银行数字化转型下的模型风险管理框架

在数字化转型浪潮中,商业银行越来越依靠大数据、人工智能和大模型等技术的结合,通过建立各类模型来实现智能获客、产品定价、风险内控、自动化决策等。模型的广泛应用一方面显著改善了客户体验,有效提升了管理精细化,大幅降低了内部管理成本;但与此同时,模型的广泛使用所带来的风险也在不断上升。本文针对模型风险日益上升的痛点问题,在梳理模型风险管理和监管经验的基础上,深入分析了模型潜在风险对管理形成的相关挑战,包括模型管理机制模糊、模型设计复杂、模型偏差易于衍化、模型基础设施薄弱、模型管控零散、模型人才匮乏等。本文尝试设计了模型风险管理的逻辑框架,从管理原则、业务场景、方法手段、模型数据等方面,搭建了体系化管理方法,并给出模型风险管理监管的改进建议,为数字化转型背景下商业银行的模型管理和风险防范提供了有益思路。

In the wave of digital transformation,commercial banks are increasingly relying on the combination of big data,artificial intelligence,and big model technologies to establish various models to achieve intelligent customer acquisition,product pricing,risk internal control,automated decision-making,etc.The widespread application of the model significantly improves customer experience,effectively enhances management refinement,and significantly reduces internal management costs.At the same time,the risks brought by the widespread use of models are also constantly increasing.This article focuses on the pain points of increasing model risks.Based on reviewing the experience of model risk management and supervision,it deeply analyzes the related challenges posed by potential risks of models to management,including fuzzy model management mechanisms,complex model design,easy derivation of model deviations,weak model infrastructure,scattered model control,and a shortage of model talents.This article attempts to design a logical framework for model risk management,to build a systematic management method on these aspects such as management principles,business scenarios,methods,and model data,and to provide suggestions for improving model risk management supervision.This provides useful ideas for model management and risk prevention in commercial banks in the context of digital transformation.