Approve/ Decline Policy Optimization using Bureau Data
Background: AQN optimized an SMB lender’s approve/ decline policy leveraging retro-appended credit bureau attributes and a Risk-Adjusted-Revenue framework it had previously built for the client
Outcome: Implementing AQN's recommended changes to Approve/ Decline policy is projected to save the client $3.4MM annually
AQN’s Approach:
Analyzed historical account performance after appending bureau retro-scores and attributes at time of origination
Found several bureau variables that could be used to identify toxic populations
Managed reduction in future originations and risk mitigation benefits from declining high-risk populations tradeoffs w/ client
Recommended approve/decline hardcuts at specific thresholds for two personal credit bureau variables
Leveraged the RAR framework to evaluate the profitability impact of these policy changes across each portfolio segment
Key Results:
