STRATHMORE UNIVERSITY

Transcription

1 STRATHMORE UNIVERSITY A FRAUD INVESTIGATIVE AND DETECTIVE FRAMEWORK IN THE MOTOR INSURANCE INDUSTRY: A KENYAN PERSPECTIVE By KISAKA GEORGE NGOSIAH A thesis submitted in partial fulfillment of the requirements for the award of the Master of Science in Information Technology degree 2012

2 DECLARATION I certify that this thesis is my original work and all material in this thesis which is not my own work has been identified. I further certify that no material has previously been submitted and approved for the award of a degree by this or any other University. This thesis is available for library use on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement. SIGNED.DATE.. Kisaka George Ngosiah FIT/060478/10 This thesis has been submitted with my approval as the University Supervisor. Prof. Vitalis Onyango-Otieno. Director, Centre for Applied Research in Mathematical Sciences Strathmore University i

3 ACKNOWLEDGEMENT First I am thankful to the almighty God who has given me life and good health throughout my academic program. Without Him I would not have reached this far. I thank my research thesis supervisor Prof. Onyango-Otieno, for the invaluable time he spent guiding my efforts. Special thanks to Dr. Marwanga and Dr. Ateya of Strathmore University too for the invaluable input during the seminars. I would also like to acknowledge the remarkable input received from Mr. Wandera, Chairman of the Association of Kenyan Insurers (AKI). Thank you for making it possible to access invaluable data for analysis. Finally I would like to acknowledge the input from friends and colleagues that lent a hand directly or indirectly towards this effort. For the fantastic support you gave me please accept my sincere gratitude ii

4 ABSTRACT Insurance fraud is a serious and growing problem, with fraudsters always perfecting their schemes to avoid detection by the basic approaches. This has caused a rise in fraudulent claims that get paid and increased loss ratios for insurance firms thereby diminishing profitability and threatening their very existence. There is widespread recognition that traditional approaches to tackling fraud are inadequate. Studies of insurance fraud have typically focused upon identifying characteristics of fraudulent claims and putting in place different measures to capture them. This thesis proposes an integrated framework to curtail insurance fraud in the Kenyan insurance industry. The research studied existing fraud detection and investigation expertise in depth. The research methodology identified two available theoretical frameworks, the Bayesian Inference Approach and the Mass Detection Tool (MDT). These were compared to comprehensive motor insurance claims fraud management with respect to the insurance industry in Kenya. The findings show that insurance claims fraud is indeed prevalent in the Kenyan industry. Sixty five percent of claims processing professionals deem the motor segment as one of the most fraud prone yet a paltry 15 percent of them use technology for fraud detection. This is despite the fact that significant strides have been made in developing systems for fraud detection. These findings were used to determine and propose an integrated ensemble motor insurance fraud detection framework for the Kenyan insurance industry. The proposed framework built up on the mass detection tool (MDT) and provides a solution for preventing, detecting and managing claims fraud in the motor insurance line of business within the Kenyan insurance industry. iii

5 TABLE OF CONTENTS DECLARATION... i ACKNOWLEDGEMENT... ii ABSTRACT... iii TABLE OF CONTENTS... iv LIST OF TABLES... viii LIST OF FIGURES... ix DEFINITION OF TERMS... x LIST OF ABBREVIATIONS... xii CHAPTER INTRODUCTION Background Problem Statement Research Objectives Research Questions Justification Scope and Limitations Theoretical Frameworks... 6 CHAPTER LITERATURE REVIEW Introduction Insurance Fraud in the Kenyan Industry Motor Insurance Fraud in the Kenyan Industry Current Investigative and Detective Methods Auditing and the Optimal Contract Co-opetition and Common Databases Rule Based Approaches Government Regulation Predictive Analytics iv

6 2.4.6 Ethnography Fraud Detection Frameworks and Models The Mass Detection Tool for early detection of insurance fraud Bayesian Inference Approach Conclusion CHAPTER RESEARCH METHODOLOGY Introduction Study Population Sampling Frame Sample Size and Error Research Design Data Collection Methods and Techniques Questionnaires Secondary Sources Data Analysis Framework Validation CHAPTER THEORETICAL MODELS FOR FRAUD DETECTION Introduction Bayesian Inference Approach The Mass Detection Tool A Summary of the two Models Conclusion The Conceptual Framework CHAPTER ANALYSIS AND PRESENTATION OF FINDINGS Introduction Respondents Demographics Existing Forms of Insurance Fraud Motor insurance fraud v

7 5.2.2 Health Insurance Fraud Property Insurance Fraud Life Insurance Fraud Conclusion Existing Methods for Detecting and Preventing Fraudulent Activity Detection of Fraudulent Activity Prevention of Fraudulent Activity Other Approaches to Detecting Potential Fraud and Fraudsters Use of Information Systems to curtail Motor insurance fraud Current Forms Future Expectations External Sources Interpretation of Findings CHAPTER FRAMEWORK FOR FRAUD DETECTION BY KENYAN INSURANCE COMPANIES Current Practice in Relation to Theory Gaps in Current Theory and Practice Conclusions Relating Theory and Practice New Framework from Research Findings and Current Theory The Proposed Framework The Proposed Framework s Execution / Validation CHAPTER DISCUSSION, CONCLUSION AND RECOMMENDATIONS Discussion Conclusion Recommendations Directions for Future Research REFERENCES APPENDICES Appendix A Research Questionnaire Appendix B Gross Written Motor Insurance Premium for Year 2010 (Kshs) vi

8 Appendix C Ethnographic Information Appendix D List of Fraud Indicators vii

9 LIST OF TABLES Table Bodily Injury Liability Claim Sample 5 Table General Business Incurred Claims Trend 11 Table Incurred Claims Ratio per Class of Business 13 Table Insurers Registered for Each Class of Business 22 Table Number of General Insurance Business Insurers 22 viii

10 LIST OF FIGURES Figure Increase in Questionable Claims in the US 7 Figure Detected General Insurance Claims Fraud 8 Figure The Mass Detection Tool 29 Figure The Conceptual Framework 32 Figure Insurance Industry Experience of Respondents 33 Figure Forms of Insurance Fraud and Their Prevalence 34 Figure Methods by Which Fraudulent Activity is Detected 38 Figure Methods by Which Fraudulent Activity is Prevented 39 Figure 6.1 The Proposed Framework 48 Figure The Proposed Framework s Execution 50 ix

11 DEFINITION OF TERMS Apparent Fraud: A claim in which there was no injury/loss or the injury was unrelated to the accident Buildup claim: A claim in which the injury/loss is exaggerated and/or the treatment is excessive Carrier: Insurer or Insurance Company. Claim: An insurance claim. A formal request or application to an insurance company asking for a payment or benefit based on the terms of the insurance policy. Co-opetition: Insurance industry term referring to the sharing of information on claims and other data across the industry Ethnography: The branch of anthropology that provides scientific description of individual human societies Insurance Fraud: Any act committed with the intent to deceitfully obtain payment from an insurer can be described as insurance fraud. Insurance fraud occurs when the insurer does not know all the facts about the insured and the claim, and when the fraudster believes that some monetary benefits can be gained by distortion of such facts Insurance Fraud Bureau: A not for profit making organization in the UK funded by the insurance industry, specifically focused on detecting and preventing organized and cross industry insurance fraud Insurer: See Carrier Loss ratio: The amount of claims against premiums paid x

12 Peril: A source of danger; a possibility of incurring loss or misfortune Railway spine: A nineteenth-century diagnosis for the post-traumatic symptoms of passengers involved in railroad accidents Trip and fall: Accident that involves pedestrians getting their feet caught on an object which causes them to fall Whiplash: A range of injuries to the neck caused by or related to a sudden distortion of the neck commonly associated with motor vehicle accidents xi

13 LIST OF ABBREVIATIONS ABI: Association of British Insurers AKI: Association of Kenya Insurers BBN: Bayesian Belief Network CRB: Credit Reference Bureau IFB: Insurance Fraud Bureau IMIDS: Integrated Motor Insurance Data System IRA: Insurance Regulatory Authority MDT: Mass Detection Tool for early detection of insurance fraud NICB: The United States National Insurance Crime Bureau PwC: PricewaterhouseCoopers xii

14 CHAPTER INTRODUCTION 1.1 Background The essence of fraud is deception (Blan and Hart, 1985). Whatever industry the fraud is situated in or whatever kind of fraud you visualize, deception is always the core of fraud (Jans, Lybaert and Vanhoof, 2010). Insurance fraud has existed ever since the beginning of insurance as a commercial enterprise and is a major problem in the United States at the beginning of the 21st century. It has no doubt existed wherever insurance policies are written, taking different forms to suit the economic time and coverage available. From the advent of railway spine in the 19th century to trip and falls and whiplash in the 20th century, individuals and groups have always been willing and able to file bogus claims (Derrig, 2002). Many definitions of insurance fraud are in common use. In this study, insurance fraud or claim fraud is used to refer to criminal acts that involve making the willful act of obtaining money or value from an insurer under false pretenses or material misrepresentations (Derrig, 2002). This therefore includes cases where a claimant fabricates or causes an accident to happen, to obtain payments they might otherwise not deserve. In recent years, economic analysis of insurance fraud has developed along two lines. The first one is mostly theoretical and its foundations may be found in the theory of optimal auditing. It aims at analyzing the strategy of insurers under claims fraud or application fraud. This approach mainly focuses on questions such as: What should be the frequency of claim auditing and how do opportunistic policyholders react to the 1

15 auditing strategy? What are the consequences of potential fraud on the design of insurance contracts, especially with regard to the indemnity schedule? What is the deterrence effect of an auditing policy? What is the role of good faith when insurance applicants may misrepresent their risk? The second branch of the literature on insurance fraud is more statistically based. This focuses mainly on the significance of fraud in insurance portfolios; on the practical issue of how insurance fraud can be detected; and on the scope of automated detection mechanisms in lowering the cost of fraudulent claims (Dionne, Giuliano and Picard, 2003). In this paper, the researchers explore the possibility of an integrated framework encompassing these two complementary approaches to insurance fraud. As in other forms of fraud in today s organizations, fraud detection and control will involve both manual intervention and system support. Manual intervention in insurance fraud detection involves picking up claims randomly for intensive investigation. In a non-commitment Costly State Verification setting insurers can only detect fraudulent claims by performing costly audits, and policyholders are overcompensated by the optimal insurance contract (Schiller, 2006). The focus is on the deterrence effect of the auditing strategy and on the consequences of insurance fraud on the design of insurance contracts System intervention on the other hand has taken several forms. Co-opetition, which is an industry term referring to the sharing of suspect claims data across the industry is one way. It is like having a central repository of suspect data. This has however had its challenges since regulatory support is needed. Rule based approaches leveraging on experience have been used too. These are based on a growing set of business rules, to warn about suspect claims. To avoid same tooth extraction more than once! 2

16 Statistical approaches that compare claim values against historical data of similar claims have also had some success. Here, outliers or claims exceeding some predefined variances are flagged. Poor tuning however leads to several false positives and false negatives being encountered. Pattern mapping is tipped to be the future. Using analytics, clustering and data mining to unravel suspect patterns and compare a given case against such patterns. This has however not been well researched on and few implementations exist. In this thesis the researchers determine the types of fraud that are currently encountered and present a framework to curtail fraud using information systems that investigate and / or detect incidences of fraud. Fraud inhibits a carrier s ability to charge lower market-leading premiums and has a detrimental impact on loss ratios. Using a combination of business rules, social networking analysis, predictive modeling and other techniques, it is possible to detect and prevent fraudulent claims before they are paid. 1.2 Problem Statement Insurance fraud has increased following fraudsters improved schemes to avoid detection hence the need for an integrated framework to curtail fraud. Information systems used in Insurance have been used mainly for administrative support, product development, new business processing, product distribution and client service. They have contributed very little to the fight against fraud in the industry at best. A study carried out by Neirotti and Paolucci (2007) reveals that technological and business path dependencies, along with time compression diseconomies, resulted in diversities in IT adoption dynamics due to their differences in IT governance and management practice. The study also showed that competitive advantages were not correlated with IT spending levels nor with the kind of IT investments that made general productivity growth in the industry possible (Neirotti and Paolucci, 2007) 3

17 In this thesis the researchers study the use of information technology systems in the insurance industry. The researchers then propose a framework that incorporates fraud investigation in the information systems. The thesis recommends a framework that introduces fraud detection capability in insurance information systems to investigate potentially fraudulent claims. 1.3 Research Objectives The purpose of this research was to determine and analyze the perennial problem of insurance fraud and develop a framework to curtail it. The framework introduces fraud detection capability in insurance information systems to identify potentially fraudulent claims. Specifically, the research objectives were:- i) To identify the existing forms of insurance fraud in the Kenyan insurance industry ii) To find out what models exist for detecting Motor insurance fraud in the global insurance industry iii) To propose a framework by which Information Systems can be employed to curtail Motor insurance fraud in the Kenyan insurance Industry iv) To validate the framework 1.4 Research Questions To achieve this, the researchers sought to answer the following questions:- i) What forms of insurance fraud have been detected in the insurance industry? ii) What models exist for detecting Motor insurance fraud in the insurance industry? iii) Based on what structure can new and existing Information Systems be applied to curtail Motor insurance fraud in the Kenyan insurance industry? 4

18 iv) How does this new structure meet the desired goal? 1.5 Justification Clearly both the local and global insurance industries have been hit by massive incidences of Motor insurance fraud. Owing to the material nature of the losses encountered due to insurance fraud, the industry is greatly concerned with the detection of fraudulent behavior. A lot has been written about how to detect fraud. However many authors state that prevention should take precedence over detection (Jans et al., 2010). Statistics from the US Insurance Fraud Bureau (IFB) show that although fraud is detected, prosecution success is very little. There is therefore need to prevent rather than detect fraud after the fact. Table Bodily Injury Liability Claim Sample 1989 Bodily Injury Liability Claim Sample "Fraud Definition" Approx Claim Count (%) 1. Apparent Fraud or Build-up 43.80% 2. Apparent Fraud Only 9.10% 3. Apparent Fraud Referable for Criminal Investigation 1.00% 4. IFB Referrals Qualifying for Active Investigation 0.50% 5. IFB Investigations Referable to Prosecution 0.10% 6. Prosecution Successes 0.09% Source: AIB Studies of 1989 BI Claims; RAD estimates of IFB Data The table shows that it is almost impossible to recover cash paid out through fraudulent claims, even when litigation is used. Predictive approaches to fraud detection therefore provide a more realistic and cost-effective avenue to curtailing fraud. Carriers use adjusters to routinely investigate claims and negotiate settlements based on the adjuster s opinion of what the actual loss is. The adjuster s skill is therefore refined 5

19 with experience posing the problem of older experienced adjusters who are nearing retirement. They are taking with them the experience it takes to identify fraud hence the need for preservation of this capacity within information systems Certainly, there was a business case to explore the opportunity to use Information Technology to curtail the rising number of incidences of insurance fraud. 1.6 Scope and Limitations The scope of our research was motor insurance claims fraud. Raw data collected from published studies was reviewed as part of the study and cited. Data from the Association of Kenyan Insurers, the Insurance Regulatory Authority and five insurance companies was collected and reviewed as part of the study too. The study was limited only to the output that the respondents provided. This includes data and information from questionnaires administered. In addition, information from various items of literature was gathered and analyzed alongside questionnaire results. All this was in the area of motor vehicle Insurance claims. The researchers did not study claims in other lines of insurance business and alternative investment / savings products such as unit trusts and pensions 1.7 Theoretical Frameworks The Bayesian Inference Approach and the Mass Detection Tool (MDT) are used to provide theoretical basis to this thesis. The Bayesian Inference approach falls under the predictive modeling category of frameworks. It uses a Bayesian learning algorithm to predict occurrence of fraud. The MDT on the other hand is an experience based approach that uses a growing set of business rules, to warn about suspect claims. This thesis uses the MDT as a basis for the proposed framework. 6

20 CHAPTER LITERATURE REVIEW 2.1 Introduction Insurance fraud is indeed a growing problem for the global insurance industry. The Association of British Insurers estimate that fraud from personal lines cost the UK industry the equivalent of Kshs. 119 billion in the year 2001 alone. Insurance fraud is difficult to deal with, partly because it has many varieties, for instance, bogus or inflated claims, staged incidents and systematic scams. Additionally, protective measures delay payment of genuine claims, impacting negatively against sales volume and company reputation (Ormerod, Morley, Ball, Langley and Spenser, 2003). The American Insurance industry studies indicate that 10 per cent or more of property/casualty insurance claims are fraudulent. Fraud is the second most costly white-collar crime in America behind tax evasion (Jans et al., 2010) Tampa Miami Orlando New York Los Angeles Figure 2.1 Increase in questionable claims in the US (Jans et al., 2010). 7

21 Further statistics from The United States National Insurance Crime Bureau (NICB) showed a steady increase in questionable vehicle claims from January 2009 through December There were 84,407 questionable claims referred to the NICB from its member insurance companies in In 2010, that number increased to 91,797. In 2011, that number increased again to 100,450, a record level. This represents a 9.4 percent increase from 2010 to Over the two year timeframe from 2009 to 2011 there was a 19 percent increase. In the vehicle category, questionable vehicle theft logged the most referrals in This segment had 11,451 referrals in 2011 after posting 2,182 referrals in 2010, a 450 percent increase from 2009 (NICB, 2012). According to the Association of British Insurers (ABI), undetected general insurance claims fraud totaled around 1.9 billion pounds a year. Fraudulent claims now account for a significant portion of all claims received by insurers. The value of detected general insurance claims fraud reached 730m in 2008 having steadily increased from 2004 (Association of British Insurers, 2009) Fraud savings ( m) Figure 2.2 Detected General Insurance Claims Fraud (Association of British Insurers, 2009) 8

22 The most common and costly form of general insurance claims fraud is opportunistic retail fraud. Opportunistic retail fraud is where individuals exaggerate or inflate genuine claims to increase the value of a payout. In a minority of cases opportunistic fraudsters would fabricate an entire claim, including, for example, deliberately causing damage so as to be able to claim. Opportunistic fraud in commercial general insurance is similar to opportunistic retail fraud but the policyholders are firms, rather than individuals ( Association of British Insurers, 2009) Historically Insurance has been a leading industry in the utilization of Information technology (Harris and Katz, 1991). The focus in these early years was primarily on administrative support, product development, new business processing, product distribution and client servicing. As a result, today many banks and insurance companies still depend on systems first developed over 30 years ago (Ward and Peppard, 2002, p.1) which have very little if any focus on security. These systems are almost useless in the fight against insurance fraud. Insurance claim fraud takes the form of nonexistent claims, adding items to a genuine claim or inflating the estimated loss value. Whichever the form, insurance fraud drains insurers. Carriers are well aware that a claimant has numerous opportunities and financial incentives to take advantage of accidents. The claimant knows exactly what happened whereas the company only has some information about what happened. Claims thus need to be reviewed and adjusted before payment, which is done at a cost. There is therefore need to sort incoming claims efficiently into categories that require the acquisition of additional information at a cost. This is known as costly state verification. For many insurers, using costly audits and explicit contracts to combat fraud has been the predominant approach (Bond and Crocker, 1997). This has led to fraud 9

23 investigations being handled in a reactive manner. The only proactive initiative in this case is dependent on the experience or intuition of the person processing the claim. 2.2 Insurance Fraud in the Kenyan Industry Insurance fraud is perhaps more of a nightmare for insurance companies in Kenya and indeed Africa as would be in most emerging economies. As carriers in this market leverage more on electronic distribution, volumes grow and with it fraud cases increase as do other related challenges, including data and transactions security and real time processing. Micro-insurance, a type of formal insurance mechanism that protects lowincome people against specific perils, had also led to drastic increase in volumes. This is so due to the high number of low-income people in the agricultural sector which has, for many years, formed the backbone of Kenya's economy. The Kenyan insurance industry has recently become concerned with the detection of fraudulent behavior. Of particular concern is fraudulent behavior in the Motor and Health lines of business. Statistics from the Association of Kenyan Insurers Insurance Industry Annual report 2009 reveal that loss ratios have been on the rise. Private and commercial motor claims ranked the highest with the loss ratio related to Private Motor insurance at 80.8%. A Medical Insurance underwriting loss of Kshs. 236M was registered in 2009 as compared to a profit of Kshs. 33.1M in the year 2008 (Association of Kenya Insurers, 2010). The industry estimates that 35% of all non-life claims are fraudulent. Data from the Association of Kenya Insurers (AKI) shows that Kshs. 20 billion was collected from both private and commercial motor vehicles in the year 2010 and Kshs. 13 billion was paid out as claims. According to the AKI Annual report for 2010, the health insurance segment made the highest loss ratio, a massive 81.5 per cent, followed by motor private insurance at 74.9 per cent (Association of Kenya Insurers, 2011). 10

24 Table 2.1 General Business Incurred Claims Trend (Association of Kenya Insurers, 2010). Class of Business Years Aviation 9,020 1,417 10,065 2,737-4,780 Engineering ,767 81, , ,396 Fire - Domestic 107, , , , ,407 Fire Industrial 148, , , , ,239 Liability 147, , , , ,877 Marine 229, , , , ,942 Motor Private 3,204,388 3,286,171 3,565,915 4,502,851 5,282,589 Motor Commercial 3,002,312 3,634,622 4, ,875,612 6,317,808 Personal Accident 1,879,278 2,769,091 3,232,202 3,490,256 4,604,216 Theft 366, , , , ,651 Workmen s compensation 1,090,642 1,182,637 1,542, ,465 1,002,722 Miscellaneous 111, , , , ,254 TOTAL 10,383,822 12,359,561 14,235,405 15,868,328 19,768,322 Figures in thousands Kshs A survey by PricewaterhouseCoopers (2011) on risk in East Africa's financial services sector had identified fraudulent claims as one of the major risks facing insurance firms in the region. Carriers estimated that they lost a total of Sh. 4 billion, paid every year to undeserving parties. Motor insurance is the worst hit industry segment and a bulk of the money lost in fraudulent claims by insurers is through rampant fraud in motor insurance. In insurance markets where the industry is still relatively immature, like East Africa, an increasing incidence of fraud will test the capacity of insurance claims settlement procedures and consumer protection laws ( PwC Risk Survey 2011, 2011). 2.3 Motor Insurance Fraud in the Kenyan Industry The Kenyan Medical and Motor insurance business segments, the mainstay of the industry, are the worst hit with fraud with medical underwriters making loses as a result of increasing fraud. Fraud in the Insurance Industry is probably as old as the industry itself and, it is a worldwide problem. Motor Insurance remains one of the classes of insurance where fraud is rampant. Statistics point to fraud accounting for as much as between 30 to 40 percent of all motor claims paid by the insurers in Kenya. 11

25 Year 2009 statistics by Association of Kenya Insurers (AKI) indicate that the total claims paid by the industry under the motor class of business were up to Kshs.10 billion. If the 30 to 40 percent estimate was to be confirmed it would mean that the industry paid as much as Kshs. 3 to 4 billion by way of fraudulent claims (Gichuhi, 2011). Most of the fraudulent claims in the industry are from public service vehicles and involved inflation of passenger numbers and exaggeration of injuries that in turn saw carriers pay hefty amounts. Following increase of insurance related fraud, the Insurance Regulatory Authority (IRA) introduced reforms in the industry. These included a standard contract for insurance across different insurance companies and the establishment of an Insurance Anti-Fraud Police Unit in October The IRA is the industry regulator tasked with the mandate of regulating, supervising and developing the insurance industry in Kenya. It therefore creates an appropriate legal framework to ensure efficient and effective supervision of the industry. According to the IRA Chief Executive Officer, fraud is rife in the insurance industry, especially in the motor vehicle class of insurance business. This led to the collapse of some insurance companies in the market segment. This trend was attributed to the firms involvement in the public service vehicles insurance business beset by soaring claims, fraud and litigation (Oyuke, 2012). 12

26 Table 2.2 Incurred Claims Ratio per Class of Business (Association of Kenya Insurers, 2010). Class of Business Years Aviation Engineering Fire - Domestic Fire Industrial Liability Marine Motor Private Motor Commercial Personal Accident Theft Workmen s compensation Miscellaneous Total Industry Average From Table 2.2, Motor Private, Personal Accident, Motor Commercial and Theft had the highest claims incurred ratios in the year These classes of business had claims ratios above 55.0% and showed a rising trend over the years. Motor Private came out as the most loss making class of business under general insurance business. Without Motor Private the industry would have made an underwriting profit of over Kshs. 1.7 billion during 2009 but made a paltry Kshs. 401, instead. 2.4 Current Investigative and Detective Methods Insurance claim payment is considered a simple multiple step process. It involves claims submission, review, and approval processes that result in a claims being paid or rejected. The process starts when an auto claim form is submitted. Evaluation is then done where the carrier verifies the claim applicant s information and this is reviewed by the claims adjuster. The adjuster assesses the damage to the vehicle. Information including claim forms, adjuster reports, police reports and photos will determine whether to reject, accept and pay or request more information. 13

27 The responsibility for detecting fraudulent claims in insurance companies rests heavily with staff at the front line of the claims handling process. Claims handlers are often inexperienced, with typical company lifetimes of less than one year, and they often lack sufficient or appropriate training in fraud detection. In order to increase the chances of detecting fraudulent claims by inexperienced staff, companies have traditionally provided claims handlers with lists of fraud indicators against which to check incoming claims (Doig, Jones and Wait, 1999). Carriers are bound by regulations with regard to how long the evaluation process can take as Insurance law requires firms to process claims within 90 days. The evaluation stage is the point at which fraud is either detected or missed. If missed here, the fraud may be detected after payment. According to Bond and Crocker (1997), using costly audits and explicit contracts to combat fraud has been the predominant approach (Bond and Crocker, 1997) Auditing and the Optimal Contract In a non-commitment Costly State Verification setting, insurers can only detect fraudulent claims by performing costly audits, and policyholders are overcompensated by the optimal insurance contract (Schiller, 2006). Since this is a costly approach, a claim adjusting process is involved. The claim adjusting process is in theory a narrowing of the information asymmetry that exists for every claim. The claimant knows exactly what happened whereas the carrier only has bits of information about what happened. The carrier needs to ascertain the information s accuracy and determine the appropriate payment to be made or if the claim is to be denied. Carriers have the discretion to spend as little as possible on a claim or invest in acquiring information to resolve the asymmetry (Derrig, 2002). 14

28 2.4.2 Co-opetition and Common Databases Where historical information is available, a common database from all insurers containing the details of all the declined risks and reasons behind the same can suffice. This, coupled with an internal database that maintains details on all frauds which have happened over the years, could go a long way in isolating claims for further analysis. Since all the insurers in the particular market are involved, the cost is shared or borne by the regulator. This approach makes not just detection easy but improves underwriting capabilities by flagging potentially fraudulent business before signing the contract. Experience however is costly to obtain as it involves learning from mistakes, which are costly in the first place Rule Based Approaches Rule based approaches leveraging on experience have been used too. These are based on a growing set of business rules, to warn about suspect claims. To avoid same tooth extraction more than once! There are two main rule based approaches; clustering and expectations. In the clustering approach, normal patterns are grouped together to form a cluster and any deviations from the norm flag suspicious cases. These are then further investigated for fraud. They indicate outliers only and not necessarily fraud cases. On the other hand, the expectations approach focuses on expected values, for instance the expected value of a windshield claim, and compares it with the actual value. Large deviations are suspicious. This approach requires a predictive model that generates the expectations. Here too, outliers or claims exceeding some predefined variances are flagged. 15

29 With rule based approaches, poor tuning leads to several false positives and false negatives being encountered Government Regulation Insurance companies, through the Insurance Regulatory Authority (IRA), have taken further proactive steps to improve fraud detection during the claims-handling process. For example, the industry has developed a database that is shared across all motor business carriers to assist in the detection of anomalous information at the claims stage. The database however presents a problem for the detection of fraud. In particular, the quality of data held within the database is not verified. Since the data is gathered by different carriers based on information supplied by the client, there is always the risk that data entry will be done independently and repetitively for each transaction that the same customer has with a company. This introduces noise and diminishes the perceived gains Predictive Analytics Predictive Modeling and Predictive Analytics have been buzzing around the claims industry for years. This approach constructs a predictive model that predicts the probability of fraud. Such a model attempts to differentiate fraud from non-fraud cases and hence requires data from both categories to facilitate learning. The return on investment (ROI) for predictive analytics is pretty easy to calculate. It is simply the percentage uptick in number of claims flagged and percentage of those claims mitigated, denied, or reduced. False positives are always an issue here too and create the need to consistently tweak parameters in analytics. Using analytics, clustering and data mining to unravel suspect patterns and compare a given case against such patterns has not been well researched on and few implementations exist. 16

30 2.4.6 Ethnography Ethnography provides perhaps the best way to profile fraud based on a particular market. Ethnography involves the immersive study of work practices in realistic contexts, in which the observer works within the system under study for extended periods of time, observing and documenting everyday activities as well as exceptional events (Ormerod, Ball and Morley, 2010). Employing an ethnographic approach complements the statistical, interview and questionnaire methods used in studies of fraud types and fraudster profiling by providing a detailed, longitudinal and independent evaluation of issues and activities surrounding how the industry deals with fraud (Ormerod et al., 2003). 2.5 Fraud Detection Frameworks and Models Different frameworks and models have been developed to try and curb the issue of motor insurance fraud. These can mainly be classified into predictive and reactive. Predictive models detect and deter fraud before it occurs while reactive ones detect fraud after the fact. Generally, fraud detection can be done using one of three approaches; Clustering, expectations or predictive modeling. The clustering and, expectations approaches highlight suspicious cases for further fraud investigation while the predictive modeling approach directly predicts the probability of fraud. The researchers analyzed two models, The Mass Detection Tool (MDT) for early detection of insurance fraud (Ormerod et al., 2003) and the Bayesian Inference approach. The MDT is a clustering approach while the Bayesian Inference approach falls under the predictive modeling category. 17

31 2.5.1 The Mass Detection Tool for early detection of insurance fraud The Mass Detection Tool (MDT) for early detection of insurance fraud is an experience based approach that uses a growing set of business rules, to warn about suspect claims. The aim of the MDT was to act as a filter for all claims, providing the claims handler with the confidence to pay genuine claims quickly while selecting out suspicious claims for further investigation. The core to the MDT is a Bayesian Belief Network (BBN) that calculates the probability of the current claim being fraudulent, based upon the prior probabilities of claims possessing the same range of fraud indicator ratings being proven as fraudulent. The calculations are based upon a store of prior probabilities of frauds given indicator ratings that is assembled from feedback of previous claim outcomes (pay, refer or refuse). So, indicators become more or less predictive of fraudulent versus genuine claims over time. The MDT is non-coercive in that although it recommends whether a claim should be paid or referred, it leaves that decision to the user. This is important since the MDT s capacity to learn is partly driven by the user overriding its advice and partly by the final decision taken downstream by the fraud investigators Bayesian Inference Approach Here, a Bayesian learning algorithm to predict occurrence of fraud. In the Bayesian Inference approach, several state-of-the-art binary classification techniques are experimentally evaluated in the context of expert automobile insurance claim fraud detection (Viaene, Derrig and Dedene, 2005). Two Bayesian networks are created to describe the behavior of auto insurance. First, a Bayesian network is constructed to model behavior under the assumption that the driver is fraudulent and another model under the assumption the driver is legal. The fraud net is set up by using expert knowledge. The legal net is set up by using data 18

32 from legal drivers. By inserting evidence in these networks, we can get the probability of the measurement E under the two mentioned assumptions. This means, we obtain judgments to what degree an observed user behavior meets typical fraudulent or legal behavior. In general and by applying Bayes rule, we get the probability of fraud from inference based on a multiplicity of factors. These include driver age, driver rating, vehicle age / price, claim value and number of previous claims. 2.6 Conclusion Growth of the insurance industry has led to the expanding application demand for data mining of massive data warehouses (Hong and Weiss, 2001). Fraud detection is one of the areas that have fueled advances in automated predictive methods. Specifically, pattern mapping and the use of analytics, clustering and data mining have been used to detect insurance fraud. This approach is used to unravel suspect patterns and compare claims cases against such patterns. Predictive analytics can enhance the work of investigators by uncovering complexities the human eye may miss (Roosevelt, 2011) The problem of insurance fraud is prevalent and threatens the very existence of insurance companies. Several efforts have been made to institute proactive solutions for tackling the major problem of insurance fraud. This problem however needs to be tackled using a multi-disciplinary approach ensemble as the individual documented approaches tackle only individual facets. An Information technology framework can supplement traditional specialized investigation units, statistical analysis of claims information, mathematical models, costly audits and explicit insurance contracts in the investigation and detection of fraudulent cases by integrating the approaches into a framework. The framework can 19

33 also be used to capture expert best practice while observing pitfalls that prevent the successful detection of fraud. 20

34 CHAPTER RESEARCH METHODOLOGY 3.1 Introduction Research design involves linking of research questions to empirical data so as to be able to come up with tangible research conclusions. It sets out the logic to the enquiry. Several key components comprise a practical research design. These include the research questions, the study's proposition, the study's units of analysis, the logic that links data to propositions and the criteria for interpretation of the research findings. The choice of a research methodology is thus dictated upon by the research questions and purpose. This research intends to propose a framework intended to provide a solution for preventing, detecting and managing claims fraud in the motor insurance line of business within the Kenyan insurance industry. The research questions raised in this thesis required a combination of various techniques to be put in use so as to answer the questions. The first question sought to establish what forms of insurance fraud have been detected in the Kenyan insurance industry. This is available from reports and media as well as subject matter experts in the industry. Article reviews and questionnaires were used to gather this information. The next question dealt with the approaches to fraud detection in the industry. This gives a view of how the carriers currently handled the problem of insurance fraud. This was elicited from insurance and ICT experts in the insurance sub-sector. This question also sought to address the issue of existing theoretical frameworks and models for detecting Motor insurance fraud in the global insurance industry. Here the aim was to find theory supporting a comprehensive view of motor insurance fraud detection, prevention and management. These were available in academic published literature. 21

35 Two available theoretical frameworks were identified and compared to comprehensive motor insurance claims fraud management with respect to the insurance industry in Kenya. This led to the establishment of a framework that was best suited for the insurance industry in Kenya. A review of what extensions were needed and what the theoretical frameworks overlooked led to the proposed framework thereby answering the third question. The researchers then tested the framework for validity of results to answer the fourth and final research question. 3.2 Study Population For purposes of this research, stratified random sampling of industry experts was used. The insurance industry had 46 insurance companies registered to transact insurance business in Kenya in Of these, 35 were licensed to transact in the motor segment. Table 3.1 Insurers Registered for Each Class of Business (Insurance Regulatory Authority, 2009). Category Number Long term business insurers 10 General business insurers 20 Composite insurers 14 Reinsurance companies 2 TOTAL 46 Table 3.2 General Insurance Business Insurers (Insurance Regulatory Authority, 2009). Serial No. Description No Serial No. Description No 1 Aviation 7 2 Engineering 33 3 Fire - Domestic 33 4 Fire Industrial 33 5 Liability 33 6 Marine 33 7 Motor Private 33 8 Motor Commercial 34 9 Personal Accident Theft Workmen s compensation Miscellaneous 33 22

36 3.3 Sampling Frame The sampling frame is a list of all those within a population who can be sampled. For purposes of this study, the top fifteen of the thirty five insurance companies licensed to transact in the motor segment were chosen. The criterion used for ranking the insurance companies was total gross written premium for the two motor insurance classes of business in the year The list of insurance companies in order of performance for year 2010 is attached in Appendix B. The top fifteen companies accounted for seventy two percent of the underwritten premium in 2010 and thus formed the sampling frame. 3.4 Sample Size and Error The sample size (n 0 ) used in this research was twenty respondents. These were employees drawn from five randomly selected insurance companies in the sampling frame. The respondents were employees in internal audit and claims processing sections of the insurance companies. No survey can ever be deemed to be free from error or provide 100% surety. Error limits of less than 10% and confidence levels of higher than 90% can be regarded as acceptable (Hussey and Hussey, 1997, pp. 226). The confidence interval (z) corresponding to a 90% confidence level is Assuming that the 15 insurance companies in the sampling frame have on average a total of sixteen staff doing claims handling and audit, the population (N) = 15 * 16 = 240. p is the number of responses (n 0 ) expressed as a percentage of the population (N). This is calculated as (n 0 /N)*100 = 8.3%. The error rate is therefore calculated using the formula e² = z²p(1-p) - z²p(1-p) (Yamane, 1967, pp. 258). n 0 N This gives an error rate of 9.7% which is acceptable. 23

37 3.5 Research Design The research was applied, analytical and cross-sectional and was developed from a qualitative point of view. Applied research plans to solve practical problems of the modern world, rather than to acquire knowledge for knowledge's sake. It is geared towards improving the human condition. In our case the problem was the prevalence of motor insurance fraud in the Kenyan Insurance industry. Analytical research attempts to establish how it came to be. This is a necessary step in the finding of solutions to it. This research took the Mass Detection Tool (MDT) as input to evaluate the practicality of a fraud investigative and detective network. The strengths and weaknesses of the MDT were evaluated and used to propose a framework that could effectively be used to address motor vehicle insurance fraud in Kenya. Ethnographic studies of insurance fraud detection were incorporated to improve the MDT. These ultimately resulted in a framework by which insurance fraud in the Kenyan insurance industry can be curtailed through timely investigation and detection. A meta-analysis research design was used. The findings from similar studies documented in selected journal articles and books were integrated with findings from the Kenyan Insurance Industry. Being a correlational study, a meta-analysis design was chosen to allow for generalizations across studies and reveal useful patterns in the combined study data. 3.6 Data Collection Methods and Techniques Primary and secondary data sources were used for the purposes of this research. The primary data was collected through questionnaires administered to subject matter experts in the industry. These were drawn from five of the top fifteen insurance companies in gross written motor insurance premium for year Secondary data was collected from the Association of Kenya Insurers, the Insurance Regulatory Authority, the Association of British Insurers and the US Insurance Fraud Bureau. 24

38 Responses from professionals in the industry were analyzed alongside raw data collected from published studies. This was done to compare the effect of fraud detection capability in information systems on the actual number of fraud cases reported Questionnaires Questionnaires were chosen as the best instrument for this study because they provide an economical and convenient approach to data collection. This was necessary due to the time limitations. Questionnaires also provide anonymity of the respondents which was necessary since the subject matter is a sensitive topic. Leaked information could have serious ramifications. Additionally, since the responses needed to be gathered in a standardized way, questionnaires were more objective. Another factor that informed the choice of questionnaires is their ease of administration. They were sent online and responses received online which ensures there was no need to visit all respondents physically. Furthermore, the results of the questionnaires could be quickly and easily quantified by the researcher during analysis Secondary Sources Several secondary sources were consulted during the data collection process. Reports from the IRA, AKI, professional audit firms and international regulators and associations provided the basic secondary data. 3.7 Data Analysis A standard questionnaire was administered to respondents. Descriptive and inferential statistics were used to analyze data from the questionnaire survey. For questionnaire data, analysis began after the first few responses. The first four questionnaires were for purposes of piloting and determined the relevant and irrelevant areas. This provided feedback for refinement before it was administered to the 20 respondents 25