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Fintech Intelligence

FINTECH INTELLIGENCE 

Scaling Predictive Accuracy for Global Investment Products

Problem Statement

“A global investment bank needed a highly accurate, large-scale forecasting system to optimize return on investment across deposits, securities, and loans—while dynamically responding to economic shifts and regional performance.”

  • Needed to forecast potential outlet churn for proactive planning 
  • Required dynamic pricing by region to improve product margins 
  • High dependency on validating models against real-world data 
  • Frequent disruptions from Federal Reserve decisions and market volatility 

Business Outcomes

35%

Increase in forecast projection accuracy

60%

Reduction in operational resource dependency

Solutions

  • Unified business-critical data across global regions and verticals into one aggregated data store 
  • Developed dedicated forecasting models for each product segment (deposits, loans, securities)
  • Embedded seasonality patterns and macroeconomic variables such as interest rate hikes 
  • Applied machine learning and deep learning models based on product types for monthly and annual cash flow predictions 

Technological Framework

Statistical Forecasting Models:

  • Auto-regressive Integrated (AR)
  • Seasonal Auto-Regressive
  • ARIMA & SARIMA
  • Trigonometric Seasonality

Machine Learning & AI:

  • CATBoost 
  • Gradient Boosting