not not allowed allowed to to share share data data or or observations observations Estimate Amount Significant Significant differences differences between between groups groups (internal (internal and and external external adjusters), adjusters), as as well well as as within within each each group group were were observed observed Source: Test of sample claims with representative claims organization Car # PA Knowledge Limited 23-8

9 This problem of high variance and overpayment in Auto Physical Damage losses can be addressed only through desired Behavior Modification on the part of appraisers and shops. Importance of Behavior Modification Key Issues in Auto Physical Damage Losses Claims losses are primarily determined by a large number of decentralized adjusters, with different performance levels The process depends significantly on the expertise of adjusters Heterogeneity of claims implies difficulty in designing predictive modeling tools for several risk segments Field reinspections and desk reviews tend to be marginally effective because the resources are limited and it is difficult to allocate them effectively The key key objective of of any any corrective strategy has has to to be be Behavior Modification through proper controls and and associated training, communications, incentives, etc. etc. PA Knowledge Limited 23-9

10 One tool used for control is the reinspections of estimates. Unfortunately, reinspections do not identify most overpayments, nor do they always achieve the desired behavior modification. Key Issues with Reinspections Unsophisticated claim selection procedure: reinspection resources tend to focus on the largest $ amount claims, or those that are conveniently accessible on a given day, rather than on those with the maximum potential likelihood of overwriting. Limited reinspection resources: Reinspection effectiveness is generally limited because of limited resources, and appraisers /shops knowledge of these limitations No single reinspection process: reinspection process may vary between appraisal types (different for staff/internal appraisers from others), making it difficult to evaluate appraisers consistently Inefficient results capture: Reinspection results are not captured in a single place, generally, and may be saved in different systems for different markets. This makes it difficult to analyze the data for identifying trends or developing corrective strategies Estimates are typically selected either randomly or through a pre-set and generally known dollar limit, which means that: Several of the most problematic claims do not get reinspected, and so savings are not maximized Reinspections tend to create limited desired modification in partner/vendor behavior PA Knowledge Limited 23-1

11 PA tackles reinspections differently, by using a sophisticated model to estimate the overwriting probability of each estimate, and then allocating limited reinspection resources accordingly. PA s Approach to Targeting Auto Physical Damage Leakage through a Probabilistic Model Expert decision process information is gathered from experienced adjusters Historical claims data is collected for model Model is designed, based on a probabilistic Bayesian framework, using historical claims data Model is trained on historical reinspection results Links are created to allow model to function dynamically with fresh daily claims data, and to learn with data Model results are fed into reinspection resource allocation process, resulting in increased hit rates and savings identified Over time, process leads to behavior modification and a significantly improved estimation process PA Knowledge Limited 23-11

12 Our predictive modeling solution directly integrates with the client claims management system, creating a tool and a process for ongoing claims evaluation and selection for reinspections. Staff Appraisals Independent Appraisals DRP Appraisals Appraisals Uploaded into Claims System Reinspections Process With PA s Predictive Model: Illustrative Data Flow PA s Predictive Model Model Recommendations (incl. Random*) Reinspections Dispatch Recommended desk reviews Recommended field reviews Desk Reviews Desk reviews requiring field reinspections Savings Identified/ Realized Field Reinspections * Typically, a fixed % of random recommendations is added to the mix in order to ensure reinspections of all shops and appraisers over a period of time PA Knowledge Limited 23-12

13 Today s Agenda Defining the opportunity Approach to Auto Physical Damage claims leakage reduction Modeling methodology Technical Design Typical Process Appendix: Application to Bodily Injury Claims PA Knowledge Limited 23-13

14 Our methodology is based on Bayesian Networks, which provide consistent statistical treatment to both hard data and experts assumptions about the likelihood of overwriting. What are Bayesian Networks? BNs are models that represent uncertainty in our knowledge: Uncertainty from experts knowledge Uncertainty about the domain being modeled Uncertainty from the knowledge engineer (aka model builder) Uncertainty about accuracy and availability of the knowledge (Source: Russel Greiner, U. of Alberta, Canada) BNs are conditional probability models Bayesian inference consists of updating prior beliefs as new information becomes available BNs are able to learn from new data Rev. Thomas Bayes ( ) is responsible for the Bayes Theorem, which is the basis for conditional probability theory BNs are graphical causal models Bayesian methods have been available for a long time, but tend to be computationally difficult Only in the last 1 years has software become good enough to develop useful applications that can be solved in a reasonable time PA Knowledge Limited 23-14

15 Bayesian networks use conditional probabilities to represent what we usually call common sense they have found a warm reception in Artificial Intelligence circles Why Bayesian? Rev. Thomas Bayes stated in 1761 that P (A B) = P (B A) * P(A) / P(B) The Bayes Theorem allows easy manipulation of conditional probabilities, which are critical to develop reasonable systems. An example from the Physical Damage claims world: The fact that there is a lower than expected amount of used parts in a vehicle repair estimate increases the probability of overwriting. However, if this vehicle is a rare model, then the probability decreases, because we don t expect to easily find LKQ parts for it. Conditional on rarity, however, the LKQ content seems appropriate Rare vehicle Without knowing rarity, the LKQ content is deemed too low, and worthy of reinspection Partial view of the predictive model LKQ content lower than expected PA Knowledge Limited 23-15

16 Bayesian Networks have been applied in diverse areas of engineering, medicine and business Some examples of applications: Medical diagnostics Language understanding SPAM filtering Machine vision Fraud detection Operational risk management Pharmaceutical drug discovery The classic Chest Clinic Bayesian Network for medical diagnosis* Online customer service Predictive maintenance Fault analysis for complex machinery * (From Norsys s Netica tutorial, based on S. L. Lauritzen and D. J. Spiegelhalter. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B (Methodological), 5(2): , 1988). PA Knowledge Limited 23-16

17 The power of today s computers allows an old theory to be applied to a modern problem. Benefits of Bayesian Networks: BNs replicate cause-and-effect relationships seen in real life, and identified by experts familiar with the industry BNs rely on a rigorous probabilistic structure, so conflicting evidence can be weighed appropriately instead of biasing the results BNs can generate results with incomplete data, and account for uncertainty in the inputs appropriately BNs stay current because of their ability to learn from new data as it is generated, reducing maintenance requirements BNs keep all assumptions visible and make them easy to share with users, even those without technical training PA Knowledge Limited 23-17

18 BNs are ideally suited for Auto Physical Damage Claims analysis Claims data is usually incomplete, and traditional statistical tools do not handle incomplete information well Experts can quickly identify overwriting in auto insurance claims, but have a difficult time making their process explicit modeling with BNs allows companies to capture experts mental models and replicate them across the company, standardizing best practices Historical reinspections results are usually patchy and unstructured a system based on BNs can start with a model created from experts opinions, and validate them as new results are collected Early in the development of the system we explored the possibility of using a more traditional method, such as logistic regression, to estimate the probability of overwriting However, we found logistic regression too rigid for this application, since it has more stringent data requirements Additionally, incorporating the experts knowledge requires a hierarchical approach to model development, which is difficult to represent using traditional regression tools PA Knowledge Limited 23-18

19 Bayesian networks are superior to alternative modeling methodologies. Features Gut feel and intuition Regression models Rules-based expert systems Neural networks Bayesian networks Can incorporate expert knowledge Can handle quantitative data inputs Cause and effect explicitly represented Non-linear relationships can be easily represented Continuous (fuzzy) logic allowed Can handle incomplete data sets Can learn from data PA Knowledge Limited 23-19

20 PA Knowledge Limited The predictive model is built by combining expert knowledge with quantitative data. Model Design and Construction Claims and Policy data Appraiser information Policy, claim and other data is gathered to incorporate in the predictive model. Data sources for possible inclusion in model Examiners and Repair Facilities Appraisal information Examiner Group Characteristics Labor Parts Estimate Independents details.. Date of joining Labor Rates Amount Prior incidences Types of Labor Type and frequency Experience level PRS Shops Labor Hours Repair/replace rules Expertise focus Training level Staff Examiners Historical ratios Repair Shop Characteristics Repair Facilities Relationship with company Relationship with other carriers Part of a dealership or network Geographical information Total Loss Threshold Amount Vehicle Valuation Prior Damage Miscellaneous Repair guidellines Betterment Mileage Illustrative Accident Profile Characteristics Vehicle Make Model Year ACV Driver Age Sex Prior claims Violations Accident Type Comprehensive Low vs. high impact Single-car vs. multiple $ size of damage claim relative to medical claim Policy and Receivable Information Limits Deductible Vehicle new to policy Premium current? Payment basis? Expert Interviews PA s experience Qualitative model Quantitative model The qualitative model represents how different inputs potentially relate to each other These relationships are represented graphically and validated through interviews The quantitative model turns the relationships between inputs into probabilities, which are calibrated using claims data and other sources PA Knowledge Limited 23-2

21 The key starting point is to tap into the accumulated knowledge of your Material Damage claims experts. Breaking down the expert decision process I know a good repair shop when I walk in the door These new staff appraisers always seem to miss the little things that can add up I know a Total Loss when I see it I always jump on those positive supplements that come in late If they all just did what I tell them to do, we wouldn t need you consultants PA Knowledge Limited 23-21

22 Bayesian networks provide an explicit way of balancing expert knowledge with hard data Illustrative Prior distribution from expert knowledge is uniform Confidence on prior expert knowledge is equivalent to 1 data point Model reacts quickly to new data when there is little confidence on prior knowledge 1 data point 1 data points 1 data points 1 data points Confidence on prior expert knowledge is equivalent to 1 data points If there is more confidence on prior knowledge, the model reacts more slowly to new data Note: Data points in this example come from a random Normal(4,5) sample PA Knowledge Limited 23-22

23 PA Knowledge Limited Expert interviews provide the base for creating the Bayesian Network, which captures a rich set of cause-effect relationships between different data inputs C L _ V e hi cl e_ h a s_ P ri or _ Dm g C L _O l d_ V e hi cl e C L _ V al u a bl e_ V e hi cl e 1 I E _c l os e _t o _T L 1 C L _ A C V _t o _M e di an _I n c om e t o. 5 > =. 5 1 I E _c l os e _t o _P o li cy Li m 1 L o w _ P ol ic y Lim 1 C L _ is _ n ot _ Ri s k_ A v er se C L _ n um _ cl aim s_ t o_ d at e t o 1 > = 4 1 C L _ n um _ yr s_ a s_ c u st < 1 > = 7 1 B i li n g _M e t h od m on th ly d d ir e ct b 1 2 N u m be r _I ns u re d t o 1 > = 4 1 C L _ h a s_m u c h_ e x pe ri e nc e C L _ A t a c hme n t_ is _W e ak O nl y_ o n e_ i ns u re d 1 C L _ is _ n ot _ L on g tim e _c u st 1 B i l _Me t h od _i s _ Ri sk ie r 1 C L _w il i ng _ to _ pr o v _f al se _i n f o C L _ P r ofi le _ Ri s k_ is _ Hi g h A c c id e nt _ d es c _u n re li a bl e A c c _ D es c _c a n_ b e _i na c c P o li c e_ R e p or t_ A v ail a bl e 1 N u m _V e h _I nv o lv e d 1 W it n es s es _l ik el y 1 M or e _t h a n_ Tw o _I nv o lv e d 1 M or e _t h a n_o n e_ I nv ol v e d 1 W it n es s es L a c k_ o f_ Wi tn e s e s C L _ I nd iv _ Ri s k_ is _ Hi g h C L _ A g e t o 2 2 t o t o 3 3 t o t o t o 75 > = 75 1 C L _ is _ at _ Ri s ky _ A ge C L _ is _ Si n gl e 1 C L _ L a ck s _ Dri vi n g _ Ex p 1 C L _ is _ Fi n a nc _ U ns t ab le C L _ R e nt s _ Re si d e nc e 1 C L _ F I CO _ S c or e t o to to to 7 7 to to 8 5 > = 85 1 C L _ L ow _ FI CO _S c or e C L _ h a s_ L ow _I nc om e C L _ V e hi cl e_ R is k _i s_ H i gh C L _ P ol ic y _ Ri sk _i s _ Hi g h A v g _ L ab o r_ R a te _ B o dy t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 A v g _ L ab o r_ R a te _ Fr am e t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 D i f _ La b o r_ R at e _ B od y < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 D i f _ La b o r_ R at e _ Fr am e < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 D i f _ La b o r_ R at e _ Pa in t < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 C L _ cl ai ms _ pe r _y r t o. 5. 5t o 1 1 t o t o 2 1 F r am e_ a s_ F ra c ti o n_ o f_ T ot al t o t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o 1 1 R o a d _ C o nd _ R e po rt e d D I W S 1 D i f ic ul t _ Dri vi n g 1 D i f ic ul t _W ea t he r 1 D i f ic ul t _ Ro a d 1 C o s tl y _ Re p ai rs _ Li ke ly T h e ft 1 B I _ C om p on e nt W e at h er _ Re p o rt e d C F I R S 1 R o a d _ C ha r ac te ri st ic s R C L G S H 1 S p e e d_ L im i t t o t o t o 3 3 t o t o 4 4 t o t o 5 > = 5 1 D i f ic ul t _ Ro a d _ Ch a r 1 D i f ic ul t _ Ro a d _ Co n d 1 H i g h _S p e ed F r am e_ L a b or _I n co n si st _w _ E D L a b o r_ H o ur s _ Fr am e < t o 5 5 t o t o 1 1 t o t o 2 > = 2 1 L a b o r_ H o ur s _ B od y < 5 5 t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 > = 7 1 L a b o r_ C o st _ Fr am e < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 L a b o r_ C o st _ B o dy < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 L a b o r_ R at e _ B od y t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ C o st _ Pa i nt < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 A v g _ Fr a c_ B P L _ of _ LP _ to t al t o t o t o t o t o 1 1 L a b o r_ C o st _ B an d P < 2 2 to 5 5 to 1 1 t o 2 > = 2 1 F r a c_ B P L _o f _L P _t o ta l < t o. 5. 5t o t o 1 1 L a b o r_ R at i os _ ar e_ t o o_ H ig h T o o _M u c h_ L a bo r D i f er e n ce _wi t h_ H is t_ B P L _ Pa rt s < t o t o t o t o t o t o t o t o t o t o t o t o. 46 > = L a b o r_ R at e s_ H i gh e r_ t ha n _ Av g C l aim _ o ve rw r it te n A P _w il i ng _ to _ c ol u d e A P _ E x pe ri e nc e t o 1 5 t o 7 7 t o 1 1 A P _ P r ofi le _ Ri s k_ is _ Hi g h A P _ E x pe ri e nc e _i s_ L ow 1 A P _ r el _w _ In s _ C o_ is _W e ak N u m _o f _ AP _i n _ A P_ C om p a ny t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1 1 A P _ C om p a ny _i s _ La r ge _O I A 1 A P _ C o _i s _V e ry _ Sm al _O I A 1 A P _ C om p a ny _i s _ Ve ry _ Sm al 1 A P _ C om p a ny _i s _ La r ge 1 A P _ Y rs _W o rk i ng _w _I n s_ C o t o 1 > = 7 1 A P _ is _ ca r el es s A P _ N um _ A p pr _ d on e _ pe r_ mo n t h t o 1 2 t o 5 5 t o 1 1 t o 2 2 t o 4 4 t o 8 > = 8 1 A P _ V ol um e _i s_ L ow O d om_ U n re a d ab le S i g ni f_ El e ct r_ D am a ge O d om_ is _ Di g it al V e h ic le _M i le a g e t o 1 1 t o 1 1 t o 2 2 t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1e 5 e 5 1 L a b o r_ H o ur s _ Pa in t < 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 > = 5 1 L a b o r_ H o ur s _ El ec tr < 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 > = 5 1 L a b o r_ R at e _ El ec tr t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ C o st _ El ec tr < 1 1 t o 2 2 t o 5 5 t o 1 1 to 5 5 to 1 1 t o 2 > = 2 1 Mi le a g e_ is _Mi s si n g 1 M i le a g e_m is s _ du e _t o _c a re le s s 1 N u m _ Ap p r_ d o n e_ t ha t_ d a y t o 1 7 t o 8 8 t o 9 9 t o 1 1 t o t o 2 > = 2 1 N u m _ Ap p r_ p er _ D ay _ A v g t o 3 3 t o t o t o t o t o 4 4 t o t o t o t o t o 5 5 t o t o t o t o t o 6 6 t o t o 7 7 t o 1 1 t o 2 > = 2 1 D i f _ N um _ Ap p r_ fr om _ A v g < -3-3 to to to to t o. 5. 5t o 1 1 L o g i n_ N am e_ is _ S ha r ed 1 A P _ T o o _M a n y _ Ap p ra is al s 1 T o o _m a ny _ fo r _On e _ A P_ p er _ D ay 1 L K Q _ P ar t s_ C o st < 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 A M _ Pa rt s _ Co s t < 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 T o t al _ Pa rt s _ Co s t < 5 5 to 1 1 t o 2 2 t o 4 > = 4 1 D i f _ Fr _ L KQ _ v s_ E x pe c te d < t o to t o t o t o. 1. 1t o. 1 5 > = F r a cti o n _ AM < t o to.1. 1t o to t o to.2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 7. 7t o. 8. 8t o. 9. 9t o 1 1 E x p e ct e d_ L KQ _ Fr _ by _ Yr t o to to to to to to to.2. 2t o to to to to.3. 3t o to to.3 4 > = E x p e ct e d_ AM _ Fr _ b y_ Y r t o to to to to to t o to to to to to to > = D i f _ Fr _ AM _v s _ Ex p ec t ed < t o to t o t o t o. 1. 1t o. 1 5 > = F r a cti o n _ L KQ <. 1. 1t o. 2. 2t o t o. 3. 3t o t o. 4. 4t o t o. 5. 5t o. 6. 6t o. 7. 7t o. 8. 8t o. 9. 9t o 1 1 V e h ic le _i s _ Ra re L e s s_ L KQ _t h a n_ E x p L e s s_ A M _ t ha n _ Ex p 1 S i g ni f_ P ar ts _ Di f S i g ni f_ P ar ts _ Di f _ ra re C a s e _i s_ IE 1 E x t en t _o f _ Dam a g e_ is _ Hi g h D A P _ U n d er bi d _I E 1 A P _ S _I E _ Pc t_ D if f_ H is t < -1-1 to to to t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 8. 8t o 1 > = 2 1 A P _ H is t _ Un d er b id _I E 1 A P _ R F _ Pa ir e d_ F re q u e ntl y A P _ R F _ Pa ir in g s _l as t_ Y e ar t o 1 1 t o 5 5 t o 1 1 R F _ Me di a n _I nc om e t o 2 2 t o t o 3 3 t o t o t o 4 4 t o t o 5 5 t o 6 6 t o 7 7 t o 8 > = 8 1 R F _ N h o o d_ R is k _i s_ H i gh R F _ N h o o d_ R is k y_ I nc om e 1 R F _ N h o o d_ H i gh _ C rim e 1 R F _ A v g _M on t h_ V ol _w _ C o_ P 6M t o 1 1 t o 5 5 t o 1 1 t o 2 > = 2 1 R F _ M o n t hl y_ V ol _ Al l_ C ar ri er s < t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1 1 to 2 > = 2 1 R F _ a n d_ C o _ R el ati o n _W ea k R F _ C o _ Fr _i s _L ow R F _ Mo n t hl y_ V ol _i s _L ow R F _ F r_ V ol _w _ C o < t o t o. 1. 1t o. 2 > =. 2 1 R F _ I nd iv _ Ri s k_ is _ Hi g h R F _ P r ofi le _ Ri s k_ is _ Hi g h R F _w il i ng _ to _ c ol u d e R F _w il i ng _ to _ n ot _f o l ow _ a p p A P _ R F _ R el ati o n _i s_ S tr o n g 1 A P _ g iv e s_ t o _m a ny _ c on c A P _ g ui d e d_ b y _ RF R e p ai r _i s_ C om pl e x M ak em o d el _F r eq u e nc y t o to to to t o t o t o. 4 > =. 4 1 P c t _ Dif f _S _ to _I E _ Vs _ Hi s t < to to t o t o. 1. 1t o. 2. 2t o. 5. 5t o 1 1 A P _ T o d ay _ U n de r bi d_ I E 1 S _ I E_ D if f_ Si g ni f _g iv e n _ Co mp le x 1 A P _ A v oi d s_ 2 n d_ V is it 1 V a l ue _I n c on si s te n t_w _ D ama g e R e p ai r _V al u e _i s_ H ig h 1 I E _t o _ A C V_ R at i o <. 5. 5t o. 8 > =. 8 1 A P _ S u p _t o _I E_ P ct _ Di f < -1-1 to to to t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 8. 8t o 1 > = 2 1 A P _ T e n ds _t o _W ri te _ Hi g h _I E 1 A P _ f ail e d_ t o_ s av e A v g _ Di f _ L ab o r_ R a te < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 C L _w il i ng _ to _ c ol u d e A P _ u nl ik el y _t o _ ha v e_ E x p_w _ Ve h R F _ C rim e _ Ra t e t o 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 C L _ N h o o d_ R is k y_ I nc om e 1 C L _ N h o o d_ H i gh _ C rim e 1 C L _ N h o o d_ Mo s tl y_ U r ba n 1 C L _ N h o o d_ R is k _i s_ H i gh C L _ Me di a n _I nc om e t o 2 2 t o t o 3 3 t o t o t o 4 4 t o t o 5 5 t o 6 6 t o 7 7 t o 8 > = 8 1 C L _ C rim e _ Ra t e t o 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 C L _ F r_ B el ow _ P ov er t y t o t o. 1. 1t o t o. 2. 2t o. 4 > =. 4 1 C L _ P ct _ U rb a n t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 7 t o 8 8 t o 9 > = 9 1 R F _ F r_ B el ow _ P ov er t y t o t o. 1. 1t o t o. 2. 2t o. 4 > =. 4 1 R F _ N h o o d_ Mo s tl y_ U r ba n 1 R F _ P ct _ U rb a n t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 7 t o 8 8 t o 9 > = 9 1 C L _ R is k y_ P L ev el R F _ R is k y_ P L ev el R F _ h a s_ L ow _V o lu me R F _ is _ Sm al R F _ is _ D A P 1 R F _ N o t_ P ar t _o f _ De al er s hi p 1 R F _ N o t_ P ar t _o f _ Ne tw or k 1 R F _ N um _ Em pl o ye e s t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o R F _ L a b or _ Ra t es _ L ow e r R F _ h a s_ F ew _ Em p lo y e s I E _t o _ Po li cy L im _ R a ti o t o. 8 > =. 8 1 C L _ V e hi cl e_ Y rs _Ol d < 1 7 t o 8 8 t o 9 9 t o 1 1 t o t o t o t o t o t o D e d u ct i bl e t o 1 1 to to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 C o l is i on _ P oli c y Lim t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1e 5 e 5 1 C L _ V e hi cl e_ A C V t o 1 1 t o 2 2 t o 4 4 t o 5 5 t o 8 8 t o 1 1 t o t o t o t o 2 2 t o t o 3 3 t o 4 4 t o 6 > = 6 1 L o s s _Y e ar < 2 2 1t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o 2 2 > = V e h ic le _ Ye a r < t o t o t o t o t o t o t o t o t o t o t o t o 2 2 t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o 2 2 > = C a p t ur e d_ i n_ P h ys R C a p t ur e d_ i n_ P h ys R _ T S C a p t ur e d_ i n_ D e sk R P h y s R _mi s e s_ S a vi n gs P h y s R _T S _mi s se s _ Sa vi n g s M at c he d _i n _ Ph y s R M at c he d _i n _ De s k R M at c h_ S c or e _P h y s R_ T S t o M at c h_ S c or e _ De s k R t o M at c h_ S c or e _P h y s R t o C L _ n o t_ at _ F au lt 1 C L _ is _ n ot _i n s ur e d 1 M aj or _ D am ag e _ Li ke ly 1 J o b _m u ch _ hi g h er _t h a n_ A C V 1 J o b _t o _ A C V_ R a ti o < 1 1 t o t o t o t o M at c he d _i n _ Ph y s R_ T S T o t al _ La b or _ H o ur s < 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 O ut p ut _ Pr o b a bil it y T L _O v er ri de 1 A P _ mi s e d_ s av i ng s A P _ m i s e d_ L a b or _ Sa vi n g s A P _ mi s e d_ p ar t s_ s av A P _ T y pe I A S A D A P 1 P r o ce s s_ S u p _I D t o 1 > = 2 1 V e h _i s _T o ta l_ L o s 1 J o b _ Hi g h er _t h a n_ A C V _ no t _T L 1 I E _J o b _ To t al _ Amt < t o 5 5 to to 1 1 t o 2 2 t o 3 3 t o 5 5 t o 7 5 > = 75 1 J o b _ T ot al _ Am t < t o 5 5 to to 1 1 t o 2 2 t o 3 3 t o 5 5 t o 7 5 > = 75 1 I n s ur e d_w a s_ R e ck le s s 1 C i ta ti o n _i s_ f or _I n su r ed 1 C i ta ti o n _i n di c_ re c kl es s 1 W h o _P o li ce _ R ep o rt _ Vi ol I V P B O U E N 1 A v g _ L ab o r_ R a te _ Pa i nt t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ R at e _ Fr am e t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 R F _ d id n t_ f ol ow _ ap p ra is al C L _ A P _ co l u de d A P _ R F _ co l u de d C L _ mi sr e po rt e d _a c ci de n t L a b o r_ R at e _ Pa in t 2 t o t o 3 4 t o t o 5 6 t o t o 7 1 P a rt s _ Th r es h ol d < 1 4 to 5 > = 5 1 T o t al _ Pa rt s _ Hi g he r_ t ha n _ T hr es 1 T o t al _ Pa rt s _T h re s _ Di f C L _ V e hi cl e_ h a s_ P ri or _ Dm g C L _ V e hi cl e_ h a s_ P ri or _ Dm g C L _O l d_ V e hi cl e C L _ V al u a bl e_ V e hi cl e 1 I E _c l os e _t o _T L 1 C L _ A C V _t o _M e di an _I n c om e t o. 5 > =. 5 1 I E _c l os e _t o _P o li cy Li m 1 L o w _ P ol ic y Lim 1 C L _ is _ n ot _ Ri s k_ A v er se C L _ n um _ cl aim s_ t o_ d at e t o 1 > = 4 1 C L _ n um _ yr s_ a s_ c u st < 1 > = 7 1 B i li n g _M e t h od m on th ly d d ir e ct b 1 2 N u m be r _I ns u re d t o 1 > = 4 1 C L _ h a s_m u c h_ e x pe ri e nc e C L _O l d_ V e hi cl e C L _ V al u a bl e_ V e hi cl e 1 I E _c l os e _t o _T L 1 C L _ A C V _t o _M e di an _I n c om e t o. 5 > =. 5 1 I E _c l os e _t o _P o li cy Li m 1 L o w _ P ol ic y Lim 1 C L _ is _ n ot _ Ri s k_ A v er se C L _ n um _ cl aim s_ t o_ d at e t o 1 > = 4 1 C L _ n um _ yr s_ a s_ c u st < 1 > = 7 1 B i li n g _M e t h od m on th ly d d ir e ct b 1 2 N u m be r _I ns u re d t o 1 > = 4 1 C L _ h a s_m u c h_ e x pe ri e nc e C L _ A t a c hme n t_ is _W e ak O nl y_ o n e_ i ns u re d 1 C L _ is _ n ot _ L on g tim e _c u st 1 B i l _Me t h od _i s _ Ri sk ie r 1 C L _w il i ng _ to _ pr o v _f al se _i n f o C L _ P r ofi le _ Ri s k_ is _ Hi g h A c c id e nt _ d es c _u n re li a bl e A c c _ D es c _c a n_ b e _i na c c P o li c e_ R e p or t_ A v ail a bl e 1 N u m _V e h _I nv o lv e d 1 W it n es s es _l ik el y 1 M or e _t h a n_ Tw o _I nv o lv e d 1 M or e _t h a n_o n e_ I nv ol v e d 1 C L _ A t a c hme n t_ is _W e ak O nl y_ o n e_ i ns u re d 1 C L _ is _ n ot _ L on g tim e _c u st 1 B i l _Me t h od _i s _ Ri sk ie r 1 C L _w il i ng _ to _ pr o v _f al se _i n f o C L _ P r ofi le _ Ri s k_ is _ Hi g h A c c id e nt _ d es c _u n re li a bl e A c c _ D es c _c a n_ b e _i na c c P o li c e_ R e p or t_ A v ail a bl e 1 N u m _V e h _I nv o lv e d 1 W it n es s es _l ik el y 1 M or e _t h a n_ Tw o _I nv o lv e d 1 M or e _t h a n_o n e_ I nv ol v e d 1 W it n es s es L a c k_ o f_ Wi tn e s e s C L _ I nd iv _ Ri s k_ is _ Hi g h C L _ A g e t o 2 2 t o t o 3 3 t o t o t o 75 > = 75 1 C L _ is _ at _ Ri s ky _ A ge C L _ is _ Si n gl e 1 C L _ L a ck s _ Dri vi n g _ Ex p 1 C L _ is _ Fi n a nc _ U ns t ab le C L _ R e nt s _ Re si d e nc e 1 C L _ F I CO _ S c or e t o to to to 7 7 to to 8 5 > = 85 1 C L _ L ow _ FI CO _S c or e C L _ h a s_ L ow _I nc om e W it n es s es L a c k_ o f_ Wi tn e s e s C L _ I nd iv _ Ri s k_ is _ Hi g h C L _ A g e t o 2 2 t o t o 3 3 t o t o t o 75 > = 75 1 C L _ is _ at _ Ri s ky _ A ge C L _ is _ Si n gl e 1 C L _ L a ck s _ Dri vi n g _ Ex p 1 C L _ is _ Fi n a nc _ U ns t ab le C L _ R e nt s _ Re si d e nc e 1 C L _ F I CO _ S c or e t o to to to 7 7 to to 8 5 > = 85 1 C L _ L ow _ FI CO _S c or e C L _ h a s_ L ow _I nc om e C L _ V e hi cl e_ R is k _i s_ H i gh C L _ P ol ic y _ Ri sk _i s _ Hi g h A v g _ L ab o r_ R a te _ B o dy t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 A v g _ L ab o r_ R a te _ Fr am e t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 D i f _ La b o r_ R at e _ B od y < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 D i f _ La b o r_ R at e _ Fr am e < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 D i f _ La b o r_ R at e _ Pa in t < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 C L _ cl ai ms _ pe r _y r t o. 5. 5t o 1 1 t o t o 2 1 F r am e_ a s_ F ra c ti o n_ o f_ T ot al t o t o. 1 C L _ V e hi cl e_ R is k _i s_ H i gh C L _ P ol ic y _ Ri sk _i s _ Hi g h A v g _ L ab o r_ R a te _ B o dy t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 A v g _ L ab o r_ R a te _ Fr am e t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 D i f _ La b o r_ R at e _ B od y < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 D i f _ La b o r_ R at e _ Fr am e < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 D i f _ La b o r_ R at e _ Pa in t < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 C L _ cl ai ms _ pe r _y r t o. 5. 5t o 1 1 t o t o 2 1 F r am e_ a s_ F ra c ti o n_ o f_ T ot al t o t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o 1 1 R o a d _ C o nd _ R e po rt e d D I W S 1 D i f ic ul t _ Dri vi n g 1 D i f ic ul t _W ea t he r 1 D i f ic ul t _ Ro a d 1 C o s tl y _ Re p ai rs _ Li ke ly T h e ft 1 B I _ C om p on e nt W e at h er _ Re p o rt e d C F I R S 1 R o a d _ C ha r ac te ri st ic s R C L G S H 1 S p e e d_ L im i t t o t o t o 3 3 t o t o 4 4 t o t o 5 > = 5 1 D i f ic ul t _ Ro a d _ Ch a r. 1t o. 2. 2t o. 3. 3t o. 4. 4t o 1 1 R o a d _ C o nd _ R e po rt e d D I W S 1 D i f ic ul t _ Dri vi n g 1 D i f ic ul t _W ea t he r 1 D i f ic ul t _ Ro a d 1 C o s tl y _ Re p ai rs _ Li ke ly T h e ft 1 B I _ C om p on e nt W e at h er _ Re p o rt e d C F I R S 1 R o a d _ C ha r ac te ri st ic s R C L G S H 1 S p e e d_ L im i t t o t o t o 3 3 t o t o 4 4 t o t o 5 > = 5 1 D i f ic ul t _ Ro a d _ Ch a r 1 D i f ic ul t _ Ro a d _ Co n d 1 H i g h _S p e ed F r am e_ L a b or _I n co n si st _w _ E D L a b o r_ H o ur s _ Fr am e < t o 5 5 t o t o 1 1 t o t o 2 > = 2 1 L a b o r_ H o ur s _ B od y < 5 5 t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 > = 7 1 L a b o r_ C o st _ Fr am e < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 L a b o r_ C o st _ B o dy < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 L a b o r_ R at e _ B od y t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ C o st _ Pa i nt < 1 1 to 2 1 D i f ic ul t _ Ro a d _ Co n d 1 H i g h _S p e ed F r am e_ L a b or _I n co n si st _w _ E D L a b o r_ H o ur s _ Fr am e < t o 5 5 t o t o 1 1 t o t o 2 > = 2 1 L a b o r_ H o ur s _ B od y < 5 5 t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 > = 7 1 L a b o r_ C o st _ Fr am e < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 L a b o r_ C o st _ B o dy < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 L a b o r_ R at e _ B od y t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ C o st _ Pa i nt < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 A v g _ Fr a c_ B P L _ of _ LP _ to t al t o t o t o t o t o 1 1 L a b o r_ C o st _ B an d P < 2 2 to 5 5 to 1 1 t o 2 > = 2 1 F r a c_ B P L _o f _L P _t o ta l < t o. 5. 5t o t o 1 1 L a b o r_ R at i os _ ar e_ t o o_ H ig h T o o _M u c h_ L a bo r D i f er e n ce _wi t h_ H is t_ B P L _ Pa rt s < t o t o t o t o t o t o t o t o t o t o t o t o. 46 > = L a b o r_ R at e s_ H i gh e r_ t ha n _ Av g C l aim _ o ve rw r it te n A P _w il i ng _ to _ c ol u d e A P _ E x pe ri e nc e t o 1 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 A v g _ Fr a c_ B P L _ of _ LP _ to t al t o t o t o t o t o 1 1 L a b o r_ C o st _ B an d P < 2 2 to 5 5 to 1 1 t o 2 > = 2 1 F r a c_ B P L _o f _L P _t o ta l < t o. 5. 5t o t o 1 1 L a b o r_ R at i os _ ar e_ t o o_ H ig h T o o _M u c h_ L a bo r D i f er e n ce _wi t h_ H is t_ B P L _ Pa rt s < t o t o t o t o t o t o t o t o t o t o t o t o. 46 > = L a b o r_ R at e s_ H i gh e r_ t ha n _ Av g C l aim _ o ve rw r it te n A P _w il i ng _ to _ c ol u d e A P _ E x pe ri e nc e t o 1 5 t o 7 7 t o 1 1 A P _ P r ofi le _ Ri s k_ is _ Hi g h A P _ E x pe ri e nc e _i s_ L ow 1 A P _ r el _w _ In s _ C o_ is _W e ak N u m _o f _ AP _i n _ A P_ C om p a ny t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1 1 A P _ C om p a ny _i s _ La r ge _O I A 1 A P _ C o _i s _V e ry _ Sm al _O I A 1 A P _ C om p a ny _i s _ Ve ry _ Sm al 1 A P _ C om p a ny _i s _ La r ge 1 A P _ Y rs _W o rk i ng _w _I n s_ C o t o 1 > = 7 1 A P _ is _ ca r el es s A P _ N um _ A p pr _ d on e _ pe r_ mo n t h t o 1 2 t o 5 5 t o 1 1 t o 2 2 t o 4 4 t o 8 > = 8 5 t o 7 7 t o 1 1 A P _ P r ofi le _ Ri s k_ is _ Hi g h A P _ E x pe ri e nc e _i s_ L ow 1 A P _ r el _w _ In s _ C o_ is _W e ak N u m _o f _ AP _i n _ A P_ C om p a ny t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1 1 A P _ C om p a ny _i s _ La r ge _O I A 1 A P _ C o _i s _V e ry _ Sm al _O I A 1 A P _ C om p a ny _i s _ Ve ry _ Sm al 1 A P _ C om p a ny _i s _ La r ge 1 A P _ Y rs _W o rk i ng _w _I n s_ C o t o 1 > = 7 1 A P _ is _ ca r el es s A P _ N um _ A p pr _ d on e _ pe r_ mo n t h t o 1 2 t o 5 5 t o 1 1 t o 2 2 t o 4 4 t o 8 > = 8 1 A P _ V ol um e _i s_ L ow O d om_ U n re a d ab le S i g ni f_ El e ct r_ D am a ge O d om_ is _ Di g it al V e h ic le _M i le a g e t o 1 1 t o 1 1 t o 2 2 t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1e 5 e 5 1 L a b o r_ H o ur s _ Pa in t < 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 > = 5 1 L a b o r_ H o ur s _ El ec tr < 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 > = 5 1 L a b o r_ R at e _ El ec tr t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ C o st _ El ec tr < 1 1 t o 2 2 t o 5 5 t o 1 1 to 5 5 to 1 1 t o 2 > = A P _ V ol um e _i s_ L ow O d om_ U n re a d ab le S i g ni f_ El e ct r_ D am a ge O d om_ is _ Di g it al V e h ic le _M i le a g e t o 1 1 t o 1 1 t o 2 2 t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1e 5 e 5 1 L a b o r_ H o ur s _ Pa in t < 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 > = 5 1 L a b o r_ H o ur s _ El ec tr < 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 > = 5 1 L a b o r_ R at e _ El ec tr t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ C o st _ El ec tr < 1 1 t o 2 2 t o 5 5 t o 1 1 to 5 5 to 1 1 t o 2 > = 2 1 Mi le a g e_ is _Mi s si n g 1 M i le a g e_m is s _ du e _t o _c a re le s s 1 N u m _ Ap p r_ d o n e_ t ha t_ d a y t o 1 7 t o 8 8 t o 9 9 t o 1 1 t o t o 2 > = 2 1 N u m _ Ap p r_ p er _ D ay _ A v g t o 3 3 t o t o t o t o t o 4 4 t o t o t o t o t o 5 5 t o t o t o t o t o 6 6 t o t o 7 7 t o 1 1 t o 2 > = 2 1 D i f _ N um _ Ap p r_ fr om _ A v g < -3-3 to to to to t o. 5. 5t o 1 1 L o g i n_ N am e_ is _ S ha r ed 1 A P _ T o o _M a n y _ Ap p ra is al s 1 T o o _m a ny _ fo r _On e _ A P_ p er _ D ay 1 Mi le a g e_ is _Mi s si n g 1 M i le a g e_m is s _ du e _t o _c a re le s s 1 N u m _ Ap p r_ d o n e_ t ha t_ d a y t o 1 7 t o 8 8 t o 9 9 t o 1 1 t o t o 2 > = 2 1 N u m _ Ap p r_ p er _ D ay _ A v g t o 3 3 t o t o t o t o t o 4 4 t o t o t o t o t o 5 5 t o t o t o t o t o 6 6 t o t o 7 7 t o 1 1 t o 2 > = 2 1 D i f _ N um _ Ap p r_ fr om _ A v g < -3-3 to to to to t o. 5. 5t o 1 1 L o g i n_ N am e_ is _ S ha r ed 1 A P _ T o o _M a n y _ Ap p ra is al s 1 T o o _m a ny _ fo r _On e _ A P_ p er _ D ay 1 L K Q _ P ar t s_ C o st < 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 A M _ Pa rt s _ Co s t < 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 T o t al _ Pa rt s _ Co s t < 5 5 to 1 1 t o 2 2 t o 4 > = 4 1 D i f _ Fr _ L KQ _ v s_ E x pe c te d < t o to t o t o t o. 1. 1t o. 1 5 > = F r a cti o n _ AM < t o to.1. 1t o to t o to.2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 7. 7t o. 8. 8t o. 9. 9t o 1 1 E x p e ct e d_ L KQ _ Fr _ by _ Yr t o to to to to to to to.2. 2t o to to to to.3. 3t o to to.3 4 > = E x p e ct e d_ AM _ Fr _ b y_ Y r t o to to to to to t o to L K Q _ P ar t s_ C o st < 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 A M _ Pa rt s _ Co s t < 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 T o t al _ Pa rt s _ Co s t < 5 5 to 1 1 t o 2 2 t o 4 > = 4 1 D i f _ Fr _ L KQ _ v s_ E x pe c te d < t o to t o t o t o. 1. 1t o. 1 5 > = F r a cti o n _ AM < t o to.1. 1t o to t o to.2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 7. 7t o. 8. 8t o. 9. 9t o 1 1 E x p e ct e d_ L KQ _ Fr _ by _ Yr t o to to to to to to to.2. 2t o to to to to.3. 3t o to to.3 4 > = E x p e ct e d_ AM _ Fr _ b y_ Y r t o to to to to to t o to to to to to to > = D i f _ Fr _ AM _v s _ Ex p ec t ed < t o to t o t o t o. 1. 1t o. 1 5 > = F r a cti o n _ L KQ <. 1. 1t o. 2. 2t o t o. 3. 3t o t o. 4. 4t o t o. 5. 5t o. 6. 6t o. 7. 7t o. 8. 8t o. 9. 9t o 1 1 V e h ic le _i s _ Ra re L e s s_ L KQ _t h a n_ E x p L e s s_ A M _ t ha n _ Ex p 1 S i g ni f_ P ar ts _ Di f S i g ni f_ P ar ts _ Di f _ ra re C a s e _i s_ IE 1 E x t en t _o f _ Dam a g e_ is _ Hi g h D A P _ U n d er bi d _I E to to to to to > = D i f _ Fr _ AM _v s _ Ex p ec t ed < t o to t o t o t o. 1. 1t o. 1 5 > = F r a cti o n _ L KQ <. 1. 1t o. 2. 2t o t o. 3. 3t o t o. 4. 4t o t o. 5. 5t o. 6. 6t o. 7. 7t o. 8. 8t o. 9. 9t o 1 1 V e h ic le _i s _ Ra re L e s s_ L KQ _t h a n_ E x p L e s s_ A M _ t ha n _ Ex p 1 S i g ni f_ P ar ts _ Di f S i g ni f_ P ar ts _ Di f _ ra re C a s e _i s_ IE 1 E x t en t _o f _ Dam a g e_ is _ Hi g h D A P _ U n d er bi d _I E 1 A P _ S _I E _ Pc t_ D if f_ H is t < -1-1 to to to t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 8. 8t o 1 > = 2 1 A P _ H is t _ Un d er b id _I E 1 A P _ R F _ Pa ir e d_ F re q u e ntl y A P _ R F _ Pa ir in g s _l as t_ Y e ar t o 1 1 t o 5 5 t o 1 1 R F _ Me di a n _I nc om e t o 2 2 t o t o 3 3 t o t o t o 4 4 t o t o 5 5 t o 6 6 t o 7 7 t o 8 > = 8 1 R F _ N h o o d_ R is k _i s_ H i gh R F _ N h o o d_ R is k y_ I nc om e 1 R F _ N h o o d_ H i gh _ C rim e 1 R F _ A v g _M on t h_ V ol _w _ C o_ P 6M t o 1 1 t o 5 5 t o 1 1 t o 2 > = 2 1 R F _ M o n t hl y_ V ol _ Al l_ C ar ri er s < t o 5 1 A P _ S _I E _ Pc t_ D if f_ H is t < -1-1 to to to t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 8. 8t o 1 > = 2 1 A P _ H is t _ Un d er b id _I E 1 A P _ R F _ Pa ir e d_ F re q u e ntl y A P _ R F _ Pa ir in g s _l as t_ Y e ar t o 1 1 t o 5 5 t o 1 1 R F _ Me di a n _I nc om e t o 2 2 t o t o 3 3 t o t o t o 4 4 t o t o 5 5 t o 6 6 t o 7 7 t o 8 > = 8 1 R F _ N h o o d_ R is k _i s_ H i gh R F _ N h o o d_ R is k y_ I nc om e 1 R F _ N h o o d_ H i gh _ C rim e 1 R F _ A v g _M on t h_ V ol _w _ C o_ P 6M t o 1 1 t o 5 5 t o 1 1 t o 2 > = 2 1 R F _ M o n t hl y_ V ol _ Al l_ C ar ri er s < t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1 1 to 2 > = 2 1 R F _ a n d_ C o _ R el ati o n _W ea k R F _ C o _ Fr _i s _L ow R F _ Mo n t hl y_ V ol _i s _L ow R F _ F r_ V ol _w _ C o < t o t o. 1. 1t o. 2 > =. 2 1 R F _ I nd iv _ Ri s k_ is _ Hi g h R F _ P r ofi le _ Ri s k_ is _ Hi g h R F _w il i ng _ to _ c ol u d e R F _w il i ng _ to _ n ot _f o l ow _ a p p A P _ R F _ R el ati o n _i s_ S tr o n g 1 A P _ g iv e s_ t o _m a ny _ c on c A P _ g ui d e d_ b y _ RF R e p ai r _i s_ C om pl e x t o 1 1 t o 2 2 t o 5 5 t o 1 1 to 2 > = 2 1 R F _ a n d_ C o _ R el ati o n _W ea k R F _ C o _ Fr _i s _L ow R F _ Mo n t hl y_ V ol _i s _L ow R F _ F r_ V ol _w _ C o < t o t o. 1. 1t o. 2 > =. 2 1 R F _ I nd iv _ Ri s k_ is _ Hi g h R F _ P r ofi le _ Ri s k_ is _ Hi g h R F _w il i ng _ to _ c ol u d e R F _w il i ng _ to _ n ot _f o l ow _ a p p A P _ R F _ R el ati o n _i s_ S tr o n g 1 A P _ g iv e s_ t o _m a ny _ c on c A P _ g ui d e d_ b y _ RF R e p ai r _i s_ C om pl e x M ak em o d el _F r eq u e nc y t o to to to t o t o t o. 4 > =. 4 1 P c t _ Dif f _S _ to _I E _ Vs _ Hi s t < to to t o t o. 1. 1t o. 2. 2t o. 5. 5t o 1 1 A P _ T o d ay _ U n de r bi d_ I E 1 S _ I E_ D if f_ Si g ni f _g iv e n _ Co mp le x 1 A P _ A v oi d s_ 2 n d_ V is it 1 V a l ue _I n c on si s te n t_w _ D ama g e R e p ai r _V al u e _i s_ H ig h 1 I E _t o _ A C V_ R at i o <. 5. 5t o. 8 > =. 8 1 A P _ S u p _t o _I E_ P ct _ Di f < -1-1 to to to t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 8. 8t o 1 > = 2 1 A P _ T e n ds _t o _W ri te _ Hi g h _I E 1 M ak em o d el _F r eq u e nc y t o to to to t o t o t o. 4 > =. 4 1 P c t _ Dif f _S _ to _I E _ Vs _ Hi s t < to to t o t o. 1. 1t o. 2. 2t o. 5. 5t o 1 1 A P _ T o d ay _ U n de r bi d_ I E 1 S _ I E_ D if f_ Si g ni f _g iv e n _ Co mp le x 1 A P _ A v oi d s_ 2 n d_ V is it 1 V a l ue _I n c on si s te n t_w _ D ama g e R e p ai r _V al u e _i s_ H ig h 1 I E _t o _ A C V_ R at i o <. 5. 5t o. 8 > =. 8 1 A P _ S u p _t o _I E_ P ct _ Di f < -1-1 to to to t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 8. 8t o 1 > = 2 1 A P _ T e n ds _t o _W ri te _ Hi g h _I E 1 A P _ f ail e d_ t o_ s av e A v g _ Di f _ L ab o r_ R a te < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 C L _w il i ng _ to _ c ol u d e A P _ u nl ik el y _t o _ ha v e_ E x p_w _ Ve h R F _ C rim e _ Ra t e t o 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 C L _ N h o o d_ R is k y_ I nc om e 1 C L _ N h o o d_ H i gh _ C rim e 1 C L _ N h o o d_ Mo s tl y_ U r ba n 1 C L _ N h o o d_ R is k _i s_ H i gh C L _ Me di a n _I nc om e t o 2 2 t o t o 3 3 t o t o t o 4 4 t o t o 5 5 t o 6 6 t o 7 7 t o 8 > = 8 1 C L _ C rim e _ Ra t e t o 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 A P _ f ail e d_ t o_ s av e A v g _ Di f _ L ab o r_ R a te < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 C L _w il i ng _ to _ c ol u d e A P _ u nl ik el y _t o _ ha v e_ E x p_w _ Ve h R F _ C rim e _ Ra t e t o 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 C L _ N h o o d_ R is k y_ I nc om e 1 C L _ N h o o d_ H i gh _ C rim e 1 C L _ N h o o d_ Mo s tl y_ U r ba n 1 C L _ N h o o d_ R is k _i s_ H i gh C L _ Me di a n _I nc om e t o 2 2 t o t o 3 3 t o t o t o 4 4 t o t o 5 5 t o 6 6 t o 7 7 t o 8 > = 8 1 C L _ C rim e _ Ra t e t o 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 C L _ F r_ B el ow _ P ov er t y t o t o. 1. 1t o t o. 2. 2t o. 4 > =. 4 1 C L _ P ct _ U rb a n t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 7 t o 8 8 t o 9 > = 9 1 R F _ F r_ B el ow _ P ov er t y t o t o. 1. 1t o t o. 2. 2t o. 4 > =. 4 1 R F _ N h o o d_ Mo s tl y_ U r ba n 1 R F _ P ct _ U rb a n t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 7 t o 8 8 t o 9 > = 9 1 C L _ R is k y_ P L ev el R F _ R is k y_ P L ev el R F _ h a s_ L ow _V o lu me R F _ is _ Sm al R F _ is _ D A P 1 C L _ F r_ B el ow _ P ov er t y t o t o. 1. 1t o t o. 2. 2t o. 4 > =. 4 1 C L _ P ct _ U rb a n t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 7 t o 8 8 t o 9 > = 9 1 R F _ F r_ B el ow _ P ov er t y t o t o. 1. 1t o t o. 2. 2t o. 4 > =. 4 1 R F _ N h o o d_ Mo s tl y_ U r ba n 1 R F _ P ct _ U rb a n t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 7 t o 8 8 t o 9 > = 9 1 C L _ R is k y_ P L ev el R F _ R is k y_ P L ev el R F _ h a s_ L ow _V o lu me R F _ is _ Sm al R F _ is _ D A P 1 R F _ N o t_ P ar t _o f _ De al er s hi p 1 R F _ N o t_ P ar t _o f _ Ne tw or k 1 R F _ N um _ Em pl o ye e s t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o R F _ L a b or _ Ra t es _ L ow e r R F _ h a s_ F ew _ Em p lo y e s I E _t o _ Po li cy L im _ R a ti o t o. 8 > =. 8 1 C L _ V e hi cl e_ Y rs _Ol d < 1 7 t o 8 8 t o 9 9 t o 1 1 t o t o t o t o t o t o D e d u ct i bl e t o 1 1 to to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 C o l is i on _ P oli c y Lim t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1e 5 e 5 1 C L _ V e hi cl e_ A C V t o 1 1 t o 2 R F _ N o t_ P ar t _o f _ De al er s hi p 1 R F _ N o t_ P ar t _o f _ Ne tw or k 1 R F _ N um _ Em pl o ye e s t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o R F _ L a b or _ Ra t es _ L ow e r R F _ h a s_ F ew _ Em p lo y e s I E _t o _ Po li cy L im _ R a ti o t o. 8 > =. 8 1 C L _ V e hi cl e_ Y rs _Ol d < 1 7 t o 8 8 t o 9 9 t o 1 1 t o t o t o t o t o t o D e d u ct i bl e t o 1 1 to to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 C o l is i on _ P oli c y Lim t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1e 5 e 5 1 C L _ V e hi cl e_ A C V t o 1 1 t o 2 2 t o 4 4 t o 5 5 t o 8 8 t o 1 1 t o t o t o t o 2 2 t o t o 3 3 t o 4 4 t o 6 > = 6 1 L o s s _Y e ar < 2 2 1t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o 2 2 > = V e h ic le _ Ye a r < t o t o t o t o t o t o t o t o t o t o t o t o 2 2 t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o 2 2 > = C a p t ur e d_ i n_ P h ys R C a p t ur e d_ i n_ P h ys R _ T S C a p t ur e d_ i n_ D e sk R 2 t o 4 4 t o 5 5 t o 8 8 t o 1 1 t o t o t o t o 2 2 t o t o 3 3 t o 4 4 t o 6 > = 6 1 L o s s _Y e ar < 2 2 1t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o 2 2 > = V e h ic le _ Ye a r < t o t o t o t o t o t o t o t o t o t o t o t o 2 2 t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o 2 2 > = C a p t ur e d_ i n_ P h ys R C a p t ur e d_ i n_ P h ys R _ T S C a p t ur e d_ i n_ D e sk R P h y s R _mi s e s_ S a vi n gs P h y s R _T S _mi s se s _ Sa vi n g s M at c he d _i n _ Ph y s R M at c he d _i n _ De s k R M at c h_ S c or e _P h y s R_ T S t o M at c h_ S c or e _ De s k R t o M at c h_ S c or e _P h y s R t o C L _ n o t_ at _ F au lt 1 C L _ is _ n ot _i n s ur e d 1 M aj or _ D am ag e _ Li ke ly 1 J o b _m u ch _ hi g h er _t h a n_ A C V 1 J o b _t o _ A C V_ R a ti o < 1 1 t o t o t o t o P h y s R _mi s e s_ S a vi n gs P h y s R _T S _mi s se s _ Sa vi n g s M at c he d _i n _ Ph y s R M at c he d _i n _ De s k R M at c h_ S c or e _P h y s R_ T S t o M at c h_ S c or e _ De s k R t o M at c h_ S c or e _P h y s R t o C L _ n o t_ at _ F au lt 1 C L _ is _ n ot _i n s ur e d 1 M aj or _ D am ag e _ Li ke ly 1 J o b _m u ch _ hi g h er _t h a n_ A C V 1 J o b _t o _ A C V_ R a ti o < 1 1 t o t o t o t o M at c he d _i n _ Ph y s R_ T S T o t al _ La b or _ H o ur s < 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 O ut p ut _ Pr o b a bil it y T L _O v er ri de 1 A P _ mi s e d_ s av i ng s A P _ m i s e d_ L a b or _ Sa vi n g s A P _ mi s e d_ p ar t s_ s av A P _ T y pe I A S A D A P 1 P r o ce s s_ S u p _I D t o 1 > = 2 1 V e h _i s _T o ta l_ L o s 1 J o b _ Hi g h er _t h a n_ A C V _ no t _T L 1 I E _J o b _ To t al _ Amt < t o 5 5 to to 1 1 t o 2 2 t o 3 3 t o 5 5 t o 7 5 > = 75 1 M at c he d _i n _ Ph y s R_ T S T o t al _ La b or _ H o ur s < 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 O ut p ut _ Pr o b a bil it y T L _O v er ri de 1 A P _ mi s e d_ s av i ng s A P _ m i s e d_ L a b or _ Sa vi n g s A P _ mi s e d_ p ar t s_ s av A P _ T y pe I A S A D A P 1 P r o ce s s_ S u p _I D t o 1 > = 2 1 V e h _i s _T o ta l_ L o s 1 J o b _ Hi g h er _t h a n_ A C V _ no t _T L 1 I E _J o b _ To t al _ Amt < t o 5 5 to to 1 1 t o 2 2 t o 3 3 t o 5 5 t o 7 5 > = 75 1 J o b _ T ot al _ Am t < t o 5 5 to to 1 1 t o 2 2 t o 3 3 t o 5 5 t o 7 5 > = 75 1 I n s ur e d_w a s_ R e ck le s s 1 C i ta ti o n _i s_ f or _I n su r ed 1 C i ta ti o n _i n di c_ re c kl es s 1 W h o _P o li ce _ R ep o rt _ Vi ol I V P B O U E N 1 A v g _ L ab o r_ R a te _ Pa i nt t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ R at e _ Fr am e t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 R F _ d id n t_ f ol ow _ ap p ra is al C L _ A P _ co l u de d J o b _ T ot al _ Am t < t o 5 5 to to 1 1 t o 2 2 t o 3 3 t o 5 5 t o 7 5 > = 75 1 I n s ur e d_w a s_ R e ck le s s 1 C i ta ti o n _i s_ f or _I n su r ed 1 C i ta ti o n _i n di c_ re c kl es s 1 W h o _P o li ce _ R ep o rt _ Vi ol I V P B O U E N 1 A v g _ L ab o r_ R a te _ Pa i nt t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ R at e _ Fr am e t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 R F _ d id n t_ f ol ow _ ap p ra is al C L _ A P _ co l u de d A P _ R F _ co l u de d C L _ mi sr e po rt e d _a c ci de n t L a b o r_ R at e _ Pa in t 2 t o t o 3 4 t o t o 5 6 t o t o 7 1 P a rt s _ Th r es h ol d < 1 4 to 5 > = 5 1 T o t al _ Pa rt s _ Hi g he r_ t ha n _ T hr es 1 T o t al _ Pa rt s _T h re s _ Di f High-Level auto repair model diagram Main output variable: Probability of overwriting 8+ data inputs, combining hard data in non-linear cause-effect relationships elicited by experts

24 PA Knowledge Limited C L _ V e hi cl e_ h a s_ P ri or _ Dm g C L _O l d_ V e hi cl e C L _ V al u a bl e_ V e hi cl e 1 I E _c l os e _t o _T L 1 C L _ A C V _t o _M e di an _I n c om e t o. 5 > =. 5 1 I E _c l os e _t o _P o li cy Li m 1 L o w _ P ol ic y Lim 1 C L _ is _ n ot _ Ri s k_ A v er se C L _ n um _ cl aim s_ t o_ d at e t o 1 > = 4 1 C L _ n um _ yr s_ a s_ c u st < 1 > = 7 1 B i li n g _M e t h od m on th ly d d ir e ct b 1 2 N u m be r _I ns u re d t o 1 > = 4 1 C L _ h a s_m u c h_ e x pe ri e nc e C L _ A t a c hme n t_ is _W e ak O nl y_ o n e_ i ns u re d 1 C L _ is _ n ot _ L on g tim e _c u st 1 B i l _Me t h od _i s _ Ri sk ie r 1 C L _w il i ng _ to _ pr o v _f al se _i n f o C L _ P r ofi le _ Ri s k_ is _ Hi g h A c c id e nt _ d es c _u n re li a bl e A c c _ D es c _c a n_ b e _i na c c P o li c e_ R e p or t_ A v ail a bl e 1 N u m _V e h _I nv o lv e d 1 W it n es s es _l ik el y 1 M or e _t h a n_ Tw o _I nv o lv e d 1 M or e _t h a n_o n e_ I nv ol v e d 1 W it n es s es L a c k_ o f_ Wi tn e s e s C L _ I nd iv _ Ri s k_ is _ Hi g h C L _ A g e t o 2 2 t o t o 3 3 t o t o t o 75 > = 75 1 C L _ is _ at _ Ri s ky _ A ge C L _ is _ Si n gl e 1 C L _ L a ck s _ Dri vi n g _ Ex p 1 C L _ is _ Fi n a nc _ U ns t ab le C L _ R e nt s _ Re si d e nc e 1 C L _ F I CO _ S c or e t o to to to 7 7 to to 8 5 > = 85 1 C L _ L ow _ FI CO _S c or e C L _ h a s_ L ow _I nc om e C L _ V e hi cl e_ R is k _i s_ H i gh C L _ P ol ic y _ Ri sk _i s _ Hi g h A v g _ L ab o r_ R a te _ B o dy t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 A v g _ L ab o r_ R a te _ Fr am e t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 D i f _ La b o r_ R at e _ B od y < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 D i f _ La b o r_ R at e _ Fr am e < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 D i f _ La b o r_ R at e _ Pa in t < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 C L _ cl ai ms _ pe r _y r t o. 5. 5t o 1 1 t o t o 2 1 F r am e_ a s_ F ra c ti o n_ o f_ T ot al t o t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o 1 1 R o a d _ C o nd _ R e po rt e d D I W S 1 D i f ic ul t _ Dri vi n g 1 D i f ic ul t _W ea t he r 1 D i f ic ul t _ Ro a d 1 C o s tl y _ Re p ai rs _ Li ke ly T h e ft 1 B I _ C om p on e nt W e at h er _ Re p o rt e d C F I R S 1 R o a d _ C ha r ac te ri st ic s R C L G S H 1 S p e e d_ L im i t t o t o t o 3 3 t o t o 4 4 t o t o 5 > = 5 1 D i f ic ul t _ Ro a d _ Ch a r 1 D i f ic ul t _ Ro a d _ Co n d 1 H i g h _S p e ed F r am e_ L a b or _I n co n si st _w _ E D L a b o r_ H o ur s _ Fr am e < t o 5 5 t o t o 1 1 t o t o 2 > = 2 1 L a b o r_ H o ur s _ B od y < 5 5 t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 > = 7 1 L a b o r_ C o st _ Fr am e < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 L a b o r_ C o st _ B o dy < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 L a b o r_ R at e _ B od y t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ C o st _ Pa i nt < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 A v g _ Fr a c_ B P L _ of _ LP _ to t al t o t o t o t o t o 1 1 L a b o r_ C o st _ B an d P < 2 2 to 5 5 to 1 1 t o 2 > = 2 1 F r a c_ B P L _o f _L P _t o ta l < t o. 5. 5t o t o 1 1 L a b o r_ R at i os _ ar e_ t o o_ H ig h T o o _M u c h_ L a bo r D i f er e n ce _wi t h_ H is t_ B P L _ Pa rt s < t o t o t o t o t o t o t o t o t o t o t o t o. 46 > = L a b o r_ R at e s_ H i gh e r_ t ha n _ Av g C l aim _ o ve rw r it te n A P _w il i ng _ to _ c ol u d e A P _ E x pe ri e nc e t o 1 5 t o 7 7 t o 1 1 A P _ P r ofi le _ Ri s k_ is _ Hi g h A P _ E x pe ri e nc e _i s_ L ow 1 A P _ r el _w _ In s _ C o_ is _W e ak N u m _o f _ AP _i n _ A P_ C om p a ny t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1 1 A P _ C om p a ny _i s _ La r ge _O I A 1 A P _ C o _i s _V e ry _ Sm al _O I A 1 A P _ C om p a ny _i s _ Ve ry _ Sm al 1 A P _ C om p a ny _i s _ La r ge 1 A P _ Y rs _W o rk i ng _w _I n s_ C o t o 1 > = 7 1 A P _ is _ ca r el es s A P _ N um _ A p pr _ d on e _ pe r_ mo n t h t o 1 2 t o 5 5 t o 1 1 t o 2 2 t o 4 4 t o 8 > = 8 1 A P _ V ol um e _i s_ L ow O d om_ U n re a d ab le S i g ni f_ El e ct r_ D am a ge O d om_ is _ Di g it al V e h ic le _M i le a g e t o 1 1 t o 1 1 t o 2 2 t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1e 5 e 5 1 L a b o r_ H o ur s _ Pa in t < 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 > = 5 1 L a b o r_ H o ur s _ El ec tr < 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 > = 5 1 L a b o r_ R at e _ El ec tr t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ C o st _ El ec tr < 1 1 t o 2 2 t o 5 5 t o 1 1 to 5 5 to 1 1 t o 2 > = 2 1 Mi le a g e_ is _Mi s si n g 1 M i le a g e_m is s _ du e _t o _c a re le s s 1 N u m _ Ap p r_ d o n e_ t ha t_ d a y t o 1 7 t o 8 8 t o 9 9 t o 1 1 t o t o 2 > = 2 1 N u m _ Ap p r_ p er _ D ay _ A v g t o 3 3 t o t o t o t o t o 4 4 t o t o t o t o t o 5 5 t o t o t o t o t o 6 6 t o t o 7 7 t o 1 1 t o 2 > = 2 1 D i f _ N um _ Ap p r_ fr om _ A v g < -3-3 to to to to t o. 5. 5t o 1 1 L o g i n_ N am e_ is _ S ha r ed 1 A P _ T o o _M a n y _ Ap p ra is al s 1 T o o _m a ny _ fo r _On e _ A P_ p er _ D ay 1 L K Q _ P ar t s_ C o st < 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 A M _ Pa rt s _ Co s t < 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 T o t al _ Pa rt s _ Co s t < 5 5 to 1 1 t o 2 2 t o 4 > = 4 1 D i f _ Fr _ L KQ _ v s_ E x pe c te d < t o to t o t o t o. 1. 1t o. 1 5 > = F r a cti o n _ AM < t o to.1. 1t o to t o to.2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 7. 7t o. 8. 8t o. 9. 9t o 1 1 E x p e ct e d_ L KQ _ Fr _ by _ Yr t o to to to to to to to.2. 2t o to to to to.3. 3t o to to.3 4 > = E x p e ct e d_ AM _ Fr _ b y_ Y r t o to to to to to t o to to to to to to > = D i f _ Fr _ AM _v s _ Ex p ec t ed < t o to t o t o t o. 1. 1t o. 1 5 > = F r a cti o n _ L KQ <. 1. 1t o. 2. 2t o t o. 3. 3t o t o. 4. 4t o t o. 5. 5t o. 6. 6t o. 7. 7t o. 8. 8t o. 9. 9t o 1 1 V e h ic le _i s _ Ra re L e s s_ L KQ _t h a n_ E x p L e s s_ A M _ t ha n _ Ex p 1 S i g ni f_ P ar ts _ Di f S i g ni f_ P ar ts _ Di f _ ra re C a s e _i s_ IE 1 E x t en t _o f _ Dam a g e_ is _ Hi g h D A P _ U n d er bi d _I E 1 A P _ S _I E _ Pc t_ D if f_ H is t < -1-1 to to to t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 8. 8t o 1 > = 2 1 A P _ H is t _ Un d er b id _I E 1 A P _ R F _ Pa ir e d_ F re q u e ntl y A P _ R F _ Pa ir in g s _l as t_ Y e ar t o 1 1 t o 5 5 t o 1 1 R F _ Me di a n _I nc om e t o 2 2 t o t o 3 3 t o t o t o 4 4 t o t o 5 5 t o 6 6 t o 7 7 t o 8 > = 8 1 R F _ N h o o d_ R is k _i s_ H i gh R F _ N h o o d_ R is k y_ I nc om e 1 R F _ N h o o d_ H i gh _ C rim e 1 R F _ A v g _M on t h_ V ol _w _ C o_ P 6M t o 1 1 t o 5 5 t o 1 1 t o 2 > = 2 1 R F _ M o n t hl y_ V ol _ Al l_ C ar ri er s < t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1 1 to 2 > = 2 1 R F _ a n d_ C o _ R el ati o n _W ea k R F _ C o _ Fr _i s _L ow R F _ Mo n t hl y_ V ol _i s _L ow R F _ F r_ V ol _w _ C o < t o t o. 1. 1t o. 2 > =. 2 1 R F _ I nd iv _ Ri s k_ is _ Hi g h R F _ P r ofi le _ Ri s k_ is _ Hi g h R F _w il i ng _ to _ c ol u d e R F _w il i ng _ to _ n ot _f o l ow _ a p p A P _ R F _ R el ati o n _i s_ S tr o n g 1 A P _ g iv e s_ t o _m a ny _ c on c A P _ g ui d e d_ b y _ RF R e p ai r _i s_ C om pl e x M ak em o d el _F r eq u e nc y t o to to to t o t o t o. 4 > =. 4 1 P c t _ Dif f _S _ to _I E _ Vs _ Hi s t < to to t o t o. 1. 1t o. 2. 2t o. 5. 5t o 1 1 A P _ T o d ay _ U n de r bi d_ I E 1 S _ I E_ D if f_ Si g ni f _g iv e n _ Co mp le x 1 A P _ A v oi d s_ 2 n d_ V is it 1 V a l ue _I n c on si s te n t_w _ D ama g e R e p ai r _V al u e _i s_ H ig h 1 I E _t o _ A C V_ R at i o <. 5. 5t o. 8 > =. 8 1 A P _ S u p _t o _I E_ P ct _ Di f < -1-1 to to to t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 8. 8t o 1 > = 2 1 A P _ T e n ds _t o _W ri te _ Hi g h _I E 1 A P _ f ail e d_ t o_ s av e A v g _ Di f _ L ab o r_ R a te < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 C L _w il i ng _ to _ c ol u d e A P _ u nl ik el y _t o _ ha v e_ E x p_w _ Ve h R F _ C rim e _ Ra t e t o 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 C L _ N h o o d_ R is k y_ I nc om e 1 C L _ N h o o d_ H i gh _ C rim e 1 C L _ N h o o d_ Mo s tl y_ U r ba n 1 C L _ N h o o d_ R is k _i s_ H i gh C L _ Me di a n _I nc om e t o 2 2 t o t o 3 3 t o t o t o 4 4 t o t o 5 5 t o 6 6 t o 7 7 t o 8 > = 8 1 C L _ C rim e _ Ra t e t o 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 C L _ F r_ B el ow _ P ov er t y t o t o. 1. 1t o t o. 2. 2t o. 4 > =. 4 1 C L _ P ct _ U rb a n t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 7 t o 8 8 t o 9 > = 9 1 R F _ F r_ B el ow _ P ov er t y t o t o. 1. 1t o t o. 2. 2t o. 4 > =. 4 1 R F _ N h o o d_ Mo s tl y_ U r ba n 1 R F _ P ct _ U rb a n t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 7 t o 8 8 t o 9 > = 9 1 C L _ R is k y_ P L ev el R F _ R is k y_ P L ev el R F _ h a s_ L ow _V o lu me R F _ is _ Sm al R F _ is _ D A P 1 R F _ N o t_ P ar t _o f _ De al er s hi p 1 R F _ N o t_ P ar t _o f _ Ne tw or k 1 R F _ N um _ Em pl o ye e s t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o R F _ L a b or _ Ra t es _ L ow e r R F _ h a s_ F ew _ Em p lo y e s I E _t o _ Po li cy L im _ R a ti o t o. 8 > =. 8 1 C L _ V e hi cl e_ Y rs _Ol d < 1 7 t o 8 8 t o 9 9 t o 1 1 t o t o t o t o t o t o D e d u ct i bl e t o 1 1 to to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 C o l is i on _ P oli c y Lim t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1e 5 e 5 1 C L _ V e hi cl e_ A C V t o 1 1 t o 2 2 t o 4 4 t o 5 5 t o 8 8 t o 1 1 t o t o t o t o 2 2 t o t o 3 3 t o 4 4 t o 6 > = 6 1 L o s s _Y e ar < 2 2 1t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o 2 2 > = V e h ic le _ Ye a r < t o t o t o t o t o t o t o t o t o t o t o t o 2 2 t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o 2 2 > = C a p t ur e d_ i n_ P h ys R C a p t ur e d_ i n_ P h ys R _ T S C a p t ur e d_ i n_ D e sk R P h y s R _mi s e s_ S a vi n gs P h y s R _T S _mi s se s _ Sa vi n g s M at c he d _i n _ Ph y s R M at c he d _i n _ De s k R M at c h_ S c or e _P h y s R_ T S t o M at c h_ S c or e _ De s k R t o M at c h_ S c or e _P h y s R t o C L _ n o t_ at _ F au lt 1 C L _ is _ n ot _i n s ur e d 1 M aj or _ D am ag e _ Li ke ly 1 J o b _m u ch _ hi g h er _t h a n_ A C V 1 J o b _t o _ A C V_ R a ti o < 1 1 t o t o t o t o M at c he d _i n _ Ph y s R_ T S T o t al _ La b or _ H o ur s < 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 O ut p ut _ Pr o b a bil it y T L _O v er ri de 1 A P _ mi s e d_ s av i ng s A P _ m i s e d_ L a b or _ Sa vi n g s A P _ mi s e d_ p ar t s_ s av A P _ T y pe I A S A D A P 1 P r o ce s s_ S u p _I D t o 1 > = 2 1 V e h _i s _T o ta l_ L o s 1 J o b _ Hi g h er _t h a n_ A C V _ no t _T L 1 I E _J o b _ To t al _ Amt < t o 5 5 to to 1 1 t o 2 2 t o 3 3 t o 5 5 t o 7 5 > = 75 1 J o b _ T ot al _ Am t < t o 5 5 to to 1 1 t o 2 2 t o 3 3 t o 5 5 t o 7 5 > = 75 1 I n s ur e d_w a s_ R e ck le s s 1 C i ta ti o n _i s_ f or _I n su r ed 1 C i ta ti o n _i n di c_ re c kl es s 1 W h o _P o li ce _ R ep o rt _ Vi ol I V P B O U E N 1 A v g _ L ab o r_ R a te _ Pa i nt t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ R at e _ Fr am e t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 R F _ d id n t_ f ol ow _ ap p ra is al C L _ A P _ co l u de d A P _ R F _ co l u de d C L _ mi sr e po rt e d _a c ci de n t L a b o r_ R at e _ Pa in t 2 t o t o 3 4 t o t o 5 6 t o t o 7 1 P a rt s _ Th r es h ol d < 1 4 to 5 > = 5 1 T o t al _ Pa rt s _ Hi g he r_ t ha n _ T hr es 1 T o t al _ Pa rt s _T h re s _ Di f C L _ V e hi cl e_ h a s_ P ri or _ Dm g C L _ V e hi cl e_ h a s_ P ri or _ Dm g C L _O l d_ V e hi cl e C L _ V al u a bl e_ V e hi cl e 1 I E _c l os e _t o _T L 1 C L _ A C V _t o _M e di an _I n c om e t o. 5 > =. 5 1 I E _c l os e _t o _P o li cy Li m 1 L o w _ P ol ic y Lim 1 C L _ is _ n ot _ Ri s k_ A v er se C L _ n um _ cl aim s_ t o_ d at e t o 1 > = 4 1 C L _ n um _ yr s_ a s_ c u st < 1 > = 7 1 B i li n g _M e t h od m on th ly d d ir e ct b 1 2 N u m be r _I ns u re d t o 1 > = 4 1 C L _ h a s_m u c h_ e x pe ri e nc e C L _O l d_ V e hi cl e C L _ V al u a bl e_ V e hi cl e 1 I E _c l os e _t o _T L 1 C L _ A C V _t o _M e di an _I n c om e t o. 5 > =. 5 1 I E _c l os e _t o _P o li cy Li m 1 L o w _ P ol ic y Lim 1 C L _ is _ n ot _ Ri s k_ A v er se C L _ n um _ cl aim s_ t o_ d at e t o 1 > = 4 1 C L _ n um _ yr s_ a s_ c u st < 1 > = 7 1 B i li n g _M e t h od m on th ly d d ir e ct b 1 2 N u m be r _I ns u re d t o 1 > = 4 1 C L _ h a s_m u c h_ e x pe ri e nc e C L _ A t a c hme n t_ is _W e ak O nl y_ o n e_ i ns u re d 1 C L _ is _ n ot _ L on g tim e _c u st 1 B i l _Me t h od _i s _ Ri sk ie r 1 C L _w il i ng _ to _ pr o v _f al se _i n f o C L _ P r ofi le _ Ri s k_ is _ Hi g h A c c id e nt _ d es c _u n re li a bl e A c c _ D es c _c a n_ b e _i na c c P o li c e_ R e p or t_ A v ail a bl e 1 N u m _V e h _I nv o lv e d 1 W it n es s es _l ik el y 1 M or e _t h a n_ Tw o _I nv o lv e d 1 M or e _t h a n_o n e_ I nv ol v e d 1 C L _ A t a c hme n t_ is _W e ak O nl y_ o n e_ i ns u re d 1 C L _ is _ n ot _ L on g tim e _c u st 1 B i l _Me t h od _i s _ Ri sk ie r 1 C L _w il i ng _ to _ pr o v _f al se _i n f o C L _ P r ofi le _ Ri s k_ is _ Hi g h A c c id e nt _ d es c _u n re li a bl e A c c _ D es c _c a n_ b e _i na c c P o li c e_ R e p or t_ A v ail a bl e 1 N u m _V e h _I nv o lv e d 1 W it n es s es _l ik el y 1 M or e _t h a n_ Tw o _I nv o lv e d 1 M or e _t h a n_o n e_ I nv ol v e d 1 W it n es s es L a c k_ o f_ Wi tn e s e s C L _ I nd iv _ Ri s k_ is _ Hi g h C L _ A g e t o 2 2 t o t o 3 3 t o t o t o 75 > = 75 1 C L _ is _ at _ Ri s ky _ A ge C L _ is _ Si n gl e 1 C L _ L a ck s _ Dri vi n g _ Ex p 1 C L _ is _ Fi n a nc _ U ns t ab le C L _ R e nt s _ Re si d e nc e 1 C L _ F I CO _ S c or e t o to to to 7 7 to to 8 5 > = 85 1 C L _ L ow _ FI CO _S c or e C L _ h a s_ L ow _I nc om e W it n es s es L a c k_ o f_ Wi tn e s e s C L _ I nd iv _ Ri s k_ is _ Hi g h C L _ A g e t o 2 2 t o t o 3 3 t o t o t o 75 > = 75 1 C L _ is _ at _ Ri s ky _ A ge C L _ is _ Si n gl e 1 C L _ L a ck s _ Dri vi n g _ Ex p 1 C L _ is _ Fi n a nc _ U ns t ab le C L _ R e nt s _ Re si d e nc e 1 C L _ F I CO _ S c or e t o to to to 7 7 to to 8 5 > = 85 1 C L _ L ow _ FI CO _S c or e C L _ h a s_ L ow _I nc om e C L _ V e hi cl e_ R is k _i s_ H i gh C L _ P ol ic y _ Ri sk _i s _ Hi g h A v g _ L ab o r_ R a te _ B o dy t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 A v g _ L ab o r_ R a te _ Fr am e t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 D i f _ La b o r_ R at e _ B od y < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 D i f _ La b o r_ R at e _ Fr am e < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 D i f _ La b o r_ R at e _ Pa in t < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 C L _ cl ai ms _ pe r _y r t o. 5. 5t o 1 1 t o t o 2 1 F r am e_ a s_ F ra c ti o n_ o f_ T ot al t o t o. 1 C L _ V e hi cl e_ R is k _i s_ H i gh C L _ P ol ic y _ Ri sk _i s _ Hi g h A v g _ L ab o r_ R a te _ B o dy t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 A v g _ L ab o r_ R a te _ Fr am e t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 D i f _ La b o r_ R at e _ B od y < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 D i f _ La b o r_ R at e _ Fr am e < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 D i f _ La b o r_ R at e _ Pa in t < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 C L _ cl ai ms _ pe r _y r t o. 5. 5t o 1 1 t o t o 2 1 F r am e_ a s_ F ra c ti o n_ o f_ T ot al t o t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o 1 1 R o a d _ C o nd _ R e po rt e d D I W S 1 D i f ic ul t _ Dri vi n g 1 D i f ic ul t _W ea t he r 1 D i f ic ul t _ Ro a d 1 C o s tl y _ Re p ai rs _ Li ke ly T h e ft 1 B I _ C om p on e nt W e at h er _ Re p o rt e d C F I R S 1 R o a d _ C ha r ac te ri st ic s R C L G S H 1 S p e e d_ L im i t t o t o t o 3 3 t o t o 4 4 t o t o 5 > = 5 1 D i f ic ul t _ Ro a d _ Ch a r. 1t o. 2. 2t o. 3. 3t o. 4. 4t o 1 1 R o a d _ C o nd _ R e po rt e d D I W S 1 D i f ic ul t _ Dri vi n g 1 D i f ic ul t _W ea t he r 1 D i f ic ul t _ Ro a d 1 C o s tl y _ Re p ai rs _ Li ke ly T h e ft 1 B I _ C om p on e nt W e at h er _ Re p o rt e d C F I R S 1 R o a d _ C ha r ac te ri st ic s R C L G S H 1 S p e e d_ L im i t t o t o t o 3 3 t o t o 4 4 t o t o 5 > = 5 1 D i f ic ul t _ Ro a d _ Ch a r 1 D i f ic ul t _ Ro a d _ Co n d 1 H i g h _S p e ed F r am e_ L a b or _I n co n si st _w _ E D L a b o r_ H o ur s _ Fr am e < t o 5 5 t o t o 1 1 t o t o 2 > = 2 1 L a b o r_ H o ur s _ B od y < 5 5 t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 > = 7 1 L a b o r_ C o st _ Fr am e < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 L a b o r_ C o st _ B o dy < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 L a b o r_ R at e _ B od y t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ C o st _ Pa i nt < 1 1 to 2 1 D i f ic ul t _ Ro a d _ Co n d 1 H i g h _S p e ed F r am e_ L a b or _I n co n si st _w _ E D L a b o r_ H o ur s _ Fr am e < t o 5 5 t o t o 1 1 t o t o 2 > = 2 1 L a b o r_ H o ur s _ B od y < 5 5 t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 > = 7 1 L a b o r_ C o st _ Fr am e < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 L a b o r_ C o st _ B o dy < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 L a b o r_ R at e _ B od y t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ C o st _ Pa i nt < 1 1 to 2 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 A v g _ Fr a c_ B P L _ of _ LP _ to t al t o t o t o t o t o 1 1 L a b o r_ C o st _ B an d P < 2 2 to 5 5 to 1 1 t o 2 > = 2 1 F r a c_ B P L _o f _L P _t o ta l < t o. 5. 5t o t o 1 1 L a b o r_ R at i os _ ar e_ t o o_ H ig h T o o _M u c h_ L a bo r D i f er e n ce _wi t h_ H is t_ B P L _ Pa rt s < t o t o t o t o t o t o t o t o t o t o t o t o. 46 > = L a b o r_ R at e s_ H i gh e r_ t ha n _ Av g C l aim _ o ve rw r it te n A P _w il i ng _ to _ c ol u d e A P _ E x pe ri e nc e t o 1 2 to 5 5 to to 1 1 t o 2 2 t o 4 > = 4 1 A v g _ Fr a c_ B P L _ of _ LP _ to t al t o t o t o t o t o 1 1 L a b o r_ C o st _ B an d P < 2 2 to 5 5 to 1 1 t o 2 > = 2 1 F r a c_ B P L _o f _L P _t o ta l < t o. 5. 5t o t o 1 1 L a b o r_ R at i os _ ar e_ t o o_ H ig h T o o _M u c h_ L a bo r D i f er e n ce _wi t h_ H is t_ B P L _ Pa rt s < t o t o t o t o t o t o t o t o t o t o t o t o. 46 > = L a b o r_ R at e s_ H i gh e r_ t ha n _ Av g C l aim _ o ve rw r it te n A P _w il i ng _ to _ c ol u d e A P _ E x pe ri e nc e t o 1 5 t o 7 7 t o 1 1 A P _ P r ofi le _ Ri s k_ is _ Hi g h A P _ E x pe ri e nc e _i s_ L ow 1 A P _ r el _w _ In s _ C o_ is _W e ak N u m _o f _ AP _i n _ A P_ C om p a ny t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1 1 A P _ C om p a ny _i s _ La r ge _O I A 1 A P _ C o _i s _V e ry _ Sm al _O I A 1 A P _ C om p a ny _i s _ Ve ry _ Sm al 1 A P _ C om p a ny _i s _ La r ge 1 A P _ Y rs _W o rk i ng _w _I n s_ C o t o 1 > = 7 1 A P _ is _ ca r el es s A P _ N um _ A p pr _ d on e _ pe r_ mo n t h t o 1 2 t o 5 5 t o 1 1 t o 2 2 t o 4 4 t o 8 > = 8 5 t o 7 7 t o 1 1 A P _ P r ofi le _ Ri s k_ is _ Hi g h A P _ E x pe ri e nc e _i s_ L ow 1 A P _ r el _w _ In s _ C o_ is _W e ak N u m _o f _ AP _i n _ A P_ C om p a ny t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1 1 A P _ C om p a ny _i s _ La r ge _O I A 1 A P _ C o _i s _V e ry _ Sm al _O I A 1 A P _ C om p a ny _i s _ Ve ry _ Sm al 1 A P _ C om p a ny _i s _ La r ge 1 A P _ Y rs _W o rk i ng _w _I n s_ C o t o 1 > = 7 1 A P _ is _ ca r el es s A P _ N um _ A p pr _ d on e _ pe r_ mo n t h t o 1 2 t o 5 5 t o 1 1 t o 2 2 t o 4 4 t o 8 > = 8 1 A P _ V ol um e _i s_ L ow O d om_ U n re a d ab le S i g ni f_ El e ct r_ D am a ge O d om_ is _ Di g it al V e h ic le _M i le a g e t o 1 1 t o 1 1 t o 2 2 t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1e 5 e 5 1 L a b o r_ H o ur s _ Pa in t < 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 > = 5 1 L a b o r_ H o ur s _ El ec tr < 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 > = 5 1 L a b o r_ R at e _ El ec tr t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ C o st _ El ec tr < 1 1 t o 2 2 t o 5 5 t o 1 1 to 5 5 to 1 1 t o 2 > = A P _ V ol um e _i s_ L ow O d om_ U n re a d ab le S i g ni f_ El e ct r_ D am a ge O d om_ is _ Di g it al V e h ic le _M i le a g e t o 1 1 t o 1 1 t o 2 2 t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1e 5 e 5 1 L a b o r_ H o ur s _ Pa in t < 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 > = 5 1 L a b o r_ H o ur s _ El ec tr < 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 > = 5 1 L a b o r_ R at e _ El ec tr t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ C o st _ El ec tr < 1 1 t o 2 2 t o 5 5 t o 1 1 to 5 5 to 1 1 t o 2 > = 2 1 Mi le a g e_ is _Mi s si n g 1 M i le a g e_m is s _ du e _t o _c a re le s s 1 N u m _ Ap p r_ d o n e_ t ha t_ d a y t o 1 7 t o 8 8 t o 9 9 t o 1 1 t o t o 2 > = 2 1 N u m _ Ap p r_ p er _ D ay _ A v g t o 3 3 t o t o t o t o t o 4 4 t o t o t o t o t o 5 5 t o t o t o t o t o 6 6 t o t o 7 7 t o 1 1 t o 2 > = 2 1 D i f _ N um _ Ap p r_ fr om _ A v g < -3-3 to to to to t o. 5. 5t o 1 1 L o g i n_ N am e_ is _ S ha r ed 1 A P _ T o o _M a n y _ Ap p ra is al s 1 T o o _m a ny _ fo r _On e _ A P_ p er _ D ay 1 Mi le a g e_ is _Mi s si n g 1 M i le a g e_m is s _ du e _t o _c a re le s s 1 N u m _ Ap p r_ d o n e_ t ha t_ d a y t o 1 7 t o 8 8 t o 9 9 t o 1 1 t o t o 2 > = 2 1 N u m _ Ap p r_ p er _ D ay _ A v g t o 3 3 t o t o t o t o t o 4 4 t o t o t o t o t o 5 5 t o t o t o t o t o 6 6 t o t o 7 7 t o 1 1 t o 2 > = 2 1 D i f _ N um _ Ap p r_ fr om _ A v g < -3-3 to to to to t o. 5. 5t o 1 1 L o g i n_ N am e_ is _ S ha r ed 1 A P _ T o o _M a n y _ Ap p ra is al s 1 T o o _m a ny _ fo r _On e _ A P_ p er _ D ay 1 L K Q _ P ar t s_ C o st < 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 A M _ Pa rt s _ Co s t < 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 T o t al _ Pa rt s _ Co s t < 5 5 to 1 1 t o 2 2 t o 4 > = 4 1 D i f _ Fr _ L KQ _ v s_ E x pe c te d < t o to t o t o t o. 1. 1t o. 1 5 > = F r a cti o n _ AM < t o to.1. 1t o to t o to.2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 7. 7t o. 8. 8t o. 9. 9t o 1 1 E x p e ct e d_ L KQ _ Fr _ by _ Yr t o to to to to to to to.2. 2t o to to to to.3. 3t o to to.3 4 > = E x p e ct e d_ AM _ Fr _ b y_ Y r t o to to to to to t o to L K Q _ P ar t s_ C o st < 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 A M _ Pa rt s _ Co s t < 1 1 to 2 2 to 3 3 to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 T o t al _ Pa rt s _ Co s t < 5 5 to 1 1 t o 2 2 t o 4 > = 4 1 D i f _ Fr _ L KQ _ v s_ E x pe c te d < t o to t o t o t o. 1. 1t o. 1 5 > = F r a cti o n _ AM < t o to.1. 1t o to t o to.2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 7. 7t o. 8. 8t o. 9. 9t o 1 1 E x p e ct e d_ L KQ _ Fr _ by _ Yr t o to to to to to to to.2. 2t o to to to to.3. 3t o to to.3 4 > = E x p e ct e d_ AM _ Fr _ b y_ Y r t o to to to to to t o to to to to to to > = D i f _ Fr _ AM _v s _ Ex p ec t ed < t o to t o t o t o. 1. 1t o. 1 5 > = F r a cti o n _ L KQ <. 1. 1t o. 2. 2t o t o. 3. 3t o t o. 4. 4t o t o. 5. 5t o. 6. 6t o. 7. 7t o. 8. 8t o. 9. 9t o 1 1 V e h ic le _i s _ Ra re L e s s_ L KQ _t h a n_ E x p L e s s_ A M _ t ha n _ Ex p 1 S i g ni f_ P ar ts _ Di f S i g ni f_ P ar ts _ Di f _ ra re C a s e _i s_ IE 1 E x t en t _o f _ Dam a g e_ is _ Hi g h D A P _ U n d er bi d _I E to to to to to > = D i f _ Fr _ AM _v s _ Ex p ec t ed < t o to t o t o t o. 1. 1t o. 1 5 > = F r a cti o n _ L KQ <. 1. 1t o. 2. 2t o t o. 3. 3t o t o. 4. 4t o t o. 5. 5t o. 6. 6t o. 7. 7t o. 8. 8t o. 9. 9t o 1 1 V e h ic le _i s _ Ra re L e s s_ L KQ _t h a n_ E x p L e s s_ A M _ t ha n _ Ex p 1 S i g ni f_ P ar ts _ Di f S i g ni f_ P ar ts _ Di f _ ra re C a s e _i s_ IE 1 E x t en t _o f _ Dam a g e_ is _ Hi g h D A P _ U n d er bi d _I E 1 A P _ S _I E _ Pc t_ D if f_ H is t < -1-1 to to to t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 8. 8t o 1 > = 2 1 A P _ H is t _ Un d er b id _I E 1 A P _ R F _ Pa ir e d_ F re q u e ntl y A P _ R F _ Pa ir in g s _l as t_ Y e ar t o 1 1 t o 5 5 t o 1 1 R F _ Me di a n _I nc om e t o 2 2 t o t o 3 3 t o t o t o 4 4 t o t o 5 5 t o 6 6 t o 7 7 t o 8 > = 8 1 R F _ N h o o d_ R is k _i s_ H i gh R F _ N h o o d_ R is k y_ I nc om e 1 R F _ N h o o d_ H i gh _ C rim e 1 R F _ A v g _M on t h_ V ol _w _ C o_ P 6M t o 1 1 t o 5 5 t o 1 1 t o 2 > = 2 1 R F _ M o n t hl y_ V ol _ Al l_ C ar ri er s < t o 5 1 A P _ S _I E _ Pc t_ D if f_ H is t < -1-1 to to to t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 8. 8t o 1 > = 2 1 A P _ H is t _ Un d er b id _I E 1 A P _ R F _ Pa ir e d_ F re q u e ntl y A P _ R F _ Pa ir in g s _l as t_ Y e ar t o 1 1 t o 5 5 t o 1 1 R F _ Me di a n _I nc om e t o 2 2 t o t o 3 3 t o t o t o 4 4 t o t o 5 5 t o 6 6 t o 7 7 t o 8 > = 8 1 R F _ N h o o d_ R is k _i s_ H i gh R F _ N h o o d_ R is k y_ I nc om e 1 R F _ N h o o d_ H i gh _ C rim e 1 R F _ A v g _M on t h_ V ol _w _ C o_ P 6M t o 1 1 t o 5 5 t o 1 1 t o 2 > = 2 1 R F _ M o n t hl y_ V ol _ Al l_ C ar ri er s < t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1 1 to 2 > = 2 1 R F _ a n d_ C o _ R el ati o n _W ea k R F _ C o _ Fr _i s _L ow R F _ Mo n t hl y_ V ol _i s _L ow R F _ F r_ V ol _w _ C o < t o t o. 1. 1t o. 2 > =. 2 1 R F _ I nd iv _ Ri s k_ is _ Hi g h R F _ P r ofi le _ Ri s k_ is _ Hi g h R F _w il i ng _ to _ c ol u d e R F _w il i ng _ to _ n ot _f o l ow _ a p p A P _ R F _ R el ati o n _i s_ S tr o n g 1 A P _ g iv e s_ t o _m a ny _ c on c A P _ g ui d e d_ b y _ RF R e p ai r _i s_ C om pl e x t o 1 1 t o 2 2 t o 5 5 t o 1 1 to 2 > = 2 1 R F _ a n d_ C o _ R el ati o n _W ea k R F _ C o _ Fr _i s _L ow R F _ Mo n t hl y_ V ol _i s _L ow R F _ F r_ V ol _w _ C o < t o t o. 1. 1t o. 2 > =. 2 1 R F _ I nd iv _ Ri s k_ is _ Hi g h R F _ P r ofi le _ Ri s k_ is _ Hi g h R F _w il i ng _ to _ c ol u d e R F _w il i ng _ to _ n ot _f o l ow _ a p p A P _ R F _ R el ati o n _i s_ S tr o n g 1 A P _ g iv e s_ t o _m a ny _ c on c A P _ g ui d e d_ b y _ RF R e p ai r _i s_ C om pl e x M ak em o d el _F r eq u e nc y t o to to to t o t o t o. 4 > =. 4 1 P c t _ Dif f _S _ to _I E _ Vs _ Hi s t < to to t o t o. 1. 1t o. 2. 2t o. 5. 5t o 1 1 A P _ T o d ay _ U n de r bi d_ I E 1 S _ I E_ D if f_ Si g ni f _g iv e n _ Co mp le x 1 A P _ A v oi d s_ 2 n d_ V is it 1 V a l ue _I n c on si s te n t_w _ D ama g e R e p ai r _V al u e _i s_ H ig h 1 I E _t o _ A C V_ R at i o <. 5. 5t o. 8 > =. 8 1 A P _ S u p _t o _I E_ P ct _ Di f < -1-1 to to to t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 8. 8t o 1 > = 2 1 A P _ T e n ds _t o _W ri te _ Hi g h _I E 1 M ak em o d el _F r eq u e nc y t o to to to t o t o t o. 4 > =. 4 1 P c t _ Dif f _S _ to _I E _ Vs _ Hi s t < to to t o t o. 1. 1t o. 2. 2t o. 5. 5t o 1 1 A P _ T o d ay _ U n de r bi d_ I E 1 S _ I E_ D if f_ Si g ni f _g iv e n _ Co mp le x 1 A P _ A v oi d s_ 2 n d_ V is it 1 V a l ue _I n c on si s te n t_w _ D ama g e R e p ai r _V al u e _i s_ H ig h 1 I E _t o _ A C V_ R at i o <. 5. 5t o. 8 > =. 8 1 A P _ S u p _t o _I E_ P ct _ Di f < -1-1 to to to t o. 1. 1t o. 2. 2t o. 3. 3t o. 4. 4t o. 5. 5t o. 6. 6t o. 8. 8t o 1 > = 2 1 A P _ T e n ds _t o _W ri te _ Hi g h _I E 1 A P _ f ail e d_ t o_ s av e A v g _ Di f _ L ab o r_ R a te < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 C L _w il i ng _ to _ c ol u d e A P _ u nl ik el y _t o _ ha v e_ E x p_w _ Ve h R F _ C rim e _ Ra t e t o 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 C L _ N h o o d_ R is k y_ I nc om e 1 C L _ N h o o d_ H i gh _ C rim e 1 C L _ N h o o d_ Mo s tl y_ U r ba n 1 C L _ N h o o d_ R is k _i s_ H i gh C L _ Me di a n _I nc om e t o 2 2 t o t o 3 3 t o t o t o 4 4 t o t o 5 5 t o 6 6 t o 7 7 t o 8 > = 8 1 C L _ C rim e _ Ra t e t o 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 A P _ f ail e d_ t o_ s av e A v g _ Di f _ L ab o r_ R a te < -2-2 t o t o t o 1 1 t o 1 1 t o 2 > = 2 1 C L _w il i ng _ to _ c ol u d e A P _ u nl ik el y _t o _ ha v e_ E x p_w _ Ve h R F _ C rim e _ Ra t e t o 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 C L _ N h o o d_ R is k y_ I nc om e 1 C L _ N h o o d_ H i gh _ C rim e 1 C L _ N h o o d_ Mo s tl y_ U r ba n 1 C L _ N h o o d_ R is k _i s_ H i gh C L _ Me di a n _I nc om e t o 2 2 t o t o 3 3 t o t o t o 4 4 t o t o 5 5 t o 6 6 t o 7 7 t o 8 > = 8 1 C L _ C rim e _ Ra t e t o 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 C L _ F r_ B el ow _ P ov er t y t o t o. 1. 1t o t o. 2. 2t o. 4 > =. 4 1 C L _ P ct _ U rb a n t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 7 t o 8 8 t o 9 > = 9 1 R F _ F r_ B el ow _ P ov er t y t o t o. 1. 1t o t o. 2. 2t o. 4 > =. 4 1 R F _ N h o o d_ Mo s tl y_ U r ba n 1 R F _ P ct _ U rb a n t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 7 t o 8 8 t o 9 > = 9 1 C L _ R is k y_ P L ev el R F _ R is k y_ P L ev el R F _ h a s_ L ow _V o lu me R F _ is _ Sm al R F _ is _ D A P 1 C L _ F r_ B el ow _ P ov er t y t o t o. 1. 1t o t o. 2. 2t o. 4 > =. 4 1 C L _ P ct _ U rb a n t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 7 t o 8 8 t o 9 > = 9 1 R F _ F r_ B el ow _ P ov er t y t o t o. 1. 1t o t o. 2. 2t o. 4 > =. 4 1 R F _ N h o o d_ Mo s tl y_ U r ba n 1 R F _ P ct _ U rb a n t o 1 1 t o 2 2 t o 3 3 t o 4 4 t o 5 5 t o 6 6 t o 7 7 t o 8 8 t o 9 > = 9 1 C L _ R is k y_ P L ev el R F _ R is k y_ P L ev el R F _ h a s_ L ow _V o lu me R F _ is _ Sm al R F _ is _ D A P 1 R F _ N o t_ P ar t _o f _ De al er s hi p 1 R F _ N o t_ P ar t _o f _ Ne tw or k 1 R F _ N um _ Em pl o ye e s t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o R F _ L a b or _ Ra t es _ L ow e r R F _ h a s_ F ew _ Em p lo y e s I E _t o _ Po li cy L im _ R a ti o t o. 8 > =. 8 1 C L _ V e hi cl e_ Y rs _Ol d < 1 7 t o 8 8 t o 9 9 t o 1 1 t o t o t o t o t o t o D e d u ct i bl e t o 1 1 to to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 C o l is i on _ P oli c y Lim t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1e 5 e 5 1 C L _ V e hi cl e_ A C V t o 1 1 t o 2 R F _ N o t_ P ar t _o f _ De al er s hi p 1 R F _ N o t_ P ar t _o f _ Ne tw or k 1 R F _ N um _ Em pl o ye e s t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o R F _ L a b or _ Ra t es _ L ow e r R F _ h a s_ F ew _ Em p lo y e s I E _t o _ Po li cy L im _ R a ti o t o. 8 > =. 8 1 C L _ V e hi cl e_ Y rs _Ol d < 1 7 t o 8 8 t o 9 9 t o 1 1 t o t o t o t o t o t o D e d u ct i bl e t o 1 1 to to 4 4 to 5 5 to 1 1 t o 2 > = 2 1 C o l is i on _ P oli c y Lim t o 5 5 t o 1 1 t o 2 2 t o 5 5 t o 1e 5 e 5 1 C L _ V e hi cl e_ A C V t o 1 1 t o 2 2 t o 4 4 t o 5 5 t o 8 8 t o 1 1 t o t o t o t o 2 2 t o t o 3 3 t o 4 4 t o 6 > = 6 1 L o s s _Y e ar < 2 2 1t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o 2 2 > = V e h ic le _ Ye a r < t o t o t o t o t o t o t o t o t o t o t o t o 2 2 t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o 2 2 > = C a p t ur e d_ i n_ P h ys R C a p t ur e d_ i n_ P h ys R _ T S C a p t ur e d_ i n_ D e sk R 2 t o 4 4 t o 5 5 t o 8 8 t o 1 1 t o t o t o t o 2 2 t o t o 3 3 t o 4 4 t o 6 > = 6 1 L o s s _Y e ar < 2 2 1t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o 2 2 > = V e h ic le _ Ye a r < t o t o t o t o t o t o t o t o t o t o t o t o 2 2 t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o t o 2 2 > = C a p t ur e d_ i n_ P h ys R C a p t ur e d_ i n_ P h ys R _ T S C a p t ur e d_ i n_ D e sk R P h y s R _mi s e s_ S a vi n gs P h y s R _T S _mi s se s _ Sa vi n g s M at c he d _i n _ Ph y s R M at c he d _i n _ De s k R M at c h_ S c or e _P h y s R_ T S t o M at c h_ S c or e _ De s k R t o M at c h_ S c or e _P h y s R t o C L _ n o t_ at _ F au lt 1 C L _ is _ n ot _i n s ur e d 1 M aj or _ D am ag e _ Li ke ly 1 J o b _m u ch _ hi g h er _t h a n_ A C V 1 J o b _t o _ A C V_ R a ti o < 1 1 t o t o t o t o P h y s R _mi s e s_ S a vi n gs P h y s R _T S _mi s se s _ Sa vi n g s M at c he d _i n _ Ph y s R M at c he d _i n _ De s k R M at c h_ S c or e _P h y s R_ T S t o M at c h_ S c or e _ De s k R t o M at c h_ S c or e _P h y s R t o C L _ n o t_ at _ F au lt 1 C L _ is _ n ot _i n s ur e d 1 M aj or _ D am ag e _ Li ke ly 1 J o b _m u ch _ hi g h er _t h a n_ A C V 1 J o b _t o _ A C V_ R a ti o < 1 1 t o t o t o t o M at c he d _i n _ Ph y s R_ T S T o t al _ La b or _ H o ur s < 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 O ut p ut _ Pr o b a bil it y T L _O v er ri de 1 A P _ mi s e d_ s av i ng s A P _ m i s e d_ L a b or _ Sa vi n g s A P _ mi s e d_ p ar t s_ s av A P _ T y pe I A S A D A P 1 P r o ce s s_ S u p _I D t o 1 > = 2 1 V e h _i s _T o ta l_ L o s 1 J o b _ Hi g h er _t h a n_ A C V _ no t _T L 1 I E _J o b _ To t al _ Amt < t o 5 5 to to 1 1 t o 2 2 t o 3 3 t o 5 5 t o 7 5 > = 75 1 M at c he d _i n _ Ph y s R_ T S T o t al _ La b or _ H o ur s < 2 2 t o 4 4 t o 6 6 t o 8 8 t o 1 1 O ut p ut _ Pr o b a bil it y T L _O v er ri de 1 A P _ mi s e d_ s av i ng s A P _ m i s e d_ L a b or _ Sa vi n g s A P _ mi s e d_ p ar t s_ s av A P _ T y pe I A S A D A P 1 P r o ce s s_ S u p _I D t o 1 > = 2 1 V e h _i s _T o ta l_ L o s 1 J o b _ Hi g h er _t h a n_ A C V _ no t _T L 1 I E _J o b _ To t al _ Amt < t o 5 5 to to 1 1 t o 2 2 t o 3 3 t o 5 5 t o 7 5 > = 75 1 J o b _ T ot al _ Am t < t o 5 5 to to 1 1 t o 2 2 t o 3 3 t o 5 5 t o 7 5 > = 75 1 I n s ur e d_w a s_ R e ck le s s 1 C i ta ti o n _i s_ f or _I n su r ed 1 C i ta ti o n _i n di c_ re c kl es s 1 W h o _P o li ce _ R ep o rt _ Vi ol I V P B O U E N 1 A v g _ L ab o r_ R a te _ Pa i nt t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ R at e _ Fr am e t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 R F _ d id n t_ f ol ow _ ap p ra is al C L _ A P _ co l u de d J o b _ T ot al _ Am t < t o 5 5 to to 1 1 t o 2 2 t o 3 3 t o 5 5 t o 7 5 > = 75 1 I n s ur e d_w a s_ R e ck le s s 1 C i ta ti o n _i s_ f or _I n su r ed 1 C i ta ti o n _i n di c_ re c kl es s 1 W h o _P o li ce _ R ep o rt _ Vi ol I V P B O U E N 1 A v g _ L ab o r_ R a te _ Pa i nt t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 L a b o r_ R at e _ Fr am e t o 2 2 t o t o 3 3 t o t o 4 4 t o t o 5 5 t o t o t o 7 > = 7 1 R F _ d id n t_ f ol ow _ ap p ra is al C L _ A P _ co l u de d A P _ R F _ co l u de d C L _ mi sr e po rt e d _a c ci de n t L a b o r_ R at e _ Pa in t 2 t o t o 3 4 t o t o 5 6 t o t o 7 1 P a rt s _ Th r es h ol d < 1 4 to 5 > = 5 1 T o t al _ Pa rt s _ Hi g he r_ t ha n _ T hr es 1 T o t al _ Pa rt s _T h re s _ Di f For example, this claim s probability of being overwritten is 72.2%, but this value is the result of a complex combination of inputs 72.2% probability of overwriting Appraiser is 45% likely to have missed potential labor savings Excessive frame labor is likely a major source of overwriting

25 Policy, claim and other data is gathered to calibrate the predictive model. Data sources for possible inclusion in model Examiners and Repair Facilities Examiner Group Characteristics Independents Date of joining Prior incidences Experience level PRS Shops Expertise focus Training level Staff Examiners Historical ratios Repair Facilities Relationship with company Relationship with other carriers Part of a dealership or network Geographical information Appraisal information Labor Parts Labor Rates Amount Types of Labor Type and frequency Labor Hours Repair/replace rules Total Loss Miscellaneous Threshold Amount Repair guidellines Vehicle Valuation Betterment Prior Damage Mileage Accident Profile Characteristics Vehicle Make Model Year ACV Driver Age Sex Prior claims Violations Accident Type Comprehensive Low vs. high impact Single-car vs. multiple $ size of damage claim relative to medical claim Policy and Receivable Information Limits Deductible Vehicle new to policy Premium current? Payment basis? PA Knowledge Limited 23-25

26 Model calibration can be done with a small number of reinspection results, from which the model will continue to learn over time Model Calibration Process Step 1: Population Learning This calibrates model input nodes, using estimates without reinspection data Step 2: Performance Learning* This step uses re-inspection data to calibrate relationships between variables 1% 9% 94.3% 1% Correlation of estimates processed in intermediate models with those processed in the final model 8% 7% 6% 5% 4% 3% 2% estimates (none of them reinspected) were used to calibrate the input nodes 68.3% 1% % 82 In-house Physical Reinspections 391 Outsourced Physical Reinspections 1168 Desk Reviews * Performance Learning relies on an iterative optimization algorithm called Expectation Maximization, or EM. This algorithm maximizes the likelihood that the data used was generated by the model in question, each iteration modifies the conditional probability tables in the model to increase the likelihood that the model would generate the dataset. Source: PA s Experience with model calibration PA Knowledge Limited 23-26

27 Once the model is calibrated, it is used to calculate a probability of overwriting for all the claims processed by the system. Distribution of probabilities for historical estimates generated by the predictive model Disguised Low Probability of Overwriting 28% Mean High Probability of Overwriting PA Knowledge Limited 23-27

28 Using the model probabilities, reinspection hit rates on assigned claims both field and desk is substantially greater. Allocation of Capacity to Model Recommendations Desk Reviews (average prob.: 42%) Disguised The shaded area includes the top % of all estimates that can be reinspected by projected capacity Both Desk and Field Field Reinspections (average prob.: 65%) Claims allocation is based on company s reinspection capacity PA Knowledge Limited 23-28

29 The actual field hit rate closely matches the model generated probabilities. CPMS results Disguised client example Field Reinspection Hits Hit Rate Average Probability PA Knowledge Limited 23-29

30 Today s Agenda Defining the opportunity Approach to Auto Physical Damage claims leakage reduction Modeling methodology Technical Design Typical Process Appendix: Application to Bodily Injury Claims PA Knowledge Limited 23-3

31 A single system architecture which is necessary to enable the multiple data inputs for the models. Data Inputs for Predictive Models Illustrative Field Reinspection Data Policy Data Reinspection Payments Supplement Policy Desk Reviews Data Initial Estimate Predictive Model vehicle Claims Data Appraiser External Data Claimants Repair Facility Loss Information Financial Data PA Knowledge Limited 23-31

32 This single system architecture allows for a robust system that, while fully integrated, has a focused purpose. Solution System Architecture Overview Active Server Pages User Tasks Model Management Administration Management Reporting CMS Daily Extracts Estimate Data External Data Data Interface and Mapping Tool Model/Claim Database Model Interface Model Engine The model is part of an integrated IT solution required for daily analysis of claims and dispatch of reinspectors. PA Knowledge Limited 23-32

33 Included with our solution is a user interface that allows management of both the model and the information needed to achieve the benefits expected from your reinspection process. PA Knowledge Limited 23-33