Dynamic Reward Shaping for Intelligent Risk Management of Loan Lost-Linking Customers in Multi-Mode Scenarios

In the artificial intelligence-based risk control model, the multi-mode dynamic decision-making problem of loan lost-linking customers has become a prominent concern in the intelligent transformation of the financial institution. This paper proposes a Dynamic Reward Shaping (DRS)-based reinforcement learning optimization framework. With the establishment of a parameterized virtual customer behavior simulator and mode-sensitive weight adapting rules, we set up a dynamic balance mechanism that can balance collection efficiency, cost control, and customer relationship maintenance. Across three typical lost-linking modes—Hide and Seek (HS) mode (cyclical lost-linking), Flee with the Money (FM) mode (systematic asset redistribution), and False Disappearance (FD) mode (abrupt lost-linking)—the DRS model improves significantly over traditional methods at a cost of 42%-55% less policy volatility with respect to an MCI of 48.7% (HS), 37.5% (FM), and 60.9% (FD). Using light module upgrading (upgrading the center reward calculation block only), one-step calculation latency decreases from 25 ms to 16.2 ms, offering an economic smart upgrading route to banks. It is the first that uses the DRS mechanism in multi-mode lost-linking risk control cases. With mode-sensitive weight update rules and action space compression techniques, it overcomes the traditional problem of goal conflict and computational efficiency of widely used methods. The system could be modular and react rapidly to various business conditions throughout the banking sector.