Automobile Insurance Fraud Research Articles

AUTOMOBILE INSURANCE FRAUD DETECTION USING ENSEMBLE LEARNING MODELS

Automobile insurance fraud is a universal problem that has negative effects on both insurance companies and policyholders. This research proposes a novel ensemble learning model to accurately detect potential fraudulent vehicle insurance claims. By leveraging advanced machine learning techniques and addressing the challenge of imbalanced data, our model aims to enhance fraud detection efficiency and reduce financial losses. Our approach combines a stacking ensemble learner with carefully selected base classifiers, meta-classifier and data pre-processing techniques. We evaluate the model's performance on a real-world dataset of insurance claims, demonstrating superior results compared to existing methods. Notably, our model achieves high accuracy, recall, precision and area under curves, ensuring comprehensive detection and minimizing false positives.

An Investigation of Machine Learning Applications in the Financial Fraud Detection

Financial fraud presents itself in various forms, and it often involves intricate financial transaction networks, making it challenging to detect the perpetrators and identify the characteristics of the fraud. In recent years, machine learning has gained widespread application within the financial sector. Therefore, various financial fraud detection models have been developed based on diverse machine learning methodologies. In the method part, this paper provides an overview of the machine learning process and then discusses the application of machine learning models in various financial fraud scenarios. Financial fraud in the insurance field has been further refined into automobile and medical insurance fraud. In automobile insurance fraud detection, some studies applied implicit Naive Bayes Model to analyze observable features and estimate hidden variables, and some studies used resampler to solve data imbalance and adopted 7 kinds of machine learning models for analysis. In health insurance fraud detection, many studies train medical data with multiple models, including AdaBoost, Logistic Regression and Support Vector Machine. In credit card fraud detection, studies use five algorithms, including random forest and decision tree et al., and some construct a model combining Decision Tree (DT) and Logistic Regression (LR). In bank fraud detection, some studies introduce Value-at-Risk to combine it with machine learning algorithms, and some studies propose a decentralized model training method based on federated learning. In the discussion section, this paper addresses the current limitations of the research, such as poor interpretability, uneven distribution of data sets, and issues related to customer privacy, and proposes corresponding solutions.

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.

Research on Detecting Auto Insurance Fraud

With the expansion of the insurance sector has come an increasing concern about insurance fraud. This issue has grown in prominence over time, necessitating the implementation of appropriate countermeasures. In response, this work conducts a thorough analysis of the available literature, with an emphasis on the incorporation of machine learning approaches for detecting fraudulent actions in the field of automobile insurance. By delving into the intricacies of the vehicle insurance fraud landscape, the research investigates the strategic deployment of expert systems and machine learning models to enhance fraud identification processes. Drawing from the wealth of high-quality auto insurance data spanning the last five years, this work explores the implementation of diverse machine learning algorithms, including Logistic Regression, Decision Tree, and Discriminative Analysis, in order to construct comprehensive predictive models. After the modeling was completed, comprehensive testing and analysis were conducted, and the results showed that the logistic regression model had the highest accuracy among these three models. Finally, the future research direction of automobile insurance fraud detection technology is discussed, and how to reasonably prevent automobile insurance fraud is proposed.

A Robust and Resourceful Automobile Insurance Fraud Detection with Multi-Stacked LSTM Network and Adaptive Synthetic Oversampling

A Robust and Resourceful Automobile Insurance Fraud Detection with Multi-Stacked LSTM Network and Adaptive Synthetic Oversampling

Robust and resourceful automobile insurance fraud detection with multi-stacked LSTM network and adaptive synthetic oversampling

Robust and resourceful automobile insurance fraud detection with multi-stacked LSTM network and adaptive synthetic oversampling

Prediction of automobile insurance fraud claims using machine learning

Automobile insurance fraud is a significant issue for insurance firms, causing financial losses and higher premiums for policyholders. This study aims to create a predictive model for accurately identifying potential vehicle insurance fraud claims. Understanding fraud detection processes and operationalizing information communication technology is crucial for implementing corrective actions, but personally reviewing insurance claims is time-consuming and costly. This study explored machine learning algorithms to detect fraudulent vehicle insurance claims. The research evaluated AdaBoost, XGboostNB, SVM, LR, DT, ANN, and RF. AdaBoost and XGBoost classifiers outperformed other models with 84.5% classification accuracy, while LR classifiers performed poorly with balanced and unbalanced data. The ANN classifier performed better with unbalanced data. Performance evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of the models. The results demonstrate the effectiveness of machine learning in distinguishing between genuine and fraudulent claims, providing insurance companies with a powerful tool to proactively combat fraud and improve their overall risk management strategies. The findings of this research contribute to the insurance industry’s efforts to enhance fraud detection systems, reduce financial losses, and offer more competitive insurance premiums to honest policyholders.

Discrimination of Insurance Fraud Based on Machine Learning

In the insurance industry, Insurance fraud is a common phenomenon. However, according to statistics, among all types of insurance, automobile insurance fraud is the high incidence time of Insurance fraud. Based on 39 characteristic variables in the insurance claims database, this paper completes data preprocessing through normalization and coding of Categorical variable; Then it analyzes the correlation between data characteristics and Insurance fraud; Then, the principal component analysis method is used to reduce dimensionality and extract features from multi-dimensional features; Finally, the SVM classifier is trained to effectively identify Insurance fraud. The research results show that the model is effective in identifying Insurance fraud, and can achieve 79% accurate discrimination.

On the Cost-efficiency of Automobile Insurance Fraud Detection Methods: A Meta-analysis

We suggest an improved cost savings calculation method in case of automobile insurance fraud detection. Moreover, we compare the cost saving ability of 77 fraud detection methods using heat maps and compare the cost saving ability of traditional statistics-based methods with that of machine-learning based methods, based on Romanian insurance data. The findings of this study clearly show that only a small percentage of the currently available automobile insurance fraud detection methods can be applied in a truly lucrative manner, and in general, machine-learning detection methods prove to be less cost-effective than the traditional econometric-statistic methods.

Traditional versus AI-Based Fraud Detection: Cost Efficiency in the Field of Automobile Insurance

Business practice and various industry reports all show that automobile insurance fraud is very common, which is why effective fraud detection is so important. In our study, we investigate whether today’s widespread AI-based fraud detection methods are more effective from a financial (cost-effectiveness) point of view than methods based on traditional statistical-econometric tools. Based on our results, we came to the unexpected conclusion that the current AI-based automobile insurance fraud detection methods tested on a real database found in the literature are less cost-effective than traditional statistical-econometric methods.

The value of cross-data set analysis for automobile insurance fraud detection

This study focuses on personal automobile policies underwritten. Its aim is to provide decision support and to apply new models with good predictive performance and high operational efficiency. We propose a new approach by constructing a score that evolves over the life of a claim. It consists of creating a score at the opening of a claim and another derived from the information of the first adjuster’s report. Natural language processing is also used on a textual variable relating to the description of the claim provided by the agency. The fraud score is estimated by using a gradient boosting machine (GBM) and a neural network. The results are interpreted using the local interpretable model-agnostic explanations (LIME). They show that fraud detection is improved when all the information and the textual variable are included. Furthermore, we observe that the GBM method overperforms the neural network approach.

Automobile insurance fraud detection in the age of big data – a systematic and comprehensive literature review

PurposeThe purpose of this paper is to survey the automobile insurance fraud detection literature in the past 31 years (1990–2021) and present a research agenda that addresses the challenges and opportunities artificial intelligence and machine learning bring to car insurance fraud detection.Design/methodology/approachContent analysis methodology is used to analyze 46 peer-reviewed academic papers from 31 journals plus eight conference proceedings to identify their research themes and detect trends and changes in the automobile insurance fraud detection literature according to content characteristics.FindingsThis study found that automobile insurance fraud detection is going through a transformation, where traditional statistics-based detection methods are replaced by data mining- and artificial intelligence-based approaches. In this study, it was also noticed that cost-sensitive and hybrid approaches are the up-and-coming avenues for further research.Practical implicationsThis paper’s findings not only highlight the rise and benefits of data mining- and artificial intelligence-based automobile insurance fraud detection but also highlight the deficiencies observable in this field such as the lack of cost-sensitive approaches or the absence of reliable data sets.Originality/valueThis paper offers greater insight into how artificial intelligence and data mining challenges traditional automobile insurance fraud detection models and addresses the need to develop new cost-sensitive fraud detection methods that identify new real-world data sets.

Research on automobile insurance fraud identification based on fuzzy association rules

With the development of automobile insurance industry, how to identify automobile insurance fraud from massive data becomes particularly important. The purpose of this paper is to improve automobile insurance fraud management and explore the application of data mining technology in automobile insurance fraud identification. To this aim, an Apriori algorithm based on simulated annealing genetic fuzzy C-means (SAGFCM-Apriori) have been proposed. The SAGFCM-Apriori algorithm combines fuzzy theory with association rule mining, expanding the application scope of the Apriori algorithm. Considering that the clustering center of the traditional fuzzy C-means (FCM) algorithm is easy to fall into local optimal, the simulated annealing genetic (SAG) algorithm is used to optimize it. The SAG algorithm optimized FCM (SAGFCM) is used to generate fuzzy membership degrees and introduces fuzzy data into the Apriori algorithm. The Apriori algorithm is improved by reducing the rule mining time when acquiring rules. The results of empirical studies on several data sets demonstrate that the optimization of FCM by SAG can effectively avoid the local optimal problem, improve the accuracy of clustering, and enable SAGFCM-Apriori to obtain better fuzzy data during data preprocessing. Moreover, the proposed algorithm can reduce the mining time of association rules and improve mining efficiency. Finally, the SAGFCM-Apriori algorithm is applied to the scene of automobile insurance fraud identification, and the automobile insurance fraud data is mined to obtain fuzzy association rules that can identify fraud claims.

EDR을 활용한 자동차 보험사기 의심 사례 분석

According to the recent press release from the Financial Supervisory Service, automobile insurance fraud is increasing every year. The government tried to respond to insurance fraud by enforcing the Special Act on Prevention of Insurance Fraud. However, the size of insurance fraud is increasing due to intelligent and organized insurance scammers. In order to respond to insurance fraud, the vehicle driving information prior to a collision must be objectively verified by the vehicle accident analyst. EDR is one of the functions of ACU to record the event data when crash occurred. EDR data should be verified whether the data is recorded from the specified situation by using PDOF result, airbag deployment status and ignition cycle. In this study, one of the vehicle insurance fraud case was covered. In that accident case, the driver claimed that the vehicle could not be operated due to out of control. In the EDR data, the driver did not manipulate the vehicle event data though it was in a dangerous situation. So, when the EDR data was recorded and could be download from a damaged vehicle, the driver's operation information can be verified.

Automobile insurance fraud detection

The risk of incurring financial losses from fraudulent claims is an issue concerning all insurance companies. The detection of such claims is not an easy task. Moreover, a number of old-school methods have proven to be inefficient. Statistical techniques for predictive modelling have been applied to detect fraudulent claims. In this article, we compare two techniques: Artificial neural networks and the Naïve Bayes classifier. The theory underpinning both techniques is discussed and an application of these techniques to a dataset of labelled automobile insurance claims is then presented. Fraudulent claims only constitute a small percentage of the total number of claims. As a result, datasets tend to be unbalanced. This in turn causes a number of problems. To overcome such issues, techniques which deal with unbalanced datasets are also discussed. The suitability of Neural Networks and the Naïve Bayes classifier to the dataset is discussed and the results are compared and contrasted by using a number of performance measures including ROC curves, Accuracy, AUC, Precision, and Sensitivity. Both classification techniques gave comparable results with the Neural network giving slightly better results than the Naïve Bayes classifier on the training dataset. However, when applied to the test data, the Naïve Bayes classifier slightly outperformed the artificial neural network.

Insurance Fraud Detection using Spiking Neural Network along with NormAD Algorithm

General automobile insurance in recent years, has seen a huge escalation of fraud cases. The requirement of utilizing well organised and coherent technique to check on or determine user those are potential frauds. Thus, the deployment of the NormAD algorithm with less delay to enhance the safety and authorized in the operative process. The paper here describes attribute extrication method and Spiking Neural Network structure to resolve the issue of identification of automobile insurance fraud. The attribute second-level extrication algorithm coined in this paper can efficiently derive key attributes and enhance the identification accuracy of succeeding algorithms. So as to achieve to resolve the issue of unstable simulation allotment in the automobile insurance fraud identification scheme, an exemplary distributed method established on the plan of small unit proportion balance is presented. Formulated on the above techniques of attributes extrication and sample division, a model established on Spiking Neural Network with NormAD Algorithm is proposed. This method utilizes the complete goal of implementation of the Spiking Neural Network model algorithm that rely on Spiking Neuron, and ultimately accomplishes in enhancing the exactness of the detection of Automobile Insurance Fraud.

TGANs with Machine Learning Models in Automobile Insurance Fraud Detection and Comparative Study with Other Data Imbalance Techniques

A data-driven Fraud detection model for insurance business can be seen as a two-phase method. Phase I is data-preprocessing of a given dataset, in which, handling class imbalance is a major challenge. Phase II is that of classification using Machine Learning models. It is important to comprehend if there is any influence of the technique used in Phase I on the efficiency of the model used for Phase II. A natural query that intrigues one is whether there is a golden combination of a technique in Phase I and a specific model in Phase II for assured best performance of a Fraud Detection Model.In this work, we study a few techniques for handling data imbalance issue namely, SMOTE, MWMOTE, ADASYN and TGAN in combination with various classifier models like Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), LightGBM, XGBoost and Gradient Boosting Machines (GBM). The study is conducted on a dataset for motor vehicle insurance fraud detection.We present a comparison of various combinations of data imbalance technique and classifier models. It is observed that the combination of TGAN in Phase I and GBM in Phase II gives the best performance. This combination performs best in terms of important metrics such as false positive rate, precision and specificity. We obtained the lowest false positive rate of 0.0011 and precision of 0.9988 which minimizes the most critical risk for the insurance company of falsely classifying a non-fraud claim as a fraud. Finally, the specificity of 0.9989 indicates that the model was also very good at predicting the non-fraudulent claim.

Poisson item count techniques with noncompliance.

The Poisson item count technique (PICT) is a survey method that was recently developed to elicit respondents' truthful answers to sensitive questions. It simplifies the well-known item count technique (ICT) by replacing a list of independent innocuous questions in known proportions with a single innocuous counting question. However, ICT and PICT both rely on the strong "no design effect assumption" (ie, respondents give the same answers to the innocuous items regardless of the absence or presence of the sensitive item in the list) and "no liar" (ie, all respondents give truthful answers) assumptions. To address the problem of self-protective behavior and provide more reliable analyses, we introduced a noncompliance parameter into the existing PICT. Based on the survey design of PICT, we considered more practical model assumptions and developed the corresponding statistical inferences. Simulation studies were conducted to evaluate the performance of our method. Finally, a real example of automobile insurance fraud was used to demonstrate our method.

Two-Stage Automobile Insurance Fraud Detection by Using Optimized Fuzzy C-Means Clustering and Supervised Learning

A novel two-stage automobile insurance fraud detection system is proposed that initially extracts a test set from the original imbalanced insurance dataset. A genetic algorithm based optimized fuzzy c-means clustering is then applied on the remaining data set for undersampling the majority samples by eliminating the outliers among them. Thereafter, the detection of the fraudulent claims occurs in two stages. In the first stage, each insurance record is passed to the clustering module that identifies the claim as genuine, malicious, or suspicious. The genuine and malicious samples are removed and only the suspicious instances are further scrutinized in the second stage by four trained supervised classifiers − Decision Tree, Support Vector Machine, Group Method for Data Handling and Multi-Layer Perceptron individually for final decision making. Extensive experiments and comparative analysis with another recent approach using a real-world automobile insurance dataset justifies the effectiveness of the proposed system.

Fast Unsupervised Automobile Insurance Fraud Detection Based on Spectral Ranking of Anomalies

Collecting insurance fraud samples is costly and if performed manually is very time consuming. This issue suggests the usage of unsupervised models for fraud data collection. One of the accurate methods in this regards is Spectral Ranking of Anomalies (SRA) that is shown to work better than other methods for auto-insurance fraud detection, specifically. However, this approach is not scalable to large samples and is not appropriate for online fraud detection. This is while, real-time fraud management systems are necessary to prevent huge losses. In this study, we propose an implementation methodology which makes it possible to apply the SRA to big data senarios. We exploit the power of spectral ranking of anomalies to create an estimated target variable from the unlabeled dataset. We then use two robust models, namely, random forest and deep neural networks to fit a model based on the estimated labeled training set. Next, the incoming live data are fed to the mentioned trained models for predicting the target variable. Simulation results confirm that the proposed approach has higher speed and acceptable false alarm rate compared to existing related methods.