Document Type : Original Research Paper

Authors

• Saghar Heidari
• Morteza Hosseingholipour

Department of Actuarial Science, Faculty of Mathematical Sciences, Shahid Beheshti University

Abstract

BACKGROUND AND OBJECTIVES: Variable annuities have become a popular tool in retirement planning due to their attractive guarantees. These insurance products offer a reliable, steady income stream, not only aiding in financial stability during retirement but also enhancing mental well-being and overall quality of life by enhancing financial security and reducing anxiety. Variable annuities, offered by insurance companies, are tax-deferred retirement products designed to help individuals navigate the challenges of preserving the long-term value of their assets amid economic volatility, inflation, and uncertainty. However, the risk management of these products, which requires precise, up-to-date, and frequent valuation, poses a significant computational challenge for insurance companies, especially in large portfolios. Today, common and traditional methods such as Monte Carlo simulation, are often inefficient for risk management of these products due to their time-consuming nature. In recent years, with the emergence of modern technologies such as meta-modeling methods as a solution to these challenges, they have been proposed, but unfortunately, limited research has focused on increasing the adaptability and intelligence of these secondary models when facing changing data. In this regard, to address this challenge, the main objective of this study is to improve the adaptability and intelligence of meta-models by developing an intelligent meta-model based on artificial intelligence that can value large variable annuity portfolios more quickly and accurately. As a result, this would enable better responsiveness to ongoing market developments and portfolio structure changes.
METHODS: In this study, to address data changes, we need to improve the adaptability and intelligence of meta-models, which will consequently improve their responsiveness to ongoing developments in the market and portfolio structure. This improvement is achieved through the development of methods for intelligently determining the number of representative contracts, optimally selecting a subset of contracts, and choosing the most accurate underlying valuation models. For this purpose, a suite of models and methods will be evaluated to assess their effectiveness and suitability in this context. Therefore, the methodology of this research involves the design and implementation of an intelligent meta-model, whose performance is evaluated using five well-known machine learning models from among machine learning models and according to the evaluation criteria commonly used in these methods. The main emphasis of the proposed approach in this study is on improving model accuracy through innovative approaches in data preprocessing and effective sampling, enabling the models so that the chosen models can generalize complex patterns to new data rather than merely memorizing them. These evaluations are conducted using standard metrics, and ultimately, the integration of these elements into an intelligent system aims to provide a final validation of the potential of artificial intelligence in this field.
FINDINGS: The results of this study confirm the effectiveness of artificial intelligence in enhancing the meta-models for valuing variable annuities. The final meta-model obtained from the proposed approach demonstrated highly accurate performance according to standard metrics, indicating the model’s ability to cope with the valuation complexities of large insurance portfolios. The key factors of this success include the high importance of data preprocessing, targeted sampling, and the selection of efficient machine learning models as representative models. The results indicate that although increasing the size of the dataset for the meta-model may not necessarily improve prediction accuracy and efficiency, the use of optimization algorithms for selecting and tuning the model’s parameters can significantly improve performance. Therefore, the findings emphasize the important role of parameter optimization over merely increasing the training data size in enhancing the final model’s accuracy and efficiency in prediction.
CONCLUSION: This research focuses on intelligent enhancement of meta-models through artificial intelligence to transform variable annuity portfolio management. The main finding of this study is a significant improvement in the quality and accuracy of the obtained results in valuation and risk management of the large portfolios of variable annuities in insurance industry. For this, by integrating precise and reliable methodologies into the proposed meta-model structure, enabling insurance companies to manage their portfolios of variable annuities with greater confidence and provide better service to policyholders. These results not only enhance the credibility and significant rolls of novel approach of artificial intelligence applications in the insurance industry but also offer practical insights for companies to develop stronger and more adaptable valuation and risk management models in the face of the financial complexities inherent in variable annuities.

Keywords

• Intelligent Models
• Insurance Large Portfolio
• Machine Learning
• Meta Models
• Risk Management
• Variable Annueties

Main Subjects

• Risk management in the insurance industry