Technical Notes
Authors:
Rishabh Aryan, B.Tech (Computer Science and Engineering), MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.
Amit Kumar Sahu, Assistant Professor, Department of Computer Science and Engineering, MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.
Corresponding Author: Rishabh Aryan, B.Tech (Computer Science and Engineering), MATS School of Engineering and Information Technology (MSEIT), MATS University, Raipur-493441, Chhattisgarh.
Email ID: rishabharyan07052004@gmail.com
Abstract:
The proliferation of fraudulent insurance claims poses a significant challenge to the insurance industry, leading to substantial financial losses and eroding trust among policyholders. This study delves into the realm of Fraudulent Insurance Claims Detection Using Machine Learning, a dynamic approach aimed at harnessing the power of artificial intelligence to mitigate this pervasive issue.
With the advent of advanced technologies and the availability of vast datasets, machine learning algorithms have emerged as a potent tool for automating the detection and prevention of fraudulent insurance claims. This research undertakes a comprehensive exploration of the subject, focusing on the
development and evaluation of machine learning models designed to distinguish genuine claims from
fraudulent ones.
The study encompasses various facets of the fraudulent claims detection process, including data preprocessing, feature engineering, model selection, and performance evaluation. Machine learning algorithms such as Random Forest, Support Vector Machine, Neural Networks, and Decision Trees are
scrutinized for their efficacy in identifying suspicious patterns and anomalies within insurance claims data. Natural Language Processing (NLP) techniques are also applied to textual information to extract valuable insights.
Results obtained from extensive experimentation with real-world insurance datasets reveal promising outcomes. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) statistics are employed to gauge the model performance, including metrics such as True Positives (TP),
True Negatives (TN), False Positives (FP), and False Negatives (FN).
Additionally, this research investigates the integration of deep learning and generative adversarial networks (GANs) to enhance the accuracy and robustness of fraudulent claims detection. Practical aspects such as model deployment through Application Programming Interfaces (APIs) and user-friendly Graphical User Interfaces (GUIs) are also considered for real-world implementation.
The findings from this study hold immense potential for the insurance industry, leading to improved fraud prevention and more efficient claims processing. Moreover, the research underscores the importance of proactive strategies in combating insurance fraud, offering a substantial return on investment (ROI) for insurers. By leveraging machine learning techniques and adhering to data-driven methodologies, the industry can better protect its financial stability while preserving the trust of policyholders.
Keywords -Insurance Fraud Detection, Machine Learning Algorithms, Deep Learning, Natural Language Processing (NLP), Anomaly Detection, ROC-AUC Evaluation.
Accepted: 04/11/2022
Published: 30/11/2022
Author(s) Retains the Copyrights of This Article
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