A customer is denied a loan. A transaction is flagged and frozen for suspected fraud. A credit card application is silently declined. In each of these cases, the decision was made, at least in part, by an AI system, and when the customer or the regulator asks why, the bank needs a clear answer. Explainability risk is what happens when that answer does not exist, when the model that produced the decision is too complex, too opaque, or too poorly documented for anyone inside the institution to reconstruct the actual reasoning behind it.
As Indian banks and NBFCs increasingly rely on machine learning for credit scoring, fraud detection, customer segmentation, and collections prioritisation, explainability risk has moved from a theoretical AI ethics concern to a practical governance and compliance problem. The RBI’s own draft Guidance on Regulatory Principles for Model Risk Management, issued in mid 2026, makes clear that explainability is not optional for models used in regulated financial decisions, it is an expectation regulated entities must be able to demonstrate.
What Explainability Risk Actually Looks Like
Explainability risk materialises in several distinct ways inside a bank. A credit officer may be unable to tell a rejected applicant which specific factors drove the model’s decision, beyond a generic statement that the application did not meet the required score. An internal auditor reviewing a model may be unable to determine why the model weighted certain variables more heavily than others, making it impossible to assess whether the model is behaving as intended. A compliance team responding to a regulatory inquiry may struggle to reconstruct the exact logic behind a specific historical decision, particularly for complex models that have been retrained multiple times since the decision was made.
In each case, the underlying problem is the same. The institution built or deployed a model without ensuring that someone, somewhere, could explain its output in terms a regulator, an auditor, or an affected customer could actually understand.
Why This Risk Has Grown Sharply
Traditional credit scoring models, built on logistic regression or simple scorecards, were relatively easy to explain because the relationship between inputs and outputs was linear and transparent. Modern machine learning models, including gradient boosted trees, neural networks, and increasingly, large language model based systems, can achieve higher predictive accuracy but often at the cost of transparency. These models can involve thousands of interacting variables and non linear relationships that are genuinely difficult to translate into a simple, human readable explanation, even for the data scientists who built them.
This tension between predictive performance and explainability is not a new problem in machine learning generally, but it becomes a specific regulatory and legal problem in banking, where decisions about credit, fraud, and account access carry direct consequences for customers and are subject to fair lending, consumer protection, and increasingly, AI governance requirements.
The Regulatory Backdrop in India
The RBI’s draft Guidance on Regulatory Principles for Model Risk Management, issued on June 24, 2026, applies across eleven categories of regulated entities and explicitly extends model governance expectations to models employing artificial intelligence and machine learning, including third party models. The guidance emphasises governance and risk management across the complete model lifecycle, which necessarily includes the ability to explain how a model arrives at its outputs, not just how accurate those outputs are on average.
This means explainability can no longer be treated as a data science nice to have. It needs to be built into model development standards, validation processes, and documentation requirements from the outset, with clear expectations for what level of explanation is required for different types of decisions and different levels of model complexity.
Consequences When Explainability Fails
When a bank cannot explain an AI decision, the consequences extend well beyond an awkward customer conversation. Regulatory examiners reviewing model governance can flag the gap as a supervisory concern, potentially triggering broader scrutiny of the institution’s model risk management practices. Customers who believe they were treated unfairly, whether in credit decisions or fraud related account restrictions, have grounds for complaints that the institution cannot adequately defend without a clear explanation. Internal audit and risk functions lose the ability to properly validate that a model is working as intended, since validating a model’s logic requires understanding what that logic actually is. And in the event of litigation or a formal grievance, the absence of a documented, defensible explanation for a specific decision becomes a significant legal and reputational liability.
Building Explainability Into Model Governance
Addressing explainability risk requires deliberate choices at multiple stages of the model lifecycle. At the design stage, institutions need to weigh predictive performance against explainability requirements for the specific use case, recognising that a highly complex model may not be appropriate for decisions that require strong individual level explanation, such as credit denials. Where complex models are used, institutions increasingly rely on explainability techniques such as SHAP values or LIME to approximate which factors most influenced a specific prediction, though these techniques themselves need to be validated rather than assumed to be accurate. Documentation standards need to capture not just model performance metrics but the reasoning behind key design choices, so that explanations can be reconstructed even after a model has been updated or retired. And governance structures need clear ownership for explainability, typically sitting jointly between model risk, technology, and business functions, so that no one team can treat it as someone else’s responsibility.
Conclusion
Explainability risk sits at the intersection of technology, governance, and customer fairness, and it is becoming a defining test of whether a bank’s AI adoption is genuinely responsible or simply fast. Institutions that build explainability into model design from the start will be far better positioned than those trying to reconstruct explanations after a regulator or customer asks for one.
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