Credit processes in banking are at a turning point. Volatile markets, new competitors, increasing regulatory requirements and an exponentially growing database are putting unprecedented pressure on traditional, largely manual and siloed processes.
At the same time, customers expect fast and consistent decisions, while regulators demand transparency, traceability, and control. In this environment, the use of AI is increasingly coming into focus.
The panel discussion “AI-powered credit process: Balancing innovation, risk, and regulation” at RiskMinds International made it clear that the transformation of the credit process is no longer just an efficiency initiative, but a strategic necessity.
Banks need to think about innovation, risk and regulation together – and redesign the lending process accordingly as an end-to-end system.
AI adoption is advancing – governance is still catching up
At many banks, AI is already being used in the credit decisioning process. Machine learning models enhance rating and scoring models by incorporating new data sources, such as transaction and cash flow information. Automated decision engines increase straight-through processing rates, while early warning systems help identify portfolio risks earlier.
At the same time, a key weakness is emerging: the adoption of AI is outpacing banks’ ability to control and govern it effectively. While more than 90% of large organizations use AI, fewer than 10% have mature AI governance frameworks in place.
In addition, the growing number of models is increasingly overwhelming validation and governance functions. Credit decisions, portfolio monitoring, stress testing and risk management are, in many institutions, historically separated, with limited transparency into data provenance, model usage, and decision-making impact.
What AI specifically improves in the credit process
AI opens up the possibility for banks to rethink the lending process holistically. In underwriting and decision-making, new data sources enable a much more precise risk assessment. Transaction and behavioral data reflect real cash flows better than traditional financial metrics. The result is measurable performance improvements. During the RiskMinds panel discussion, participants mentioned increases in the accuracy ratio between 3% and 8%.
Better models build trust in automated decisions. Reducing false-negative rates increases the acceptance of straight-through processing – especially in retail and SME businesses. Automation rates of more than 90% are no longer a vision; they are a reality. They are already becoming common practice.
Another value-add is continuous monitoring. AI-supported early warning systems replace selective portfolio analyses with permanent risk monitoring. This enables more dynamic management of risk appetite and faster reactions to market changes.
The paradigm in credit portfolio management is also shifting. It’s moving away from static limits and toward a dynamic, data-driven risk appetite that combines origination decisions, portfolio strategy, and risk-bearing capacity.
The potential of AI in stress testing is becoming particularly clear. In an increasingly interconnected risk landscape, historical scenarios are reaching their limits. AI and generative AI enable the development of complex, interlinked scenarios beyond traditional assumptions. The goal is to further develop stress testing from a purely analytical exercise into a control-relevant instrument – from “stress to insight” to “stress to action.”
Why progress and complexity go hand in hand
Multiple factors are driving the use of AI, including the availability of large, heterogeneous volumes of data, increasing market and risk complexity, high cost pressures and the desire for faster, more consistent decisions.
At the same time, regulation itself is becoming a driver. Transparency, explainability and governance are no longer optional, but prerequisites for the productive use of AI.
However, this is also where the biggest obstacles lie. New regulations, such as the EU AI Act, increase requirements for documentation, monitoring, and human oversight, especially in high-risk applications such as credit decisions.
Complex models pose significant explainability challenges for banks, while clean data provenance and automated lineage become mandatory disciplines as the number of variables grows. In addition, there is a transition from selective model validation to continuous monitoring, as data and models constantly evolve. Limited human resources, skill gaps, and questions around bias, fairness and ethics further exacerbate these challenges.
EU AI Act and high-risk AI: Governance as a control center
Credit decisions are typically considered high-risk AI use cases under the EU AI Act. For banks, this means it is no longer sufficient to document individual models. The entire operational context, from the use case and decision impact to the underlying data, controls, and ongoing monitoring, must be mapped as an operating model.
The RiskMinds panel discussion highlighted two distinct paradigm shifts. First, the focus is moving from pure model governance to use-case governance. The decisive factor is not only how a model works, but how it is used. Second, validation is becoming a continuous task. Point-in-time checks are no longer sufficient in dynamic AI systems.
The role of AI governance
AI governance, therefore, becomes a key enabler for the scalable and responsible use of AI in the credit process. Central platforms for inventorying use cases and models, policy-based controls, end-to-end documentation, and continuous monitoring of performance, drift, bias, and compliance create transparency and reduce complexity.
Human-in-the-loop mechanisms and clear responsibilities ensure that automation does not lead to a loss of control but instead strengthens decision quality and trust.
Use and governance of AI – two sides of the same coin
On the one hand, banks want to use AI in their business processes in a targeted way to make credit decisions faster, more precise, and more scalable. On the other hand, they must comply with strict regulatory governance requirements.
These two perspectives are not opposites, however. They are two sides of the same coin.
There is no sustainable use of AI without proper AI governance. Banks must first trust their own AI systems and models before integrating them deeply into critical business processes. This trust is not an end in itself, but a prerequisite for improving key banking KPIs in a targeted way – from greater efficiency and a better cost-income ratio to increased profitability (ROE), better risk quality (NPL ratio) and more stable profitability, for example, through an optimized net interest margin.
Only when AI models are operated transparently and in a controlled, understandable way can banks use them to ensure long-term profitability at lower cost and with acceptable risk.
Building future‑ready credit decisions with governed AI
AI has the potential to fundamentally transform the lending process from individual credit decisions to portfolio management. However, success depends not only on better models, but on the ability to operate those models securely, transparently, and in a controlled way within regulatory frameworks. Banks that approach AI and governance together create the foundation for more resilient, more efficient, and future‑ready credit processes.
