Leveraging Machine Learning for Real-Time Fraud Detection in Insurance Claims

Thesis Abstract

Fraudulent insurance claims pose a significant challenge to the financial sustainability and operational efficiency of insurance companies worldwide, leading to substantial financial losses estimated at billions of dollars annually. The rapid increase in claim submissions, coupled with sophisticated deceitful strategies, necessitates the development of advanced, real-time detection mechanisms that can accurately identify fraudulent activities promptly to mitigate financial impact. This study aims to leverage machine learning techniques to create an effective, real-time fraud detection system specifically tailored for insurance claims processing. The primary objectives include evaluating existing machine learning models for fraud detection, developing a predictive model optimized for real-time application, and assessing its performance against traditional rule-based methods. The research adopts a quantitative, exploratory design, utilizing historical insurance claims data collected from a leading national insurance provider over a three-year period, comprising approximately 150,000 claims. The target population encompasses all claims submitted within the specified timeframe, with a stratified random sampling technique used to select a representative subset of 20,000 claims for model training and testing. Data collection instruments include structured claims data extracted from the company's database, encompassing variables such as claimant demographics, nature of the claim, claim amount, prior claim history, and fraud likelihood indicators. Data preprocessing involved normalization, feature engineering, and handling of missing values to ensure data quality. The study employs multiple machine learning algorithms, including Random Forest, Support Vector Machine (SVM), Gradient Boosting, and Neural Networks, to develop predictive models. These models are trained using 70% of the dataset, with their performance evaluated on the remaining 30% using metrics such as accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Advanced techniques such as cross-validation and ensemble modeling are applied to enhance model robustness. The research further applies the Theory of Anomaly Detection and the Fraud Triangle Theory to underpin the modeling process, guiding feature selection and interpretation of fraud indicators. The expected findings indicate that machine learning models, particularly Gradient Boosting and Neural Networks, demonstrate superior performance in identifying fraudulent claims in real-time scenarios, with anticipated accuracy exceeding 85% and significant improvements in precision and recall over traditional rule-based systems. The models are expected to effectively reduce false-positive rates and improve timely alerting mechanisms, thereby enabling insurers to act swiftly against suspected fraud. Additionally, feature importance analysis will reveal critical variables influencing fraud detection, contributing to the interpretability and refinement of the models. This research contributes to the existing body of knowledge by systematically evaluating and comparing multiple machine learning techniques within the context of real-time fraud detection in insurance claims, providing a replicable framework adaptable across different insurance domains. It advances theoretical understanding by integrating the Fraud Triangle and Anomaly Detection theories with practical predictive modeling, fostering a deeper comprehension of the drivers of fraudulent claims. The study offers practical insights and a prototype system that insurers can implement to enhance fraud detection capabilities, thereby reducing financial losses and increasing operational efficiency. The study concludes that machine learning-driven real-time fraud detection systems are viable and more effective than traditional approaches, emphasizing the importance of continuous model updating and feature refinement. Recommendations include adopting hybrid models that combine machine learning with expert rule-based systems, investing in ongoing data collection and model retraining processes, and promoting cross-sector collaboration for data sharing. Suggestions for future research involve exploring deep learning architectures, integrating unstructured data such as claim narratives and social media signals, and expanding the scope to include behavioral and psychological analysis of claimants to further enhance fraud detection accuracy.