A global survey found that 55% of bank chief risk officers consider advanced technology a top priority for managing risk, while 72% report that AI adoption within the risk function remains at an early stage—a position that has changed little since 2024. The first figure reflects strategic intent; the second reflects implementation maturity. The practical question is how risk functions can move AI from priority to production without weakening control, explainability, or accountability.
Quick Answer
AI can help lenders process broader datasets, prioritize applications, and surface risk signals faster than manual review alone. Risk and compliance do not disappear; they extend into data quality, model validation, explainability, bias and drift monitoring, human oversight, and auditability. Global survey data shows that adoption within bank risk functions remains limited for most respondents. Balancing speed, risk, and compliance is therefore not only a model-performance problem. It is an operating-model and governance challenge.
The Reality Behind Every AI Credit Decisioning Rollout
The 15th EY and Institute of International Finance (IIF) Global Bank Risk Management Survey, which polled 101 banks across 31 countries, found that 55% of chief risk officers (CROs) rank advanced technology as a top priority for managing risk. At the same time, 72% report that AI adoption within the risk function remains in early stages, a pace the survey found has changed little since 2024.
That’s not as much a contradiction as it is a description of where the industry actually is. Prioritizing a technology and having it embedded in daily risk decisions are two different things. The work in AI credit decisioning now needs to bridge that gap.
Speed: What AI Actually Changes
The same survey found that AI adoption is furthest along in fraud and financial crime detection, where 61% of CROs report active deployment. Cyber and operational risk monitoring follows at 41%, and credit and market risk modelling sits at 33%, the least mature of the three.
That ordering is noteworthy. Speed in credit decisioning isn’t really about a single model running faster. It’s about processing more signal per decision without adding proportional review time. This processing works best in use cases with the clearest data and lowest ambiguity. Credit risk modelling lags because it’s harder, not because banks aren’t trying. The same survey found that 80% of CROs cite data quality and availability, not appetite or budget, as the primary barrier to scaling AI further.
Risk: Why Faster Isn’t Automatically Better
As traditional risks resurge, faster decisioning is being asked to carry more weight. The same EY and IIF survey found that credit risk re-emerged as banks’ top concern, cited by 62% of CROs, driven by rising default worries and competition from non-bank lenders. Financial crime concern nearly doubled year on year to 43%, and digital fraud concern jumped from 23% to 59%.
That is the practical argument for defining human review according to decision consequence, model confidence, and exception type rather than inserting it indiscriminately into every application. Speed and oversight are not opposing goals. In the survey, 55% of CROs expected hybrid human-AI roles to increase, while 64% anticipated greater automation of manual tasks. The two trends can advance together when responsibilities and escalation paths are explicit.
Compliance: The Governance Gap Nobody’s Closed Yet
This is where the numbers get less comfortable. The Cambridge Centre for Alternative Finance’s 2026 Global AI in Financial Services Report, produced with the Bank for International Settlements, the IMF, and the World Economic Forum, found that only 50% of industry respondents have adopted explainable AI methods, even though 78% of regulators rate explainability as critical or important to their objectives. About two-thirds of respondents are not actively monitoring their AI systems for bias.
Adoption without adequate governance can create regulatory, conduct, and model risk. A credit decision supported by a well-performing model still requires specific and reproducible reasons, monitoring for bias and drift, validation appropriate to the use case, and an audit trail that can withstand scrutiny. These controls do not emerge from model accuracy alone; they must be designed around the model and the end-to-end decision process.
How Leading Lenders Are Balancing the Three
The lenders managing this well tend to focus on certain habits, not a technology choice:
- They run hybrid models (neither all-AI nor all-rules). Pairing AI’s pattern detection with rules-based guardrails gives both speed and a traceable decision path.
- They keep a human in the loop at defined points. Not every decision needs a human. But the ones with the highest consequence or lowest model confidence must go through human review.
- They build governance alongside the model. Explainability tooling, bias monitoring, and documentation get built into the rollout plan of the AI model from day one rather than being retrofitted after a regulator asks.
- They treat compliance as a design input. Teams building the model and teams responsible for compliance work from the same requirements, not in a sequence.
A Practical Checklist for Balancing Speed, Risk, and Compliance
Before scaling an AI credit decisioning rollout, it’s worth confirming:
- Can every automated decision produce a documented, specific reason instead of a generic template?
- Is someone actively monitoring for bias and model drift, or is that assumed to be fine because performance metrics look good?
- Where exactly does a human review point sit in the process, and was that placement deliberate?
- Is the AI governance framework actually implemented and tested, or does it exist mostly on paper?
- If a regulator asked for the audit trail behind a specific decision tomorrow, can the team produce it quickly?
The Bottom Line
Speed, risk, and compliance should not be treated as independent design goals. Faster credit decisioning is sustainable only when data quality, explainability, human oversight, monitoring, and auditability are built into the workflow. The 55% of surveyed CROs prioritizing advanced technology have identified the opportunity; the 72% reporting early-stage AI adoption are confronting the implementation work that determines whether it produces durable results.
