Claim Processing System. Jang-Hee Yoo and Byoung-Ho Kang. Articial Intelligence Division. Systems Engineering Research Institute

Transcription

1 A Hybrid Approach to Auto-Insurance Claim Processing System Jang-Hee Yoo and Byoung-Ho Kang Articial Intelligence Division Systems Engineering Research Institute P.O. Box 1, Yoosung, Taejeon, , Korea Jong-Uk Choi Dept. of Information Engineering Sangmyung Women's University 7 Hongji-Dong, Jongro-Gu, Seoul, , Korea Abstract In this paper, we describe the implementation of auto-insurance claim processing system by using neural network and fuzzy technique. The important factor of compensation rate calculation is determining accident responsibility rate between the injured and the assaulter. Generally, the determination of responsibility rate mainly depends on the expertise of auto-insurance experts and the accident report by the police. However, unconsistency between experts' opinions and unattainability to assure the allocation of certainty factor bring out various uncertainty problems. For resolving these uncertainty problems, we introduce the expert system which is able to compute the more precise compensation rate by applying neural network and fuzzy techniques based on the results of rule based system. 1 Introduction Even though the successful applications of the expert system to real world, there are so many problems to be solved for AI researchers in order to attain the goal: \understand the human intelligence and make the computer intelligent." For example, though uncertainty allocation process plays a important role for drawing the conclusions, uncertainty (probability/conrmation/evidential conrmation) is arbitrarily allocated in the development process of expert systems [2, 14], furthermore a rm way of assuring this process has not been provided. Auto-insurance claim process depended on unreliability of information, incompleteness of information, and imprecision of knowledge representation actually has much limited factors in using the existing uncertainty handling techniques. First of all, as the determination of responsibility rate is very serious problem between the injured and the assaulter, it is very dicult to determine the compensation rate by uncertainty factor relying on the subjective decision. Furthermore, we can not nd the auto-insurance claim processing type inference mechanism which can draw out one hypothesis from many evidences and can make inference by using the prior probability. So we draw a conclusion that Certainty-Factor Model [13] and Bayesian Model [9] are not useful and Dempster-Shafer Model [12] is not also suitable because of computational problem for handling uncertainty problems of auto-insurance claim domain. In this paper, we describe the determination of responsibility rate, the important factor of auto-insurance compensation rate, by using fuzzy database, rule-based system, and neural network learning mechanism. We also show the application of fuzzy and neural network approach to resolve the uncertainty problems on the human perception level. Finally, determination mechanism which uses fuzzy database and rule-based system in basic responsibility rate and tunes the outputs by neural networks trained expertise of human experts is also introduced.

2 2 Uncertainty Problem in Auto- Insurance Claim Processing In Republic of Korea, determination of compensation amount in car accidents heavily depends on the responsibility rates which determine the accident responsibility based on the police reports. Compensation amount of personal damage is determined based on labour disability, monthly income, calculation of actual daily income derived with consideration of the rate of labor disability, addition of medical treatment fees, and recuperation fees. The property damage determination depends on the accident data: angle, speed, location, type of cars, degree of crash, cargo damage, and the automobiles involved in the accidents. The police car accident report used as input data includes 6 parts: abstract, situation of accidents, behavior of the driver and the pedestrian, causes of the accident, the vehicle in the accident, and humans in accidents. The compensation rate is determined by using the abstract of report and causes of the accident, and the behavior of drivers and pedestrians, after that, nal compensation rate is determined in terms of accident situation data, career of drivers, and car checking up date. In this process, uncertainty problems are divided into two parts: rst, that is concerned with the reliability of input data itself and ambiguity of expression; and second, the information and the weight of information are varied by expertise of auto-insurance experts for adjusting the basic responsibility rate. We nd the rst problem can be solved by inference using fuzzy database [15, 16, 17]. For example, the overspeed violation is inferred by the degree how fast driver speeded along the street when the accident happened. The degree of speed can be judged by direction of the car, state of road, and skid marks. On the normal road, if the skid mark is 12 meter or 10 meter, the probability of overspeed might be 95 percent or 80 percent respectively. This inference mechanism was also used in determining of personal injury. If age of pedestrian is determined as such: the old as \above 55" and infants as \under 6", the compensation rate should be raised in the real world because of their disability. In the fuzzy inference mechanism, if age of pedestrian is \54" or \7", those pedestrian could be a \95 percent" old man or a \80 percent" infant. For this fuzzy data processing, fuzzy database was constructed by the expertise of autoinsurance experts, statistical data and scientic data. The second problem is that applicable inference mechanism is required for determining compensation rate which can change the weights in the various situations. As weights of input data are varied on the situation of accidents, learning mechanism was applied to change the weights for tunning the basic compensation rates. For example, if a person on a cross-way is run over by a car and if the trac sign on the passenger side is red (do not cross), the person is totally responsible for the accident. However, the basic rate of that type of accident is 50:50, that is, the passenger take responsibility of 50 percentage and car should pay 50 percentage for the personal damage. If the accident occurs at night, the compensation rate of car increases by 5 percentage because the car's low beam light is on at night and thus visible to the passengers. If the passenger was careful in crossing the road, he/she could avoid the accident. To the contrary, if the car's low-beam light was not on, this rule can not be applicable. The modication factors considered in this type of accident are various: accident occurs at main road (+5 to passenger), passenger was crossing just in front or passenger was backing to the other side because the trac sign was just changed (+10 to passenger); residential areas or shopping areas (-10 to passenger); and the passenger way is not separated from the driveway (-10 to passenger), and so on. Currently, the responsibility rate of each party involved in an accident is determined by the handbook of responsibility rate determination which was made by three Japanese juries of Tokyo local court no. 27 (trac accident court) and adopted by Korean courts and automobile insurance companies as reference. This book divides the car accidents into 4 groups: 40 vehicle vs. pedestrian accidents, 40 car vs. car accidents, 60 two wheeled vehicle vs. four vehicle accidents, and 30 bicycle vs. four wheeled vehicle accidents, totally 170 accidents. Of those types, the accidents between car vs. car requires experienced knowledge and complicated decision process, because the accident usually includes fatal injuries, even simultaneous death of several persons, and serious damages of cars and freights. Each accident are represented by tree structure diagram and detailed gures are illustrated, and those gures help the calcula-

3 Car Accident Accident Report Pedestrian vs. Car Car vs. Car Two-Wheeled Vehicle vs. Four- Wheeled Vehicle Bicycle vs. Four Wheeled Vehicle Property Damage Determination Routine Basic Rate Determination Routine Personal Damage Determination Routine Pedestrian vs. Car Estimation of Damaged Parts Monthly Income Calculation Labor Disability Calculation Pedestrian Opposite or Following the Traffic Flow On the Crosswalk Pedestrian Crossing the Road Pedestrian Lying on the Road Not on the Crosswalk The Accident by Backing Up Car Calculation of Property Damage with Consideration of Responsibility Rate Daily Income Calculated from Responsibility Rate Daily Income with Consideration of Labor Desability Medical Treatment Recuperation and Addition Fee On the Traffic Control Not on the Traffic Control On the No Crossing Road Nearby the Crosswalk On Nearby the Crossroad Etc. Basic Compensation and Explanation Figure 1: Expert Knowledge Structure tion of modication factors. Figure 1 shows the knowledge structure for determining the responsibility rate. However, this system does not suitable for reformed Korean trac law and trac system as this system was established on 1975 in Japan. This system also has the short coming that can not provide sucient scientic information. For circumventing these short comings, we use the leading cases of Korean auto-insurance companies and \Trac Accident Investigation Manual" [1] of Northwestern University, USA. 3 Inference Mechanism in Rulebased System The auto-insurance claim processing system at early stage was mainly divided into two sub-routines: the responsibility rate determination routine and the compensation rate determination routine. Figure 2 shows the architecture of auto-insurance claim processing system. In this process, McBride table is employed to calculate the amount of compensation for personal injuries. As a car is consisted of at least 1000 parts, property damage compensation rate determination needs huge information and knowledge in the various situations: damaged Figure 2: Auto-Insurance Claim Processing System parts, degree of damage, and so on. As this information can not be constructed with production rules, relation database is needed for storing this kind of information. The combinational explosion problem which occurs in constructing database and combining it with situation information still exists. On the other hand, according to locations, the accidents between car and car are again classied into four dierent types: intersection with trac light, intersection without trac light, urban drive way, and suburban drive way. In the case of accidents at intersection with trac light, main concern is to determine who violate the trac law, which is relatively easy with the data of trac signs of each party. Although 95 percentage of trac accidents of that type can be easily processed with obtained data of the trac signals, other 5 percentage of accidents are a little harder because of arguments of each party's lawfulness. Accidents at the intersection without trac light and 5 percentage of unsolvable cases at the intersection with trac light are processed using trac law principles: priority should be given to advanced car in the intersection, right-turning cars, cars on broader roads, slow speed cars, and right-hand cars. Accidents at the urban driveways such as in resident areas and shopping areas can be easily processed with applications of the general principles and verbal evidences.

4 Accidents at the suburban driveway usually include a heavy toll of lives and serious damage which is worth careful investigation from insurance company. The investigation of cases requires experienced knowledge in this eld, verbal evidences of observers and passengers, and very often scientic data analysis : car damage data, skid marks, etc. In the case of car vs. bicycle or car vs. motorcycle accidents, the duty of drivers is the same as those of the case of car vs. car accidents in itself. Consequently, the priority of each car in the case of two wheeled vehicles follows the principles of the case of car vs. car. Though there are so many opinions how to allocate the priority when the vehicle vs. human accidents happen, the principle that protect the pedestrian is generally accepted in Republic of Korea. In gure 2, the type of accidents is divided into the accident location and type (property or personal) based on the police reports, and the determination of basic responsibility rate is inferred by using fuzzy database to handle the uncertainty problems [15, 17]. Rule based system used to produce the basic responsibility rate and to classify the accidents is consisted of \fact base" and \rule base." Fact base denes the parameters used in each rule base. It also describes the query to gain the value about parameters, variable type, expected output, expected output range, number of rules for its parameters, and the rule name using the automatically generated parameters. Rule base includes rule name; operator set to represent logical connection of conditions; condition statements consisted of a parameter, an operator, and a value; and consequent statements consisted of a parameter, an operator, and a value. The system works by the menu driven approach interacted with users to gain new information. The input value is added with the expected value stored in the fact base of knowledge base or by the actual values within the expected range. The inference engine of rule-based system is consisted of knowledge base interpreter and working memory management module. Lisp likely knowledge base interpreter brings the le type knowledge base into fact memory and rule memory of working memory in order to use it in inference engine. Knowledge base scheduler provides control mechanism, checks up the rule loading conditions, loads the appropriate rules to working memory, and evaluates the rule based on the logical conjunction relationship. Inference algorithm uses the OP STACK for checking the conditions, evaluating the rules, and selecting the rules. OP STACK has the parentheses used in each rule and condition index. OP STACK is constructed when rule is loaded on working memory, `I' represents if statements, `C' represents THEN, and `D' represents DESCRIPTION respectively. The number in the parentheses represents the index in each condition, and updates as T/F by the determination of TRUE/FALSE at condition checking. Those numbers can be used as condition index for new parameter input. Table 1 shows the example that change of OP STACK for determining TRUE/FALSE after condition checking. Table 1: STACK State Change I(((T)&(1)&(T))&((3)j(T)))C((0)j(1))D() I(((T)&(T)&(T))&((3)j(T)))C((0)j(1))D() I(((0)&(1)&(2))&((3)j(4)))C((0)j(1))D() FAIL TRUE FALSE We found that ecient working memory management and ecient rule evaluation inference engine and fast inference are achieved by using OP STACK. 4 Neural Network Learning for Uncertainty Handling Neural network application to expert system eld is restricted in the elds of the distributed knowledge representation, the parallel pattern marching, and the automatic rule generation [4, 6, 7, 8, 10, 11]. Sohn [10] developed the production system which can work feedback inference and pattern matching by using BAM (Bidirectional Associative Memory) neural networks. Gallant [4] suggested the MACIE: expert system inference engine by using neural networks to learn the gained knowledge. Samad [11] suggested the applicable neural networks to rule based system without condition parts. FCM (Fuzzy Cognitive Map) developed by Kosko [7] is the hybrid system of fuzzy expert system and neural networks as well as is useful to handle the fuzzy data.

5 Decision-Making Level Accident Classifier Rule-Base Fuzzy DB Basic Compensation Rate Determination Accident Reports Learning Level Preprocessing Fuzzy DB Neural Nets Tuning Compensation Rate Final Compensation Rate Determination Figure 3: Final Compensation Rate Determination Neural network approach to auto-insurance claim processing can circumvent the problems of existing knowledge representation schemes which use the restricted and xed rule to changing situations. As the acquired knowledge is not attained by knowledge engineerers but from the public knowledge of a publications and the limited number of rule is constructed in terms of hierarchical classication structure; problem solving process is not exible to each situation [3, 5]. Furthermore, as the reliability of each information source is not obvious in processing auto-insurance claim processing system, the clearness of relationship of each information source and enhancement of certainty correctness of its source are required to develop the expert system. Generally, problem solving is attained by inferring the causes with experienced information in medical diagnoses, however, the conclusion of auto-insurance type problem is gained by staking the each sub-consequence based on the gained data. Accordingly, selective (elimination) search is mainly used to draw a conclusion by the hierarchical constructed classication in medical diagnoses [3, 13]. However, constructive approach which is able to draw a conclusion by collecting data is required in auto-insurance knowledge processing [14]. Figure 3 shows the architecture of nal compensation rate determination processing system. Whole system is mainly divided into two parts: decision making level which can decide the accident types and basic compensation rate and learning level which is able to produce nal compensation rate by modifying the basic rate by using the trained data. Neural networks uses back-propagation algorithm to determine the nal compensation rate by using accident report of the police, experienced knowledge of auto-insurance experts, and the case study. The training data is consisted of accident type, basic compensation rate at the decision making level, nal compensation rate by added input information, and auto-insurance experts. Though the traditional articial intelligence knowledge representation technique uses symbolic manipulation: logic, semantic nets, frame; knowledge representation with neural networks represents the knowledge by large oating numbers and connection weights and has the distributed activation structure. Consequently, appropriate normalization (real number) is required to train the expert knowledge in the form of symbols to neural networks. For this normalization, fuzzy database is employed. In the inferencing process, provided \30 year old" is replied to the question \how old the driver?" in terms of \young" fact; expect value 1120 range is dened to the symbol of \young" in fact base; and \30 year old" has the 0.8 membership in fuzzy database, symbol \young" can be mapped to value 0.8 in neural networks. On the other hand, in the two output node of neural network architecture, the one node outputs the add/subtract condition, another actually add/subtract the nal values. Accordingly, as fact base has the limited range in expect values, these can be mapped to fuzzy set by using membership function. The exible learning mechanism which is able to adjust weights in the various situations for tunning the input data. The nal compensation rates are calculated by combining the outputs and basic responsibility rate. The neural networks exam the relationship between information sources by producing the contribution weights. 5 Conclusions The rst version of auto-insurance claim processing system (1990) only produced basic compensation rates. Proposed system (1993) in this paper outdid old system in compensation tunning module. However, as the fuzzy

6 data set is so huge and human expertise is inconsistent, problems happened in fuzzy database construction and formalization of human expertise. For example, inconsistency of human expertise made oscillation in neural network learning procedure, and human experts in insurance companies raised the questions to fuzzy data set. To apply this system to real world's car accidents, consensus of knowledge engineers, researchers, insurance experts and the police should be preceded. References [1] Baker, J. S., Trac Accident Investigation Manual, 1st Ed., The Trac Institute in Northwestern University, [2] Chandrasekaran, B. and Michael, Tanner C., \Uncertainty Handling in Expert Systems: Uniform vs. Task- Specic Formalisms," in Kanal L. N., and Lemmer J. F. (Eds.), Uncertainty Handling in Articial Intelligence, Vol.1, Noth-Holland, pp.36-46, [3] Clancey, William J., \Heuristic Classication," Articial Intelligence, Vol.27, pp , [4] Gallant, S. I., \Connectionist Expert Systems," Communication of ACM, Vol.31, No.2, pp , [5] Hayes-Roth, Barbara, \Blackboard Architecture for Control," Articial Intelligence, Vol.26, pp , [6] Hsu, L. S., Teh, H., Chan, S. C., and Loe, K. F., \Fuzzy Logic in Connectionists Expert Systems," International Joint Conference on Neural Networks, Vol.2, pp , Jan [7] Kosko, Bart, \Adaptive Inference in Fuzzy Knowledge Network," IEEE International Conference on Neural Networks, Vol.II, pp , [8] Kwasny, Stan C. and Faisal, Kanaan A., \Rule-Based Training of Neural Networks," Expert Systems with Applications, Pergamon Press, Vol.2, No.1, pp.47-58, [9] Pearl, Judea, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers, [10] Sohn, Andrew, \Connectionist Production Systems in Local Representation," International Joint Conference on Neural Networks, Vol II, pp , Jan [11] Samad, T., \Towards Connectionist Rule based System," IEEE International Conference on Neural Networks, pp , [12] Shafer, Glenn, A Mathematical Theory of Evidence, Princeton University Press, [13] Shortlie, Edward H., and Buchanan, Bruce G., \A Model of Inexact Reasoning in Medicine," in Buchanan, Bruce G., and Shortlie, Edward H.(Eds.), Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, Addison-Wesley, pp , [14] Pao Yoh-Han, Adaptive Pattern Recognition and Neural Networks, Addison-Wesley, [15] Zadeh, Lofti A., \Fuzzy Sets," Information and Control, Vol.8, pp , [16] Zadeh, Lofti A., \Fuzzy Algorithm," Information and Control, Vol.12, pp , [17] Zadeh, Lofti A., \Fuzzy Logic," IEEE Computer, pp.83-93, April 1988.