In 2008, following the global financial crisis, regulatory bodies and governments came together to establish a set of rules and regulations to ensure that organizations that use ML and predictive models for business decisions are compliant with all relevant laws and regulations. These regulations, such as SR 09-1 and SR 11-7, etc., set out the expectations for model risk management in the banking and bank holding company sectors. These regulations also mandate the use of standardized approaches for the development, testing, validation, implementation, retraining, and retirement of models, including models acquired from third parties, and ensure that the performance of the models is monitored over time. This helps to ensure that ML models are responsible and ethical and that decisions made with them are fair and transparent.
The banking sector is one of the most data-intensive industries in the world, and as such, ML models are used extensively in order to drive decision-making and improve operations.
There are a number of ways in which ML models are used in the banking sector, including:
Through using tools such as chatbots and natural language processing, companies can handle queries and complaints to improve customer service efficiency by analyzing customer data, predicting customer behavior, and increasing operational efficiency and customer retention.
ML models are used to analyze past data to identify patterns in transaction data to detect suspicious transactions, and anomalies in real-time transactions to flag them for further investigations. ML models are also being used to identify other potential risks, such as money laundering, terrorist financing, and customer identity theft, resulting in a significant reduction in losses and reputational damage.
Financial forecasting is another area where ML models can be very useful. By analyzing historical customer data, ML models can make predictions about future trends in the market, to make better investment decisions.
Banks are under constant threat from cyber criminals, and their security systems need to be constantly updated to stay ahead of the latest threats. ML models can help banks to identify potential cyber threats by monitoring unusual activities and taking steps to prevent them. It improves their security systems, banks can stay one step ahead and keep their customer’s data safe.
Model Risk Management (MRM) is a process that involves identifying, assessing, and managing risks that could impact the accuracy or performance of a model. MRM is a subset of Governance, Risk, and Compliance (GRC) that deals specifically with the risks associated with models.
MRM requires a combination of data science, ML engineering, and risk management practices to help organizations design and implement procedures to ensure the accuracy, robustness, and reliability of their data science models.
There are a number of ways to approach model risk management, but one common approach is to establish a model risk management framework. This framework should identify the key risks associated with ML models and establish processes for assessing and mitigating those risks.
To do this, organizations need to have a clear understanding of the potential risks associated with ML models and develop a framework that mitigates and manages the risks of the deployed models.
SR 11-7 (Supervision and Regulatory Letter SR 11-7) guidance on MRM in the banking sector was released by the United States Federal Reserve and Office of the Comptroller of the Currency (OCC) in 2011 specific to the financing sector for algorithmic accountability act providing requirements for how a model should be developed, tested, validated and governed.
The guidelines are intended to help banks whenever to identify, assess, and manage risks due to inaccurate models, data quality issues, model complexity, or incorrect model implementation.
SR 11-7 implementation will vary depending on the specific models being used in the banking sector. However, some general tips on how to implement SR 11-7 for models in the banking sector include: