1. Enhancing Credit Facility In Credit Cards through ML Modelling
Predictive modelling using 12 months of historical data to identify credit card customers likely to convert retail balances to instalments. Utilize 6000 features for targeted marketing campaigns, enhancing customer engagement through channels like emails and push notifications.
2. Cross Sell Consumer Loans to Wealth Customers
Targeting Wealth customers for consumer loans offers numerous advantages for Institutions, including higher loan sizes, reduced credit risk, long-term customer relationships, upselling opportunities, reputation building, and market differentiation. One can identify lending customers from any pool of customer data through Machine Learning techniques to do consumer loans targeting. One of the projects we did was to identify the high propensity consumer loan target customers from the overall wealth base and secondly identify target Lending products from a large pool of customers.
3. Utilizing Large Language Models (LLMs) in enhancing Business Value
This comprehensive article explores the transformative applications of Large Language Models (LLMs) in the realms of text summarization, language enhancement, and structured data analysis for businesses. We delve into how LLMs work, their practical usage, and their significance in summarizing content from PDF and HTML files.
4. Reactivation of Inactive Customer base through ML
Reactivating inactive customers is a valuable opportunity for the mutual fund company, as it can help to increase the customer lifetime value, reduce the customer acquisition cost, and improve customer loyalty and satisfaction. One of the use cases where artificial intelligence and machine learning-based data analytics can be used to increase sales and grow the SIP book is by reactivating the existing inactive base of customers.
https://g-square.in/finalytica-november-2023-reactivation-inactive-customer-base-ml/
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