A major global bank, known for its extensive operations, sought to refine its forecasting methods to enhance loan distribution and optimize resource allocation across numerous branches.

The bank needed to improve the precision of its loan opportunity forecasts to better cater to the varying needs of its diverse customer base, spread across different locations and business sectors.

Developed an advanced AI-driven forecasting model that leverages machine learning and clustering analysis:

• Advanced Clustering Techniques: Implemented to categorize customers based on location, business category, and other relevant attributes, enhancing the targeting of loan offers.
• Intelligent Forecasting Models: Utilized state-of-the-art forecasting algorithms, integrating tools like Python, Flask, and XGBoost, to analyze and predict loan demand more accurately.
• External Signal Identification: Incorporated external economic and market indicators to adjust forecasting models in real-time, ensuring higher accuracy.

• Enhanced Forecasting Accuracy: Significantly improved the accuracy of predicting loan opportunities, enabling more precise and strategic resource allocation.
• Optimized Loan Distribution: Tailored loan distribution strategies to meet the specific needs of different customer segments effectively.
• Improved Decision Making: Empowered bank executives with data-driven insights, facilitating informed decision-making and strategy formulation.

Python3, Flask, NumPy, XGBoost, Gurobi, scikit-learn.