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AI-Powered Fraud Detection in Insurance and Banking

Author(s) Kranthi Kumar Asike Parameshwa
Country United States
Abstract This paper focuses on Artificial Intelligence (AI) to detect fraud in the banking and insurance industry. Due to the rapid development of online financial services, fraud cases, including identity theft, credit card fraud, and fake insurance claims are becoming smarter. The old rule-based systems are no longer adequate to identify the changing trends in fraud and there is need to have more dynamic and modernized solutions. Machine learning, deep learning, and data analytics are AI-based methods that assist in detecting and preventing fraud with considerable improvements. The paper discusses the different AI models that are applied in the detection of fraud and they include supervised and unsupervised learning algorithm, anomaly detection and neural networks. These methods allow to track in real time transaction tracking, detect suspicious patterns, and decrease false positives. AI has been popular in the banking industry in detection of credit card frauds, anti-money laundering, and identity checks. AI is used in the insurance sector to detect customer frauds, customer behavior, and to enhance the risk assessment procedure. Although it has benefits, AI-based fraud detection has issues including data privacy, model’s interpretability and quality of dataset adversities. The research outlines these weaknesses in the context of explaining the possible future trends, such as explainable AI and the incorporation of new technologies, such as blockchain. In general, AI is essential in improving the efficacy, precision, as well as dependability of fraud detection mechanisms in contemporary fiscal conditions.
Keywords Artificial Intelligence (AI), Fraud Detection, Machine Learning (ML), Deep Learning (DL), Banking Security, Insurance Fraud, Anomaly Detection, Data Analytics, Financial Crime, Risk Assessment
Field Sociology
Published In Volume 7, Issue 5, May 2026
Published On 2026-05-10
DOI https://doi.org/10.70528/IJLRP.v7.i5.2152
Short DOI https://doi.org/hb379x
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