Leveraging advanced analytics and machine learning for effective insurance fraud detection | |
| Author | Dasgupta, Paramik |
| Call Number | AIT RSPR no.DSAI-23-06 |
| Subject(s) | Machine learning Insurance fraud--Data processing |
| Note | A research study submitted in partial fulfillment of the requirements for the degree of Master of Science in Data Science and Artificial Intelligence, School of Environment, Resources and Development |
| Publisher | Asian Institute of Technology |
| Series Statement | Research studies project report ; no. DSAI-23-06 |
| Abstract | The automobile insurance sector is essential to protecting people and businesses from the financial fallout from auto accidents. The increasing number of insurance claims has made effective fraud detection and intelligent data management essential for optimizing operations and maintaining financial sustainability. The goal of this project is to bring in a new era of claims management and risk assessment for the auto insurance industry by utilizing the power of Machine Learning (ML) algorithms and dynamic dashboarding. To ensure that the data is of the highest caliber and ready for machine learning analysis, the study carefully gathers, preprocesses, and examines it. Fundamentally, the study uses sophisticated machine learning algorithms, such as Random Forest, XGBoost, and Logistic Regression, to predict insurance fraud outcomes exceptionally accurately. These algorithms, which are assessed using performance metrics like accuracy, precision, recall, F1-score, and mean absolute error (MAE), allow insurance providers to make quick, data-driven decisions on fraud outcomes. These predictive models along with Power BI to produce a real-time dashboard that displays critical Key Performance Indicators (KPIs) for the auto insurance industry. This increases the ability of insurance companies to monitor performance, expedite the processing of claims, and reduce risks while also enabling business analysts to make quick, well-informed decisions. In conclusion, this study offers a more effective and bright future for the auto insurance sector, demonstrating the revolutionary potential of data science, ML algorithms, and dynamic dashboarding. |
| Year | 2023 |
| Corresponding Series Added Entry | Asian Institute of Technology. Research studies project report ; no. DSAI-23-06 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Data Science and Artificial Intelligence (DSAI) |
| Chairperson(s) | Vatcharaporn Esichaikul; |
| Examination Committee(s) | Chutiporn Anutariya;Dailey, Matthew N.; |
| Degree | Research Studies Project Report (M. Sc.) - Asian Institute of Technology, 2023 |