Prioritize these 4 processes to balance innovation and responsibility in banking model risk management

Although artificial intelligence (AI) and machine learning (ML) have improved processes in fraud detection, underwriting, customer service, and compliance, there are risks to relying too heavily on these tools in model risk management (MRM).

Most financial institutions are typically well-equipped to manage MRM today, but four out of 10 processes need sharp attention from professionals because of the potential for reputational and regulatory consequences, according to an assessment by Crisil Integral IQ and Chartis Research:

Purpose limitation

One of the most overlooked yet damaging risks in AI/ML deployment is purpose limitation. This means repurposing a model and using it for processes not originally intended for, often without adequate validation or controls.

Though it is done through a seemingly minor adjustment in model application, it can violate compliance requirements, harm customers and damage the company’s reputation.

The danger lies in assuming that a model proven effective in one domain will perform equally well in another. For instance, a fraud detection model repurposed for credit underwriting may yield misleading results due to contextually different data patterns and performance expectations. To avoid this, institutions must clearly define each model’s intended purpose and scope in documentation and enforce strict changes in management protocols.

Model cards—structured documentation outlining model assumptions, intended uses, and limitations—are becoming increasingly valuable tools to address such issues. They help standardize understanding across teams and stakeholders. Banks should make it mandatory for their staff to get explicit approval for each use case instead of giving a blanket validation for a model.

Ultimately, cultivating a culture of critical scrutiny is the key. Institutions must actively discourage casual or opportunistic model repurposing and establish heightened monitoring requirements when models are stretched beyond their core design.

Explainability and interpretability

While AI/ML models have made it possible to use complex data to arrive at decisions, it has come at the cost of transparency. Unlike traditional statistical models, which favor simplicity and interpretability, many AI/ML systems (particularly those using deep learning) operate as “black boxes.” This opacity makes it difficult for stakeholders to understand how certain decisions were reached, increasing the risk of regulatory violations and stakeholder distrust.

The inability to explain a model’s output becomes particularly problematic in sensitive domains such as credit decisions. A financial institution that denies a loan without being able to articulate the reasoning behind the decision well may run afoul of regulators or civil rights laws even if the model’s prediction is statistically sound.

To navigate these challenges, banks are turning to post hoc explanation tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). While these help in demystifying model behavior, they often offer generalized insights rather than precise justifications for individual predictions as they can be sensitive to data sampling and correlation among input features.

To build trust and ensure compliance, banks must establish standards to select explainability tools and methods. They should also differentiate between global (general model behavior) and local (individual prediction) explanations, selecting techniques appropriate for each use case.

Institutions should not overly rely on post hoc solutions. Instead, they must prioritize interpretability by design, especially in high-stakes applications. In some cases, simpler models may be preferable when the marginal gains in accuracy from complex AI systems are outweighed by their lack of transparency.

Third-party dependency

As AI/ML capabilities often require specialized software, datasets and computational tools, many financial institution—especially smaller ones—turn to third-party vendors. While this can accelerate adoption, it also introduces critical vulnerabilities related to oversight, accountability and systemic dependence.

Third-party models often come with limited visibility into how they were developed, what data was used and how they behave under stress. Smaller institutions may lack the bargaining power or technical resources to demand transparency or perform deep due diligence. This lack of insight can delay detection of errors, increase compliance risk and even result in operational disruptions.

Moreover, the risk extends beyond the primary vendor. Increasingly, vendor solutions rely on their own third-party sources, introducing fourth-party dependencies. These cascading dependencies further reduce visibility and complicate risk assessments.

To manage such exposures, banks must create robust vendor evaluation processes. Before deploying vendor models, they should identify all assumptions, data sources and potential limitations. Ongoing model updates should be treated as significant events requiring revalidation. Additionally, institutions should assess each vendor’s contingency planning— examining how failures will be addressed, service continuity maintained and compliance assured.

By demanding accountability and transparency from vendors, institutions can better protect themselves from the pitfalls of relying on third-party AI/ML tools.

Data integrity, protection and privacy

AI/ML models thrive on vast datasets. In banking, where customer data is highly sensitive and tightly regulated, this presents a critical dual-risk challenge: Protecting privacy and preventing/detecting hidden learning where AI models may inadvertently infer protected/sensitive attributes.

One risk is unauthorized or improper use of personal data during model training. Unintended inclusion of restricted data sets can lead to privacy breaches and violations of data protection laws such as the General Data Protection Regulation (GDPR). Another, more subtle, risk is the inadvertent encoding of sensitive attributes such as race or gender through proxy variables, even when such data is not explicitly used. This can result in biased outcomes and potentially lead to legal consequences.

To mitigate these risks, institutions must implement rigorous data governance across the model lifecycle. Data sources should be carefully documented and vetted for privacy compliance. Techniques such as differential privacy and adversarial testing can help models avoid learning unwanted patterns related to protected characteristics.

Furthermore, independent validation teams should be tasked with identifying and addressing fairness and privacy risks before models are approved for deployment. Ongoing monitoring should include testing for discriminatory behavior in real-time model outputs.

Proper management of data integrity and privacy is not just a legal requirement; it is foundational to create customer trust and institutional reputation.

6 risks generally well-managed by MRM frameworks

The assessment also identified six risks of AI/ML, but these can be managed by the frameworks in use today:

Accountability: Ensuring accountability for model outcomes is essential. While this is a notable risk for AI/ML deployments, financial institutions—especially regulated banks—already operate under clear accountability structures guided by frameworks such as SR 11-7, which outline responsibilities across the model lifecycle that can be easily augmented to encompass legal, compliance, technology and third-party level accountability to the use of AI.

Bias and fairness: Bias and fairness risks are well-recognized across the industry. Thanks to regulatory obligations under laws such as the Equal Credit Opportunity Act (ECOA) and the Fair Housing Act (FHA), financial institutions already have processes in place to test and correct model bias.

Transparency and robustness: Banks have long practiced robust documentation and validation for traditional models. These capabilities are easily extended to AI/ML, ensuring transparency around model development, limitations and expected performance against extreme, missing or erroneous inputs.

Ethical and legal compliance: While unregulated industries often struggle with ethical oversight, banks operate in a mature regulatory environment that naturally enforces high ethical and legal standards by focusing on data privacy, bias and fairness, and transparency and accountability risk factors. This reduces the risk in AI/ML deployment compared with other sectors.

Scalability and performance: Although scalability is a core AI/ML concern, most financial models operate in narrowly defined use cases such as loan origination or fraud alerts. These models typically don’t require real-time scalability and performance demands of massively distributed consumer AI systems.

Human-AI interaction: Human misuse and misunderstanding of AI outputs can lead to bad decisions. But the banking industry can benefit from structured oversight, audit trails and well-trained staff, that enable more disciplined human-AI interaction than in many other industries where risks are posed by either fully autonomous AI systems or poorly governed human-AI workflows.

Conclusion

There is no doubt that the integration of AI and ML into banking has started a new era in the industry. But banks must now rethink traditional frameworks and focus on the unique challenges posed by AI/ML.

By concentrating on purpose limitation, explainability, third-party dependency and data privacy, while also fine-tuning MRM framework for the other six risk areas, institutions can adopt a pragmatic, risk-based approach that balances innovation with responsibility.

As AI continues to evolve, so must the risk practices that govern it.

Nageswara Ganduri is Global Head of Quantitative Services, Crisil Integral IQ.