Optimizing Claims Reserves and Payments with AI: Predictive Models for Financial Accuracy
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P106Keywords:
Claims Reserving, Insurance Payments, Artificial Intelligence, Machine Learning, Predictive Modeling, Financial AccuracyAbstract
Reserving and optimization of payment claims in insurance companies are accentuated procedures that have a direct impact on financial stability and profit-making. The conventional actuarial techniques are reliable, but they tend to be problematic due to outdated, inflexible indicators that have limited ability to adapt to new data trends. As Artificial Intelligence (AI) and Machine Learning (ML) have advanced, predictive models have proven to be a powerful tool for creating more accurate claims reserves and payment estimates. This paper provides an in-depth analysis of AI-based methods for predictive modelling, which can streamline the claims reservation and payment process. The study examines the application of various AI algorithms, including regression models, decision trees, neural networks, and ensemble methods, to improve financial accuracy, using the insurance industry as a case study. In the methodology section, the discussion of data preprocessing, feature engineering, model training, validation, and deployment strategy is outlined. Case study-derived empirical findings based on real insurance data sets confirm a drastic increase in the accuracy of claims reserves forecasts and payment timing optimization to minimize the risk of over- or under-reserving. Other advantages highlighted in the analysis include operational efficiencies gained from automating manual calculations and the capability to process a vast amount of data in near real-time. The article contributes to the literature by providing a comparative analysis of AI models, an adequate framework for integrating AI models into the current financial workflow of the insurance sector, and recommendations for future studies to address data quality, interpretability, and regulatory compliance challenges
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