Enhancing Liquidity Planning with AI-Driven Payment Settlement Forecasting

Data Foundation & Preprocessing

Extracted and cleansed historical transaction and settlement data to ensure consistency, accuracy, and reliability for model training.

Advanced Model Benchmarking

Evaluated multiple machine learning and time-series models, including Decision Trees, Random Forest, XGBoost, Prophet, SVR, and LSTM, to identify optimal approaches for different scenarios.

Dynamic Time-Series Forecasting Engine

Developed a flexible forecasting framework allowing users to select forecasting horizons (e.g., 4 days, 1 week, 1 month) based on business needs.

Performance-Driven Model Selection

Implemented automated model selection using performance metrics such as R² and RMSE to ensure the most accurate forecasts for each use case.

Production Deployment & Validation

Integrated the solution into the bank’s environment to generate near real-time forecasts and validated outputs against actual settlement data.

Partner / Technology Ecosystem

• Machine Learning & Time-Series Models (XGBoost, LSTM, Prophet) 

• Data Processing & Analytics Frameworks

• Visualization & UI Tools