Case Study

Optimizing Deposit Pricing with AI-Driven Interest Rate Intelligence

Industry

Banking

Location

Our Contributions

Advanced Analytics, AI Modeling, Pricing Optimization

Technologies

Machine Learning, Bayesian Optimization

Coforge partnered with a global UK bank to design and implement an AI-driven interest rate optimization solution aimed at maximizing revenue across deposit products. The initiative focused on replacing manual, intuition-based pricing decisions with a data-driven, predictive approach.

By combining machine learning models with Bayesian optimization techniques, Coforge enabled the bank to dynamically determine optimal interest rates based on market conditions, competitive positioning, and revenue impact. The solution empowered pricing and treasury teams with real-time insights, improving decision-making and driving measurable business value.

Transformation Timeline

Phase 1

Data foundation setup and market benchmarking

Phase 2

Model development and optimization framework design

Phase 3

Rate recommendation engine and visualization layer

Phase 4

Business adoption and scenario-based optimization

Drag

The Challenge

The bank relied on manual and time-intensive processes to determine interest rates for deposit products, limiting its ability to respond quickly to market changes. Pricing decisions often lacked a comprehensive view of competitive dynamics and customer behavior, leading to suboptimal revenue outcomes.

Additionally, integrating internal product data with external competitor benchmarks was complex and fragmented, making it difficult to generate accurate and timely insights. The absence of advanced analytical models further constrained the bank’s ability to evaluate trade-offs between pricing, market share, and profitability.

The organization required a scalable, intelligent solution to automate rate optimization, enhance pricing accuracy, and support strategic decision-making.

Our Approach / Solution

Market-Aware Data Foundation

Built a unified data model combining internal product attributes with externally sourced competitor rate data, enabling a comprehensive market view.

Advanced Optimization Engine

Developed machine learning models integrated with Bayesian optimization techniques to evaluate trade-offs between interest rates, market share, and revenue.

Automated Rate Recommendation

Enabled real-time computation of optimal interest rates for individual and multiple products, factoring in competitive positioning and expected revenue impact.

Revenue & Sensitivity Analysis

Delivered interactive visualizations and projections illustrating the relationship between interest rates, market share, and revenue outcomes.

Intuitive Decision Support Interface

Designed a user-friendly interface with tabular and graphical views, allowing pricing teams to quickly interpret insights and make informed decisions.

Partner / Technology Ecosystem

Machine Learning Models
Bayesian Optimization Frameworks
Data Analytics & Visualization Platforms

Impact to Date

+20%

Revenue Increase per Product

-40%

Reduction in Pricing Decision Time

Improved

Pricing Accuracy & Market Responsiveness

Enhanced

Scenario-Based Planning