The components in the proposed framework affect both the business-side and the technological side of the insurance underwriters. The framework's components therefore require both business-side and technology expert involvement to assemble and execute. The steps to execute the framework are summarized in figure 6.2
Figure 6.2 Visual Presentation on Executing the Framework
To execute the framework, individual insurance companies need to begin by building the social context. This involves enhancing underwriting processes to collect clients’
ethnographic data. The ethnographic data that should be collected and / or used with the framework is given in Appendix C. This may be amended to fine tune the framework as it is used.
The lists of fraud indicators used by claims handling staff then need to be harmonized to eliminate overlap, redundancy and / or inconsistencies. These provide the second input to the claims handling process against which to check incoming claims. The list of fraud indicators for this framework is provided in Appendix C. Simply totaling up the number of fraud indicators would be highly inefficient since the approach weights all indicators equally. Certain indicators or combinations of indicators might be particularly important and should be weighted more heavily hence the weightings.
The process is linked to the common industry database to leverage on the experience of other players. The system will provide a statistical hub to the industry through the information sharing. This will ease the information burden on members as they will have valuable information at hand. The statistics would fall in six categories namely;
stolen motor vehicle data, salvage data, written-off motor vehicles, uninsured motor vehicles, accidental claims and underwriting data.
The ethnographic data, organizational fraud indicators and common industry database are interconnected and information is shared between them.
Finally, the claims handling staff experience will be leveraged. This step is specific to individual companies and provides the competitive differentiation. Aberrant claims from the suspicion building process at this point are passed to claim handlers and investigators for the final decision. Aberrant claims are those exceeding a particular threshold in the fraud risk assessment score. Results of their decision feed back to the fraud indicators and ethnographic data to continually improve the framework. Those below the threshold are paid and archived.
CHAPTER 7
7.0. DISCUSSION, CONCLUSION AND RECOMMENDATIONS 7.1 Discussion
This research has looked at various aspects of insurance fraud detection and investigation. A framework that brings together a number of fraud detection approaches has been formulated. Information from published studies has been related to data collected from five local insurance companies. The results have been used to analyze the extent to which each of these approaches can contribute towards insurance fraud detection and investigation.
The research has shown that by using ethnography to supplement the already available fraud indicator rule sets and industry experience, an insurance company can improve fraud detection considerably. Additionally, the soon to be available common industry database information will drive gains from the framework
From the analysis done on the twenty respondents sampled across the five companies, one or more of the components of the framework was used to identify fraud. The efforts were however disjointed leading to fraud detection being done by chance as opposed to deliberately.
All insurance companies underwriting motor insurance have incurred costs related to fraud. There is unanimity with regard to the gains of a common database and the fact that customer facing claims handlers with experience provide a valuable fraud detection avenue.
The researchers found out that the person based detection and investigation approaches have been successful in nabbing majority of fraudulent claims. Only three out of twenty responses showed success in the use of technology without the human aspect.
7.2 Conclusion
The Kenyan Insurance Industry has been hit by massive incidences of motor insurance claims fraud. Owing to the material nature of the losses encountered due to fraudulent motor claims, the industry is greatly concerned with the detection of fraudulent behavior. Insurance fraud detection and investigation, particularly in Kenya, is still a maturing discipline.
Research findings show that there is a need for a formal and systematic approach to detecting and investigating motor insurance claims fraud in the insurance industry in Kenya. It is of paramount importance for insurance companies to have an integrated approach instead of the various disjointed methods used to detect and investigate fraud.
Current theory and studies of motor claims fraud provide a basis for growth of the fraud detection and investigation discipline. By developing and extending current theory to incorporate the requirements identified in the research, an integrated framework that combines both theory and current practice has been developed. This framework can be applied in practice and leverage on the benefits both in literature and current practice. The framework leads to improved motor claims fraud detection principally for insurance companies in Kenya.
The results of this research will be of importance to the top management of the Association of Kenyan Insurers (AKI) and affiliate insurance companies. The results give AKI a basis by which they can implement the Integrated Motor Insurance Data System (IMIDS). The framework offers this by providing for an industry-wide database for the storage and sharing of critical fraud information. This database is in essence the IMIDS system currently under development by AKI and these results are useful in eliciting the data requirements for storage in the IMIDS.
However colorful this may appear on paper, care should be taken to ensure proper implementation. The framework adds no value if not properly implemented, continuously improved and assessed to determine if the industry is deriving any value from it.
7.3 Recommendations
This study research has clearly indicated that the insurance industry in Kenya has a lot to do in the area of motor claims fraud detection. The Association of Kenya Insurers has taken a great milestone in terms of initiating a project to make information sharing within the industry a reality. This is a foundation step towards collaborative fighting against insurance fraud among other benefits. This can further be conceptualized by designing appropriate rules of engagement with regard to use of the database. The AKI’s efforts will be futile if, in the name of competition, insurance companies hold back critical information about fraud and fraud scams that have hit them.
7.4 Directions for Future Research
The full implementation of the proposed insurance fraud detection framework and evaluation of how well the framework’s implementation has been done is the subject of another research. The implementation involves a collaborative effort by the insurance companies possibly under the auspices of AKI. The assessment of the framework’s success can be done by individual insurance companies, by simply looking at their end year records. By so doing, it will be possible to establish whether or not the industry has in practice managed to curtail the problem of motor claims fraud.
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APPENDICES
Appendix A – Research Questionnaire
Section A: Demographic Information
A.1) Name (Optional) ________________________________________________
A.2) Company (Optional) _____________________________________________
A.3) Job Title ________________________________________________________
A.4) How long have you worked within the insurance industry in Kenya?
No. of Years Please Tick Appropriately Between 1 – 5
Between 6 – 10 Between 11 – 15 Between 16 – 20 Over 21
Section B: Information and Data Collection
Please consider the questions below and select/check all of the appropriate responses:
B.1) What forms of insurance fraud are MOST prevalent in the Kenyan insurance industry? (List three)
B.2) How is fraudulent activity detected by your business? Tick all appropriate responses.
Response
Reporting by the public, clients, customers, business associates Detection by staff other than internal investigators
Use of internal investigators Use of private investigators Use of detection technology Internal / External audit
Other, please specify
B.3) Which of the following measures does your business have in place to prevent the occurrence of motor claims fraud? (Tick all appropriate)
Response Training and awareness programs among employees
Public stance on intolerance for fraud Securing of electronic databases
Use of public information on fraud scams, fraud prevention, etc.
Background checks on clients using available databases
On-going trend analysis of reported or suspected incidents of fraud Other, please specify
B.4) Does your business pursue cases of fraud in civil court? Response Yes
No
Other, please specify
B.5) How has technology been used to facilitate the detection and investigation of Motor insurance claim fraud in the Kenyan insurance industry? (Please specify)
B.6) How can technology facilitate the detection and investigation of Motor insurance claim fraud in the Kenyan insurance industry going forward? (Please specify)
Please consider the statements below and select one of the following responses as appropriate:
B.7) Assessment of the industry YES NO
Has your company incurred financial costs as a result of motor claims fraud?
Does your company have sufficient mechanisms to detect and prevent motor insurance claims fraud?
Is there sufficient government help in the fight against insurance fraud?
Are industry players willing to join forces to combat this problem?
Does your company have an annual budget to address motor claims fraud?
Has technology been useful in addressing motor claims fraud?
NB: Please note that you may give your answers to any of the questions on a separate piece of paper in case the space provided is inadequate for your response.
Thank you for taking time to participate in this survey.
THE END
Appendix B – Gross Written Motor Insurance Premium for Year 2010 (Kshs)
Appendix C – Ethnographic Information
Client
Previous claims history: A recent small claim followed by a larger claim can be evidence of toe dipping
Financial background: Higher up the ladder the bigger the fraud
Mannerisms: People are not always good on the phone and are not necessarily trying to be obstructive
Personal characteristics: Older people don’t drive high value fast cars
Suspicious behavior: He has sent keys and documents in straightaway - you wouldn’t know to do this
Mismatch from insured’s statements: Time of accident originally stated as 9.30 then changed to 9.25
Appears too genuine “Look out for straightforward circumstance, for example, where the insured has gone into the back of the third party
Incident
Damage/injury does not match reported incident: Engineer says can’t be vandalism as bumper could not have been pulled off by a person
No witnesses or independent evidence Staged accidents ...no witnesses, car immediately taken from the scene, no police involvement
Surprising coincidence: Two claims from the same company on the same date both with excessive windscreen claims
Time of day: Staged accidents occur early morning, late evening
Third party (TP)
Insured/TP relationship: A family member who was interviewed admitted TP knew the insured
Missing TP data: Suspicious of the fact that the insured says they hit TP yet no TP details on claim form at all
Claims history of TP: The passengers duplicated the previous claims
TP claim mismatches: Incident description in TP claim do not match client’s claim
Vehicle
Insufficient information: No information about vehicle color
Mismatching information: The vehicle is a different color to that stated on policy Sale vehicle: Someone just trying to dispose the car because it is too old and attracts no buyers
Missing component: Steering wheel missing when engineer inspected vehicle; could be it was a funky steering wheel and it’s a cherished car and he’s taken it off for himself Unrecovered/burnt out: Vehicle not available for inspection
Value claimed too high/low: Discrepancy between the insured’s valuation and the price of the vehicle
Claims handling
Dubious claims handling parties: A dodgy lawyer has been identified in the claim Excessive claim: Disparity between reserves put on claim for personal injuries and the number of passengers
Mismatch from engineer's documentation: Originally claimed for 6 vehicles set fire to but there are now 7 engineers reports
Timing anomalies relating to notification: Hire period of vehicle starts two days after accident
Policy
Excessive cover: It is suspicious when premium for comprehensive cover is more than the value of the vehicle
General
General: Belief that the insured is trying to get one over the wicked insurance company
Appendix D – List of Fraud Indicators
Fraud Indicator Weighting
Loss occurs within one month of issue or expiration of the policy 5
Loss occurs after cancellation notice was sent to insured 5
No Police abstract 5
License plate does not match vehicle and/or is not registered to insured 5 Repair bills are consecutively numbered or bear weekend and holiday dates 5 Claimant offering to accept a reduced settlement whatever the circumstances 5 Disputed number of occupants in the vehicle at the time of the accident. 5
Vehicle has an incorrect log book details 5
Coverage was recently increased 4
Police abstract dated days after loss or accident 4
Damages to vehicle are so minor as to make the likelihood of injury 4
No police were called to the scene. 4
Bad history of accidents within a short period of time. 4
Claimant has hired a lawyer immediately. 4
Driver's report differs from claimant's report 4
Claimant demanding quick settlement 3
Claim is made after one week or more of theft or accident 3
Insured excessively eager to accept blame 3
Claimant unusually familiar with insurance terminology and procedures. 3
Typical accident description, almost perfect. 3
The other driver disputes that any accident actually occurred. 3
Delay between the time of the accident and the date of treatment for injuries 3
Expired driver's license 3
Vehicle is customized, classic, and/or antique 3
Purchase price was exceptionally high or low 3
Vehicle has theft and/or salvage history 3
Vehicle was previously involved in a major collision 3
Neighbors, friends, and family are not aware of loss 2
Claims to be self-employed but is vague about the business and actual
responsibilities 2
Claimant has recent or current marital and/or financial problems 2
Claiming expensive contents in vehicle at time of left 2
Displayed "for sale" signs prior to theft 2
Claimant called to make inquiry prior to the loss 2
Police did not visit the accident site 2
Pushy claimant 1
Claimant is unemployed 1
Vehicle has a history of mechanical problems 1
Vehicle has low resale value 1
Claimant hand delivered documents relevant to the claim 1
No third party 1