Design And Implementation Of Fraud Detection System In Computerized Insurance Firm
1.1 Background of Study
Fraud is committed in various fields such as insurance (Ormerod et al. 2010; Li et al. 2008; Atwood et al. 2006), credit card (Weston et al. 2008; Dal Pozzolo et al. 2014), telecommunications (Estevez, 2006), and financial communications (Kirkos et al. 2007; Kotsiantis et al. 2006; Holton, 2009). Insurance fraud is one of the most frequent types of fraud to undertake. This type of fraud can take place in many forms with the simple objective of gaining money (Almedia, 2009). One of these domains is car insurance in which fraudsters (policyholders) setting planned traffic accidents up and file fake insurance claims (e.g. inflating costs) to obtain an illicit benefit from their insurance policy (Ayuso et al. 2011). It has been reported that Almost 21% to 36% of auto-insurance claims contain elements of suspected fraud but only less than 3% of them are prosecuted (Nian et al. 2016).
There are two different types of fraud, including opportunistic, and professional fraud that the second type is committed by organized groups. Although the organized fraud is perpetrated fewer than the opportunistic insurance fraud, the majority of revenue outflow (financial losses) is due to these groups (a White paper, 2012). According to Bolton and Han (2002), fraud detection would be difficult due to many reasons. The first one is, involving high volume of data, which are constantly evolving. In reality, for processing these sets of data, the fast, the novel, and efficient algorithms are entailed. Moreover, in terms of cost, it is evident that undertaking a detailed analysis of all records is too much expensive. Here the issues of effectiveness enter; indeed, many legitimate records exist for every person that an effective method should detect fraudulent records correctly.
Traditional systems for fraud detection are only able to find fraudulent customers (opportunistic fraud), whereas more professional fraudsters will be overlooked (a white paper, Roberts, 2010). In other words, opportunistic fraud is a continuous issue for insurers, whereas the more remarkable challenge comes from professional fraud, and such organized groups of perpetrators impose the greatest cost upon insurers. Fraudulent groups are being arranged by fraudsters in order to employ different individuals for doing some works, and using the newest technologies to be at least one step in front of insurers. They know properly that insurers and law enforcements officials utilize what kind of tools, and information (Smallwood and Breading, 2011). Due to aforementioned reasons, it is imperative for insurance companies to consider relevant methods for finding organized fraud groups, and promulgating them in the future.
1.2 Statement of the Problem
The detection of insurance fraud has been seriously taken into account in recent years. Although this issue is seen more in practical and functional fields, it is considered in terms of the academic aspects due to its negative effect on insurance pricing and on efficiency of insurance industry. Despite the extensive utilization of data mining algorithms for sorting fraud out, according to (Phua et al. 2005), there are some complexities with regard to nature of data mining techniques that illustrate they might be inefficient in flagging fraudulent activities in future; firstly, a volume of data will fluctuate over time (doubtless that the volume of data will have boomed by near future). Secondly, the forms and styles of fraud are changing regularly. The third criticism is regarding introducing new patterns of suspicious activities during the near future like professional fraud that will have been generated. In the last decade, social networks play a prominent role in researches on detecting and deterring fraud in various areas such as credit card, Social online, financial trade, Internet Auction, health insurance, etc. (Eberle et al. 2010; Bindu and Thilagam, 2016; Yu et al. 2015; Chau et al. 2006; Akoglu et al. 2013; Chiu et al. 2014; Vlasselaer et al. 2015; Flynn, 2016). It is to this regard that the study desired to design and implement a fraud detection system in computerized insurance firm
1.3 Aim and Objectives of the Study
The main aim of the research work is to design and implement a fraud detection system in computerized insurance firm. Other specific objectives of the study are:
- To design a system that will be able to detect fraud in the insurance firm
- To determine the types of fraud in an insurance firm
- To design a system that will be able to compare previous data and the present data
- To aid in monitoring and controlling of fraudulent activities in an insurance firm
1.4 Research Questions
The study came up with research questions so as to ascertain the above stated objectives of the study. The research questions for the study are:
- Can the new system be able to detect fraud in an insurance firm?
- What are the types of fraud in an insurance firm?
- Can the new system be able to compare previous data and the present data?
- How can the new system aid in monitoring and controlling of fraudulent activities in an insurance firm?
1.5 Significance of Study
The study on design and implementation of fraud detection system in computerized insurance firm will be of immense benefit to the entire insurance firms in Nigeria and the computer science department in the sense that the study will educate the above subjects on the types of fraud in the insurance firms in Nigeria. The study will educate them how to design a fraud detection system. The study will also serve as a repository of information to other researchers that desire to carry out similar research on the above topic and to contribute to the body of the existing literature.
1.6 Scope of Study
The study on design and implementation of fraud detection system will focus on computerized insurance firms in Nigeria and also provides will developed software to automate the new system
1.7 Limitation of Study
Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet).Secondly the researcher will simultaneously engage in this study with other academic work. This consequently will cut down on the time devoted for the research work
1.8 Definition of Terms
Is defined as deception deliberately practiced with a view of gaining an unlawful or unfair advantage.
This is the manifestation of criminal act done either solely or in an organized manner or without associates or group with the purpose earning wealth or being rich through all illegal means.
This is an action that proceed enable a fraud to occur.
False billing fraud:
This occurs when a business or an individual receives a bill for a product whereby the representation of the product by the promoter was either false or misleading or whereby the product were either never ordered or received.
This refers generally to any type of fraud scheme that uses one or more components of the internet such as chartroom, e-mail, message boards or websites to present fraudulent transactions or to transmit the proceed of fraud into in financial institutions or to other connected with the scheme.
This is a measure of protection against intruders from gaining access to the documents and secretes information.
This is an in-depth and study of an existing system with regard to its producer in working out an improve system.
This is an investigation undertaking in order to discover new factors through planning and systematic collection, analysis and interpretation of data.
Direct personal observation:
Involves the means of watching people, events and obtaining a lot of first land information relating to the people concerned.
This is a means of which information needed are source directly by interviewing people whoa re going to sue the system
Is defined as the study of a system to access the feasibility of proposed information system and to determine how it should function.
The input refers to the data that is supplied to the system for it is to process and produce information.
The output for the system refers to the information that is gotten from the system.
Is defined as a description of a procedure which terminates with a result.
Is a diagrammatic representation of the sequence of steps involved in carrying out process.
Is defined as the process of locating and fixing error (know as bugs) in a computer program or hardware device.
Is defined as the representation the algorithms written for a particular task with the aid of high level language that help to translate a simple code to machine code and produce the minimum output needed.
This is an exercise that puts a machine, processor system through a series of actions to ascertain its current status or to verify its reliability to ask.
It is an important part of implementation process. It serves as a method of communication among the people responsible for developing.
This enable programmer to work modules in parallel and periodic testing and check on performance of the whole system allowed manageable growth in complexity without introducing untraceable bugs.
This is to change in hardware software and documentation or procedure to production system in order to correct error or anomalies, most new requirements, or improve efficiency.
This is identification and removal of errors or failures in the system.
This hooks or regular inspection or periodic monitoring of the system to detect fraud, as such invokes maintenance in such area so as to forestall bridge in the operation of the system.
5.0 Summary Conclusion and Recommendation
Ensemble combining classifiers were applied on IFD models that were designed in previous paper using an imbalance automobile insurance fraud detection dataset. The imbalance dataset problem was solved by a novel proposed technique “partitioning-undersampling”. The proposed models were evaluated according to their recall.
The model with the highest recall is IFDDT which was designed using decision tree base classifier, but the second, third and fourth best classifiers are; model using grading (SVM-DT) (IFD-G-SVM-DT) with recall 95.4%, model using stacking (ANN-DT) (IFD-S-ANN-DT) with recall 94.6% and the model using grading (SVM-ANN-DT) (IFD-G-SVM-ANN-DT) with recall 94.3%; which all were designed using ensemble classifiers.
These novel proposed models were applied on another imbalance dataset again the models with the highest recall were the ensemble combination of the three base-classifiers models. The best two models are the model using Grading (SVM-ANN-DT) with recall 78.4% and the model using Vote (SVM-ANN-DT) with recall 77.3%, this proves that ensemble combining classifiers produce powerful IFD models. The difference between those models was shown to be statistically significant difference by calculating their p-value
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