Confidential Case StudyGenAI · Credit Operations · Banking

CrediNote — GenAI Credit Memo Assistant

A GenAI-assisted workflow for structured credit memo drafting — generating consistent, source-grounded documentation drafts from user inputs and source materials, with human review controls throughout.

GenAI / LLMsPrompt EngineeringDocument ExtractionWorkflow DesignHuman-in-Loop

Executive Summary

Credit memo preparation is time-intensive and consistency-critical. CrediNote is a GenAI workflow that generates structured credit memo content from user inputs and source documents — reducing mechanical drafting burden while keeping editorial control entirely with the analyst.

Business Problem

Credit memos require consistent structure, precise language, accurate financial representation, and clear risk articulation. Producing them manually is slow and creates variability across analysts — different formats, levels of detail, and risk framing.

The goal was to reduce the mechanical burden of document drafting — not automate credit decisions — while maintaining quality, consistency, and full human editorial control.

My Role

I led the design of the workflow concept, input and output architecture, section mapping logic, prompt design, and the human review control framework. I worked with credit users to understand their memo structure requirements and governance expectations.

Architecture

Source Memo / Inputs

↓

Extraction

↓

Section Mapping

↓

Draft Generation

↓

Review Controls

↓

Final User Editing

Human Review Controls

No automatic final approval — all output requires analyst review before use

Draft outputs are clearly labelled and cannot be submitted without explicit user action

The system does not make credit recommendations — it drafts documentation based on inputs provided

Users retain full editorial control over the final document

Consistency checks surface potential gaps between input data and draft content

Business Impact

Reduced time spent on initial credit memo drafting

Improved structural consistency across credit documentation

Demonstrated a viable, governed approach to GenAI in high-stakes banking documentation workflows

Established a clear framework for human-in-loop AI assistance in credit operations

Lessons Learned

01Credit professionals are comfortable with AI drafting assistance when editorial control remains clearly with them. The framing of the tool matters as much as the capability.

02Structured inputs produce significantly better outputs than open-ended ones. Investing in form design and input validation pays dividends.

03The human review step is not just a control — it is where the analyst applies judgment that cannot be automated.

Confidentiality Note: Due to employer obligations, code, raw data, proprietary models, and internal investigation details are not disclosed. This case study presents architecture, methodology, and business impact only.

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