Credit-risk decision-support workflow

Context

Delta Holding had credit analysis work that was valuable but time-intensive. Analysts were working across two layers of information: public-company risk signals and internal operating context like cooperation history, payment behavior, and exposure across business entities.

The opportunity was not to replace analysts with a generic chatbot. It was to make the existing process faster and more usable by combining both signal layers into a structured workflow without removing the need for judgment.

Problem

The core challenge was operational, not cosmetic: analysts still needed useful assessments and limit recommendations, but assembling the relevant context from multiple sources was a poor use of expert time. Any AI-assisted path also had to fit the existing decision process and produce outputs analysts would actually trust and use, not disconnected summaries with no workflow home.

What I built

I helped shape and deliver a credit-risk decision-support workflow that combined public-company risk signals with internal operating and payment context to produce structured, analyst-ready recommendations across business lines.

The output was not just a free-form summary. It was closer to a constrained recommendation layer that could support outcomes like approve, decline, reduced limit, conditional approval, or further review.

The system was designed to support analyst judgment, not replace it. Trust came from workflow fit, constrained output shapes, and the reviewability of every recommendation.

At a public-safe level, the workflow shape looked roughly like this:

Why this shape, not a chatbot or full automation?

A chatbot would have been too loose for a credit-risk workflow. Full automation would have removed too much judgment from a decision that still needed analyst ownership.

The useful middle was a constrained recommendation layer: structured enough to improve speed and consistency, reviewable enough to preserve trust, and bounded enough to fit the existing decision process.

Delivery context

My role

I worked across workflow shaping, recommendation design, and implementation decisions that made the first version usable inside the existing analyst process.

This work was done in-house at Delta Holding, where I progressed from AI Specialist to AI Innovation Lead, led a small AI team, and continued contributing hands-on across delivery.

Team shape

The work sat between domain users who owned the credit decision flow and the technical path required to turn scattered inputs into a structured recommendation workflow.

Constraints

The system had to fit an existing analyst-owned process, combine external and internal context safely, and keep every recommendation reviewable instead of pushing black-box automation.

Production-minded choices

• The workflow was designed around an existing analyst process instead of treating the model as a stand-alone product.
• The focus stayed on usefulness, recommendation quality, and business acceptance rather than novelty.
• The implementation had to earn trust from users, because a speed gain without trust would not hold in practice.
• The work evolved from tenant-specific proofs of concept toward a shared internal platform shape, with reusable architecture and governance considerations built in from early stages.

Outcome

The strongest public outcome evidence from this case was workflow-level: analysis time dropped by roughly 75%, analyst-rated quality reached 4.4/5, and recommendation acceptance was above 90%.

Those signals matter because they point not only to speed, but to trust, consistency, and real workflow adoption.

Stakeholder signal

A senior risk analyst at DMD said the system made it possible not only to handle credit-limit increase analyses coming through sales, but also to introduce more regular periodic analyses for all buyers that had not been done before.

Why this case matters

This case matters because it shows how I approach internal AI systems in higher-trust workflows: make the data blend useful, keep outputs structured and reviewable, and design the system so it supports real decisions instead of sounding impressive in a demo.