Optimizing Rating Engines through AI and Machine Learning: Revolutionizing Pricing Precision
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P110Keywords:
Rating Engines, Pricing Optimization, Artificial Intelligence, Machine Learning, Reinforcement Learning, Explainable AI, Real-Time Analytics, Predictive Modeling, Feature Engineering, Dynamic PricingAbstract
Accuracy in pricing has become a key competitive advantage in all manner of industries such as insurance and finance, e-commerce and telecommunications. Rating engines, which constitute the engine that sets prices or premiums, have historically been highly based on statistics and ratemaking businesses that are hand-coded. These legacy methods may have worked well back then, but when it comes to flexibility, instantaneous decision making and customized pricing, they are deficient. New improvements in the fields of Artificial Intelligence (AI) and Machine Learning (ML) offer a paradigm shift to enable the optimization of rating engines by providing them with greater accuracy, adaptability, and scalability. The paper is a result of an extensive analysis of the ways of applying AI and ML techniques to improve the equipment of the rating engine to change the accuracy of pricing. The paper starts by studying the shortcomings of the traditional rating engines, such as inflexible rule systems, dependency upon statistical datasets, and predisposition to human judgment. We discuss the paradigm change to data-starved, incessantly learning models that can dynamically adapt to market changes, regulatory restrictions and client conduct. We introduce a methodology based on the conjunction of supervised learning to perform predictive modeling, reinforcement to perform adaptive pricing, and explainable AI (XAI) frameworks to make it regulatory compliant and interpretable. The techniques used in evaluating contributions of features are emphasized with SHAP values. LIME is important in the creation of sturdy pricing models, and the creation process involves feature engineering and selection. We also combine real-time streaming analytics to support on-the-fly pricing updates, and use Apache Kafka and Spark Streaming to provide millisecond-level decision latency balances. Insurance case study and airline case study prove that AI-optimized rating engines will result in a decrease in pricing errors by 23 percent, profit margin enhancement by 12 percent, and customer satisfaction indicators enhancement by 18 percent. We have found that a combination of AI/ML with good discipline knowledge, and governance systems provides the best way to go. The discussion of the possible challenges (along with the suggestion of mitigation strategies) includes algorithmic bias, model drift, and data privacy compliance, namely, constraints on fairness in training, automated drift detection, and federated learning that support privacy-friendly model updates. Besides contributing to the academic body of knowledge, the conduct of the research provides a practical pathway to follow in transforming existing rating engines to prepare enterprises to achieve modern goals in hyper-personalized and real-time commerce
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