Modifying model risk management for financial institutions in the age of generative artificial intelligence
What is model risk management?
Finding, evaluating, and reducing the risks connected to utilizing statistical, mathematical, or artificial intelligence (AI)-driven models in decision-making is the primary objective of model risk management, or MRM. Financial institutions, insurance, healthcare, and other industries where analytical and predictive models direct crucial corporate operations and strategies frequently employ these models.
What is model risk management in banking?
Model Risk Management (MRM) is the process of determining, evaluating, and reducing the risks related to making decisions using statistical, mathematical, or artificial intelligence (AI) models. These models are frequently employed in industries such as healthcare, insurance, and financial institutions where crucial company operations and strategies are guided by analytical and predictive models.
A new age of quality, accessibility, efficiency, and compliance in the financial services sector is anticipated with generative artificial intelligence (gen AI). It also brings with it new hazards and complications, just like any new technology. The deployment of Gen AI by financial institutions will depend on finding a balance between maximizing its promise and reducing its hazards.
To handle the possible risks associated with using models in decision-making, regulators and the financial services sector have historically created a variety of model risk management (MRM) frameworks. Typically, these frameworks based on concepts include:
- Model validation: A thorough evaluation of a model’s precision, dependability, and constraints. To make sure the model works as intended and to find any potential biases or flaws, this frequently entails testing it using a variety of datasets and scenarios.
- Governance: Clearly defined roles and duties for creating, implementing, and overseeing the model. This frequently entails setting up procedures for recording modifications, approving models, and guaranteeing continuous supervision.
- Risk mitigation: It is the process of recognizing and controlling possible hazards, such as model bias, problems with data quality, and misuse. This frequently entails creating risk-reduction plans, like putting bias detection methods, data quality checks, and user access controls into practice.
Google Cloud’s earlier study, which it co-authored with the Alliance for Innovative Regulation (AIR), aimed to evaluate MRM’s applicability to AI and ML models. Building upon that basis, its most recent collaborative article examines the application of model risk management frameworks and well-established governance principles to risk management in emerging AI environments.
The report specifically suggests that regulators set expectations in the following four areas, recognize excellent practices, and improve regulatory clarity:
1) Model governance;
2) Model creation, application, and utilization;
3) Model verification and supervision; and
4) Shared accountability in third-party risk control.
Recognizing Gen AI’s possible effects
According to projections, Gen AI might boost the economy by much to $340 billion a year in the banking industry alone. Financial organizations are already using innovative AI-based solutions to boost employee productivity, improve client engagement, reduce fraud and security threats, and increase efficiency.
By going beyond analysis and prediction to produce original content, Gen AI sets itself apart from standard AI. By using probabilistic evaluations, these models generate a range of potential outcomes depending on the patterns they have discovered rather than a single conclusive result. More inventive and dynamic applications are made possible by this capacity, which opens up new possibilities for human-computer interaction.
Modifying the risk management model for Gen AI
Applications of general artificial intelligence have a lot of potential advantages, but there are also special features and risks associated with the technology that need to be considered and minimized. Importantly, the deployment of Gen AI in financial institutions can be accommodated by the flexibility of current model risk management frameworks, which are intended to guarantee the dependability and transparency of financial models.
Regulators could rely on industry best practices and standards that they believe provide solid, possibly presumptive, proof that the requirements of model risk management frameworks have been fulfilled in order to reduce confusion about how model risk can be managed to account for these particular features of gen AI.
According to Google’s new research, effective oversight of gen AI will require defined governance structures that specify roles, responsibilities, and accountability. It highlight three crucial areas where all parties involved stand to gain from more regulatory clarity:
- Documentation requirements: To define documentation expectations for gen AI models, it advise revising and elaborating model risk management guidelines.
- When determining the safety and soundness of new AI-based models, it advise regulators to consider developers’ use of techniques like grounding and outcome-based model evaluations in addition to model explainability and transparency.
- Safe and sound AI implementation measures: It advise authorities to identify a set of controls that are suitable for guaranteeing the responsible deployment of advanced AI in financial services, including ongoing monitoring, stringent testing procedures, and human-in-the-loop supervision.
Cooperation between governmental organizations, regulators, and industry players will be essential to this process. The realization of the full potential of gen AI in financial services and beyond will depend heavily on its shared dedication to responsible innovation and adherence to strong model risk management standards, even while the road ahead entails traversing challenging regulatory and ethical landscapes.
