The client is a large regional bank with a strong corporate lending portfolio spanning multiple industries. Operating in a highly regulated environment, the bank is recognised for its advanced digital maturity and demanding standards for enterprise-grade solutions.

The initiative originated directly from the bank’s credit risk practitioners – analysts and risk experts working daily with complex datasets, strict regulatory requirements, and time-critical decisions. Their objective was clear: modernise corporate credit assessment in a way that delivers measurable business value, integrates seamlessly with existing decisioning systems, and stands up to both regulatory scrutiny and real-world operational pressure.

The bank’s corporate credit risk assessment process was highly manual and fragmented.

Analysts were required to process large volumes of financial and non-financial information - including financial statements, industry dynamics, macroeconomic indicators, and relevant market signals - and translate these inputs into structured credit proposals.

This workflow spanned multiple stakeholders and relied heavily on manual data collection and interpretation, resulting in inconsistent risk assessments across industries and client segments, as well as extended turnaround times.

A critical consequence was the growing number of applications landing in a “grey zone,” where risk could not be confidently classified as acceptable or unacceptable. Under pressure to deliver timely decisions and constrained by limited analytical capacity, these borderline cases were frequently declined by default - directly limiting portfolio growth, reducing approval rates for viable borrowers, and weakening the bank’s competitive positioning.

To remain competitive and unlock sustainable portfolio growth, the bank needed to:

• reduce credit approval turnaround times, particularly for SMEs, enabling faster access to capital and improving client experience;
• increase approval rates for viable borrowers by providing deeper, more consistent risk insights, especially for applications previously falling into the “grey zone;”
• standardise credit assessments across industries and client segments, ensuring objective, data-driven decisions at scale,
• augment human judgment with predictive risk signals, helping analysts identify emerging risks earlier while confidently approving borderline cases.

All of this had to be delivered as an extension of existing digital decision-making systems, fully aligned with local and European banking regulations, and with humans firmly in control of final decisions.

This set the stage for an AI-enabled credit risk assessment capability that augments analyst judgment with automated data ingestion, standardised risk evaluation, and predictive insights integrated directly into the bank’s existing decisioning systems.

The goal was to shorten time to approval, increase acceptance of creditworthy borrowers, and surface emerging risks earlier, without compromising regulatory compliance or human oversight.

We partnered closely with the client’s credit risk team to design and deliver a modular, AI-driven credit risk assessment platform.

The project started with a four-week proof of concept, built around fast feedback loops. Weekly review sessions with domain experts ensured the solution evolved incrementally and remained grounded in real analytical workflows.

Beyond implementing the initial concept, our team actively challenged assumptions and introduced critical capabilities early – including systematic data validation and accuracy measurement, both essential for building analyst trust and meeting regulatory expectations.

A key design principle was human-in-the-loop decision-making: every insight or data point extracted by the AI is presented to analysts for confirmation, with clear and intuitive visual traceability showing exactly where the information originated. This allows analysts to quickly locate, verify, and validate inputs before using them in risk assessments.
The final solution enables:

• AI integrations into an existing system users are already familiar with;
• automated extraction and consolidation of financial and industry data;
• standardised, industry-specific analytical views;
• predictive indicators across the full credit lifecycle;
• high data privacy, since the system is hosted on the bank’s infrastructure
  continuous monitoring following credit approval.

Together, these capabilities shifted analysts from manual data handling to higher-value risk interpretations and decision support.

By automating the most time-consuming and data-intensive aspects of credit risk assessment, the bank achieved a 60–75% reduction in analyst effort, allowing teams to move from manual data handling to higher-value analytical work.

This productivity gain directly increased processing capacity. With the same number of analysts, the organisation is now able to support two to three times more credit risk assessments, removing a key bottleneck in corporate lending and enabling portfolio growth without scaling headcount.

At an operational level, these improvements resulted in substantial cost avoidance. By deferring additional hiring and reducing rework caused by manual processing, the bank achieved an estimated €0.8–1.2 million in annual operational cost savings per ten credit analysts, while maintaining strict risk controls and regulatory compliance.