Case Study

Enhancing Liquidity Planning with AI-Driven Payment Settlement Forecasting

Industry

Banking

Our Contributions

Advanced Analytics, Time-Series Forecasting, Treasury Optimization

Technologies

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

Coforge partnered with a leading bank to improve liquidity management by implementing an AI-driven payment settlement forecasting solution. The objective was to accurately predict daily cash requirements to ensure timely payment obligations and reduce liquidity risk.

By leveraging advanced machine learning and time-series modeling, Coforge enabled the bank to forecast short-term cash inflows and outflows with greater accuracy. The solution empowered treasury teams with real-time insights, improving cash reserve planning, operational efficiency, and strategic decision-making.

Transformation Timeline

Phase 1

Data collection, cleansing, and preprocessing

Phase 2

Model evaluation and benchmarking

Phase 3

Forecasting engine development and UI enablement

Phase 4

Production deployment and validation

Drag

The Challenge

The bank faced challenges in accurately requirements due to the complexity and variability of cash inflows and outflows. Existing approaches relied on limited analytical capabilities, leading to inefficiencies in cash reserve planning and increased liquidity risk.

Fragmented historical data and lack of standardized preprocessing made it difficult to generate reliable forecasts. Additionally, the absence of advanced modeling techniques limited the bank’s ability to adapt to different forecasting horizons and changing transaction patterns.

The organization required a scalable and intelligent solution to improve forecasting accuracy, enable dynamic planning, and support data-driven treasury decisions.

Our Approach / Solution

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

Impact to Date

+50%

Improvement in Short-Term Forecast Accuracy

R² ≈ 0.53

(4-Day Forecast Accuracy)

R² ≈ 0.31

(1-Week Forecast Accuracy)

Reduced

Liquidity Risk through Better Planning