Integrating Customer Portfolio Theory and the Multiple Sources of Risk Approaches to Model Risk-Adjusted Revenue

This study proposes new methods to formulate customers' risk-adjusted revenue (RAR) metrics applied to the financial industry. Using a customer dataset provided by a loan company, we compute RAR using benchmark approaches presented in the literature and new formulas that combine the Customer Portfolio Theory and the Multiple Sources of Revenues approaches. We validate the efficiency and originality of our formulations by implementing statistical tests to check for differences across the different RAR measures. We find that the proposed RAR models are unique and can be implemented in the industry to account for multiple sources of risk, hence providing managers with ways to improve their valuation of customers' portfolios.