1、Model Risk Management the Difference.Key considerations in effective management of modelsTable of contentsIntroductionModel lifecycle Managing model risk for complete model lifecycleAI/ML based modelConclusion34513143|Key considerations in effective management of models1.IntroductionOver the past fe

2、w years,we are witnessing a surge in the use of models based on complex quantitative methodologies to guide the financial institutions in strategic decision-making,management of various risks and entire credit lifecycle.With ever changing business environment,Financial Institutions(FIs)are required

3、to develop/redevelop models either to meet new regulatory directives such as Fundamental Review of Trading Book(FRTB),Expected Credit Loss(ECL)computation,and Stress Testing,or using advanced techniques based on Artificial Intelligence(AI)/Machine Learning(ML).As the use of models increase,so does t

4、he risk associated with them,potentially leading to financial and reputational losses,business setbacks due to poor strategies and decisions,regulatory penalties,and in extreme cases,suspension of licenses for financial institutions in case model risk is not properly assessed and managed.The increas

5、ing complexity and reliance on model outputs have significantly heightened model risk,prompting regulators worldwide to focus more on this issue.As a result,new regulations and guidelines are being developed.Recently,in August 2024,the Reserve Bank of India(RBI)issued draft guidelines on Model Risk

6、Management(MRM)and independent model validation.Thus,to manage model risk and comply with regulatory requirements,FIs should develop and implement robust MRM policy and framework.Such a framework will ensure that the output generated by the model is reliable and can be used for the intended purpose.