Model to optimize the Loss Ratio Index of Valued Documents in a retail company in Lima through Business Intelligence and Machine Learning

The retail sector in Peru has experienced significant growth driven by economic expansion and the increased purchasing power of the middle class. Companies such as Ripley SAC, a department store chain, have expanded their presence and diversified their offerings. However, the company faces a challenge in optimizing the Loss Ratio Index (IRL) of valued documents, such as insurance policies and surety bonds, which are currently below the desired level. This research aims to develop and implement a model to optimize the IRL in retail companies using Business Intelligence (BI) and Machine Learning (ML) methodologies. The study is based on historical and real-time data on sales, claims, policies, and other relevant factors. The approach includes BI techniques for data exploration and visualization, identifying patterns and trends, and ML techniques to predict the Loss Ratio Index of valued documents. The implementation of these strategies seeks to reduce insurance losses and increase profitability.