Abstract

Insurance risk assessment is the process of evaluating possible losses coming from insurance contracts, allowing insurance companies to set premiums accordingly. Predictive modeling helps insurers to identify exposure and severity risk of different portfolios and segments, and also to compute claim reserves. This research surveys predictive modeling techniques suitable for assessing one of these risks: the insurance risk towards the policyholder. Unsupervised, traditional statistical, and machine learning techniques are examined, synthesizing the most relevant characteristics and parameters. Use of these techniques is motivated by business imperatives, with the objective of providing risk scores that can be deployed operationally. Therefore, emphasis is placed on calibrating predicted probabilities or risk scores to monetary risk in the relevant time frame, and on applying strategies to mitigate the problem of imbalanced data typical of insurance portfolios. Calibration of best-performing classifiers or predictors into expected loss has been explored, as well as transformation of severity predictions into a Poisson process (or renewal process) for the purpose of claim frequency scoring. Temporal features are constructed from simple lags and rolling statistics, and parallel datasets with different label definitions are created for further modeling, following principles of transfer learning. Practical issues such as data engineering, external data integration, dimensionality reduction, and methods for handling data imbalance are also outlined.

Keywords

  • Predictive modeling
  • insurance risk
  • calibration
  • validation
  • risk scoring
  • exposure
  • imbalanced dat

References

  1. Blier-Wong, C., Cossette, H., Lamontagne, L., & Marceau, E.. Machine learning in P&C insurance: A review for pricing and reserving. Risks.
  2. Varri, D. B. S. Automated Vulnerability Detection and Remediation Framework for Enterprise Databases. Available at SSRN.
  3. Gabrielli, A., Richman, R., & Wüthrich, M. V. A neural network boosted double overdispersed Poisson claims reserving model. ASTIN Bulletin: The Journal of the IAA.
  4. Rongali, S. K. Cloud-Native API-Led Integration Using MuleSoft and .NET for Scalable Healthcare Interoperability.
  5. Henckaerts, R., Côté, M.-P., Antonio, K., & Verbelen, R.. Boosting insights in insurance tariff plans with tree-based machine learning methods. North American Actuarial Journal.
  6. Inala, R. Designing Scalable Technology Architectures for Customer Data in Group Insurance and Investment Platforms.
  7. Ramos-Pérez, E., Alonso-González, P. J., & Núñez-Velázquez, J. J.. Stochastic reserving with a stacked model based on a hybridized artificial neural network. Expert Systems with Applications.
  8. Gottimukkala, V. R. R. Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power.
  9. Wüthrich, M. V. The balance property in neural network modelling. Statistical Theory and Related Fields.
  10. Keerthi Amistapuram , "Energy-Efficient System Design for High-Volume Insurance Applications in Cloud-Native Environments," International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI.
  11. Owens, E., Prokhorenkova, L., & Polonik, W.. Explainable artificial intelligence (XAI) in insurance. Risks.
  12. Rongali, S. K. Predictive Modeling and Machine Learning Frameworks for Early Disease Detection in Healthcare Data Systems. Current Research in Public Health. https://doi.org/crph.
  13. Berg, T., Wüthrich, M. V., & Ziegel, J. F. Local bias adjustment, duration-weighted probabilities, and automatic construction of tariff cells. Scandinavian Actuarial Journal.
  14. Sateesh Kumar Rongali. Cloud-Native API-Led Integration Using MuleSoft and .NET for Scalable Healthcare Interoperability. Journal for ReAttach Therapy and Developmental Diversities. https://doi.org/jrtdd.v.
  15. Frees, E. W., Derrig, R. A., & Meyers, G. Predictive modeling applications in actuarial science. Cambridge University Press.
  16. Vadisetty, R., Polamarasetti, A., Guntupalli, R., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. Privacy-Preserving Gen AI in Multi-Tenant Cloud Environments. Sateesh kumar and Raghunath, Vedaprada and Jyothi, Vinaya Kumar and Kudithipudi, Karthik, Privacy-Preserving Gen AI in Multi-Tenant Cloud Environments (January).
  17. Gao, G., Meng, S., & Shi, Y. Dispersion modelling of outstanding claims with double Poisson regression models. Insurance: Mathematics and Economics.
  18. Chakilam, C., Koppolu, H. K. R., Chava, K. C., & Suura, S. R. Integrating Big Data and AI in Cloud-Based Healthcare Systems for Enhanced Patient Care and Disease Management. Global Research Development (GRD) ISSN.
  19. Zhang, P., Li, Z., & Fang, K. Spatially clustered mixture of experts model for dependent frequency and severity of insurance claims. North American Actuarial Journal.
  20. Gottimukkala, V. R. R. Digital Signal Processing Challenges in Financial Messaging Systems: Case Studies in High-Volume SWIFT Flows.
  21. Barry, L. The fairness of machine learning in insurance: New rags for an old man? Risks.
  22. Vadisetty, R., Polamarasetti, A., Guntupalli, R., Rongali, S. K., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. Legal and Ethical Considerations for Hosting GenAI on the Cloud. International Journal of AI, BigData, Computational and Management Studies.
  23. Su, C., & Bai, L. A machine learning approach for claim frequency prediction in automobile insurance. PLOS ONE.
  24. Varri, D. B. S. Cloud-Native Security Architecture for Hybrid Healthcare Infrastructure. Available at SSRN.
  25. Baudry, M., & Robert, C. Y. A machine learning approach for individual claims reserving in insurance. Applied Stochastic Models in Business and Industry.
  26. Pamisetty, V. Optimizing Unclaimed Property Management through Cloud-Enabled AI and Integrated IT Infrastructures. Universal Journal of Finance and Economics. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/.
  27. Richman, R. AI in actuarial science: A review of recent advances. Variance.
  28. Amistapuram, K. Digital Transformation in Insurance: Migrating Enterprise Policy Systems to .NET Core. Universal Journal of Computer Sciences and Communications. Retrieved from https://www.scipublications.com/journal/index.php/ujcsc/article/view/.
  29. Krasheninnikova, E., García, F., & Fernández, F. Reinforcement learning for pricing strategy optimization in the insurance industry. Engineering Applications of Artificial Intelligence.
  30. Gadi, A. L. The Role of Digital Twins in Automotive R&D for Rapid Prototyping and System Integration.
  31. Panlilio, J., Caliskan, A., & Wüthrich, M. V. Modelling, analytics and insights from data: Implications for actuarial practice. British Actuarial Journal.
  32. Diana, T., Spiva, A., & Hu, J. A survey of machine learning and actuarial applications. North American Actuarial Journal.
  33. Rongali, S. K. Cloud-Native API-Led Integration Using MuleSoft and .NET for Scalable Healthcare Interoperability.
  34. England, P. D., & Verrall, R. J. Stochastic claims reserving in general insurance. British Actuarial Journal.
  35. Vadisetty, R., Polamarasetti, A., Guntupalli, R., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. Privacy-Preserving Gen AI in Multi-Tenant Cloud Environments. Sateesh kumar and Raghunath, Vedaprada and Jyothi, Vinaya Kumar and Kudithipudi, Karthik, Privacy-Preserving Gen AI in Multi-Tenant Cloud Environments (January).