SR 11 – 7 Regulations

Introduction

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 AI 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 AI models are responsible and ethical and that decisions made with them are fair and transparent.

AI in Banking Sector

The banking sector is one of the most data-intensive industries in the world, and as such, AI models are used extensively in order to drive decision-making and improve operations. There are a number of ways in which AI models are used in the banking sector, including:

1. Automated customer service:

Through the use of artificial intelligence (AI) 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.

2. Detecting fraudulent activity:

AI 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. AI 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.

3. Financial forecasting:

Financial forecasting is another area where AI models can be very useful. By analyzing historical customer data, AI models can make predictions about future trends in the market, to make better investment decisions. AI can be used to automate and streamline loan applications by training them to identify patterns in data that indicate creditworthiness.

4. Data Security:

Banks are under constant threat from cyber criminals, and their security systems need to be constantly updated to stay ahead of the latest threats. AI models can help banks to identify potential cyber threats by monitoring unusual activities and taking steps to prevent them. By using AI to help improve their security systems, banks can stay one step ahead and keep their customer’s data safe.

What is Model Risk

Model risk arises primarily because of potential or fundamental errors in the models and inappropriate usage or implementation of a model. These errors and inaccuracies can cause significant monetary losses, poor organizational decision-making, and reputational damage.

Model Risk Management (MRM)

Model Risk Management (MRM) in AI models 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 in AI models 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 AI 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 AI models and develop a framework that mitigates and manages the risks of the deployed models.

SR 11-7

SR 11-7 (Supervision and Regulatory Letter SR 11-7) guidance on MRM of AI models 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.

Guidelines

  • A banking organization’s internal audit function should assess the overall effectiveness of the model risk management framework, including the framework’s ability to address both types of model risk for individual models and in the aggregate.
  • Whenever a banking organization uses external resources for model risk management, the organization should specify the activities to be conducted in a clearly written and agreed-upon scope of work, and those activities should be conducted in accordance with SR11-7 protocols.
  • All banking organizations should ensure that their internal policies and procedures are consistent with the risk management principles and supervisory expectations contained in this guidance
  • Senior management is responsible for regularly reporting to the board on significant model risk, from individual models, and in the aggregate, and approving model risk management policies.
  • These policies should also outline controls on the use of external resources for validation and compliance and specify how that work will be integrated into the model risk management framework.
  • The role of the model owner involves ultimate accountability for model use and performance within the framework set by bank policies and procedures.
  • The model owner should also ensure that models in use have undergone appropriate validation and approval processes, promptly identify new or changed models, and provide all necessary information for validation activities.
  • Documentation and tracking of activities surrounding model development, implementation, use, and validation are needed to provide a record that makes compliance with policy transparent.

How to implement SR 11-7 in an organization

SR 11-7 implementation will vary depending on the specific AI models being used in the banking sector. However, some general tips on how to implement SR 11-7 for AI models in the banking sector include:

  1. Define your goals and objectives for a comprehensive risk management framework that includes policies, procedures, and processes to identify, assess, and manage risk.
  2. Work with regulatory bodies to establish an internal risk management committee composed of representatives from each functional area to ensure that AI models comply with all relevant regulations.
  3. To perform risk assessments for the purpose of evaluating risk management on a regular basis in order to identify any potential risks and processes to respond to those risks, and to report the information about those risks to appropriate stakeholders.
  4. Implement controls and safeguards to mitigate any risks and ensure that banks implement a risk management program throughout the organization with proper training for employees.
  5. To ensure that your AI models are operating as intended and that your risk management program remains current, you should monitor and evaluate them regularly

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