Predicting automobile insurance fraud using classical and machine learning models

Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases

Automobile Insurance Fraud Detection Using Data Mining: A Systematic Literature Review

Insurance is a pivotal element in modern society, but insurers face a persistent challenge from fraudulent behaviour performed by policyholders. This behaviour could be detrimental to both insurance companies and their honest customers, but the intricate nature of insurance fraud severely complicates its efficient, automated detection. This study surveys fifty recent publications on automobile insurance fraud detection, published between January 2019 and March 2023, and presents both the most commonly used data sets and methods for resampling and detection, as well as interesting, novel approaches. The study adopts the highly-cited Systematic Literature Review (SLR) methodology for software engineering research proposed by Kitchenham and Charters and collected studies from four online databases. The findings indicate limited public availability of automobile insurance fraud data sets. In terms of detection methods, the prevailing approach involves supervised machine learning methods that utilise structured, intrinsic features of claims or policies and that lack consideration of an example-dependent cost of misclassification. However, alternative techniques are also explored, including the use of graph-based methods, unstructured textual data, and cost-sensitive classifiers. The most common resampling approach was found to be oversampling. This SLR has identified commonly used methods in recent automobile insurance fraud detection research, and interesting directions for future research. It adds value over a related review by also including studies published from 2021 onward, and by detailing the used methodology. Limitations of this SLR include its restriction to a small number of considered publication years and limited validation of choices made during the process

Blending big data analytics : review on challenges and a recent study

With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this paper, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V's. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future. © 2013 IEEE

Neural network algorithms for fraud detection: a comparison of the complementary techniques in the last five years

Purpose: The purpose of this research is to analyse the complementary updates and techniques in the optimization of the results of neural network algorithms (NNA) in order to detect financial fraud, providing a comparison of the trend, addressed field and efficiency of the models developed in current research. Design/Methodology/Approach: The author performed a qualitative study where a compilation and selection of literature was carried out, in terms of defining the conceptual analysis, database and search strategy, consequently selecting 32 documents. Subsequently, the comparative analysis was carried out, in turn being able to determine the most used and efficient complementary technique in the last five years. Findings: The results of the comparative analysis depicted that in 2019 there was a greater impact of research based on NNA with 11 studies. 27 complementary updates and techniques were identified related to NNA, where deep neural network algorithms (DNN), convolutional neural network (CNN) and SMOTE neural network. Finally, the evaluation of effectiveness in the collected techniques achieved an average accuracy ranging between 79% and 98.74% with an overall accuracy value of 91.32%. Originality/Value: Being a technique which is applied and compared in diverse studies, ANNs uses a wide range of mechanisms concerning training and classification of data. According to the findings of this research, the complementary techniques contribute to the progress and optimization of algorithms regarding financial fraud detection, having a high degree of effectiveness concerning on-line and credit card fraud

Improved adaptive genetic algorithm for the vehicle insurance fraud identification model based on a BP neural network

With the development of the insurance industry, insurance fraud is increasing rapidly. The existence of insurance fraud considerably hinders the development of the insurance industry. Fraud identification has become the most important part of insurance fraud research. In this paper, an improved adaptive genetic algorithm (NAGA) combined with a BP neural network (BP neural network) is proposed to optimize the initial weight of BP neural networks to overcome their shortcomings, such as ease of falling into local minima, slow convergence rates and sample dependence. Finally, the historical automobile insurance claim data of an insurance company are taken as a sample. The NAGA-BP neural network model was used for simulation and prediction. The empirical results show that the improved genetic algorithm is more advanced than the traditional genetic algorithm in terms of convergence speed and prediction accuracy

How Fair Is IS Research?

While both information systems and machine learning are not neutral, the identification of discrimination is more difficult if a system learns from data and discrimination can be introduced at several stages. Therefore, this article investigates if IS Research has taken up with this topic. A literature analysis is conducted and its discussion shows that technology, organization, and human aspects have to be considered, making it a topic not only for data scientist or computer scientist, but for information systems researchers as well

Artificial Intelligence Adoption in Criminal Incestigations: Challenges and Opportunities for Research

Artificial Intelligence (AI) offers the potential to transform organisational decision-making and knowledge-sharing processes that support criminal investigations. Yet, there is still limited evidence-based knowledge concerning the successful use of AI for criminal investigations in literature. This paper identifies the main areas and current dynamics of the adoption of AI in criminal investigations using bibliometric analysis. We synthesise existing research by identifying key themes researchers have delved into on AI in criminal investigations. The themes include crime prediction and human-centred issues relating to AI use in criminal investigations. Finally, the paper elaborates on the challenges that may influence AI adoption in criminal investigations by police professionals. These challenges include possible laggard effects with AI adoption, implementation challenges, lack of government oversight, and a skills gap