Article
A STUDY ON LIFE INSURANCE OF KOTAK MAHINDRA BANK
The life insurance sector in India is undergoing rapid digital transformation, with institutions like Kotak Mahindra Bank integrating advanced technologies to improve customer experience, risk assessment, and product personalization. This study presents an AI, Machine Learning (ML), and Deep Learning (DL)-based analytical exploration of Kotak Mahindra’s life insurance offerings, focusing on how datadriven strategies can optimize policy performance, customer acquisition, and satisfaction. Using a combination of historical policyholder data, claim records, and customer feedback, the study employs ML algorithms such as Random Forest, Logistic Regression, and K-Means Clustering to predict policy renewal behavior, assess risk levels, and segment customers based on demographics and buying behavior. Natural Language Processing (NLP) is used to analyze unstructured data such as customer reviews and support interactions, revealing key emotional drivers influencing insurance purchases. LSTM networks and other deep learning models are utilized to forecast claim patterns, retention trends, and customer lifetime value. The results indicate that AI-powered approaches significantly enhance decision-making in life insurance services. Predictive analytics help reduce churn, optimize underwriting processes, and recommend personalized insurance plans. This study demonstrates that by leveraging intelligent systems, Kotak Mahindra Bank can deliver smarter, more responsive, and customer-centric life insurance solutions in an increasingly competitive financial landscape.
Full Text Attachment
