Machine Learning in Actuarial Science: Enhancing Predictive Models for Insurance Risk Management

• Author(s): Munashe Naphtali Mupa ; Sylvester Tafirenyika ; Melody Rudaviro Nyajeka ; Tamuka Mavenge Moyo ; Eliel Kundai Zhuwankinyu
• Paper ID: 1707214
• Page: 493-504
• Published Date: 19-02-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 8 February-2025

Abstract

As highlighted earlier, ML has become an essential tool in actuarial practice as far as assessment and management of risks are concerned among insurance firms. Through improving the predictive models, actuaries can better predict risks, set appropriate prices and make better underwriting decisions. Conventional actuarial practices involve reliance on historical information and statistical formulas, however, contemporary and large data require better solutions. Decision trees, deep learning neural nets and ensemble techniques, for example, are designed to analyze large volumes of structured and unstructured data for trends and correlations that could be difficult to find using other techniques. Machine learning in actuarial science involves the use of sophisticated algorithms in claims prediction, fraud detection, customer segmentation, and loss modeling. Real-time data from social media, IoT devices, and telematics have the potential of providing more accurate and timely analysis and prediction when fed to ML models; this can increase the efficiency of insurance operations and customer satisfaction (Varney, 2019). Moreover, with the use of ML, actuaries are given the capability to update the model and make necessary changes as data and trends in risk changes over time. Nevertheless, there are challenges that come with the integration of ML in actuarial science; data quality, model interpretability, and how the results are presented to the users. In this regard, while actuaries can leverage the use of sophisticated algorithms to develop predictive models for risk assessment, they also need to ensure that such models are transparent and are developed in compliance with the set regulations. Thus, the paper aims to discuss the opportunities and limitations of the machine learning approach in the context of actuarial work and its further development for managing insurance risk. The future of actuarial science actually depends on how it successfully merges with ML to provide insurance companies with even better tools for risk assessment and management.

Citations

IRE Journals:
Munashe Naphtali Mupa , Sylvester Tafirenyika , Melody Rudaviro Nyajeka , Tamuka Mavenge Moyo , Eliel Kundai Zhuwankinyu "Machine Learning in Actuarial Science: Enhancing Predictive Models for Insurance Risk Management" Iconic Research And Engineering Journals Volume 8 Issue 8 2025 Page 493-504

IEEE:
Munashe Naphtali Mupa , Sylvester Tafirenyika , Melody Rudaviro Nyajeka , Tamuka Mavenge Moyo , Eliel Kundai Zhuwankinyu "Machine Learning in Actuarial Science: Enhancing Predictive Models for Insurance Risk Management" Iconic Research And Engineering Journals, 8(8)