Data-driven customer selection is rare including regulation-adjusted client value, as product silos limit internal evaluation of client profitability, exposure, and risk.
Most banks struggle to move beyond the widely available product-by-product economics to determine the profitability of an individual client.
Goals and Objectives
Develop a holistic, regulation-adjusted view of customer profitability.
Develop advanced risk selection capabilities to select the best assets on a full-cycle perspective.
Optimize the bank’s balance sheet under all regulatory constraints.
Coordinate and enforce client value with sales activities.
Embed portfolio optimization to regulatory stress testing scenarios.
Big Data and analytics (BDA) and machine learning (ML) to drive predictive analytics such as scenario modeling based on economic growth, regulations, willingness to adjust loan portfolio, and risk exposure of the client
Early warning system of credit-quality deterioration among clients
Digital tools to support prioritization of leads for client opportunities to steer relationship management activity
Use Case Summary
An improved client and segment selection will allow corporate banks to optimize return on capital, as a result of the lower cost of risk and higher revenue over assets. It will also drive sales efficiency.
Furthermore, it will help ensure compliance with Basel III, IFRS 9, CECL, and BCBS 311.