Our client is a top-tier commercial bank serving a diverse portfolio of corporate clients, including SMEs, multinational companies, and institutional investors. Their corporate onboarding and credit risk evaluation process involved multiple departments, manual documentation reviews, and subjective decision-making frameworks.
The bank’s existing process faced several bottlenecks. Manual effort was required to read, interpret, and transcribe financial statements, leading to time-consuming document reviews that slowed down loan approvals and onboarding.
We developed a modular, risk assessment engine that analyzes corporate financial data, contextual information, and historical trends to support underwriters and risk analysts, automating time-consuming parts of the evaluation without removing human oversight.
Software Deployment
Workflow Mapping and Model Alignment
We started with a collaborative discovery phase involving credit analysts, compliance officers, and other stakeholders. Together, we mapped the current evaluation process and identified where automation could bring the most value.
Document Collection and Financial Labeling
Next, we gathered hundreds of anonymized financial reports, audit statements, and risk evaluation samples from past clients. We built a preprocessing pipeline to clean, normalize, and transform uploaded documents and spreadsheets into machine-readable, structured formats.
Model Training and System Architecture
We built the core risk engine by combining three components: a document extraction module using OCR and LLMs, a financial analytics layer for computing ratios and trends, and an LLM-based explanation generator trained on credit risk summaries.
Controlled Rollout and Analyst Feedback
The engine was rolled out to a select group of underwriters and credit officers. We ran it in parallel with the manual review process, comparing results and collecting qualitative feedback on scoring accuracy, report quality, and summary clarity.
Performance Monitoring and Regulatory Readiness
After successful validation, the system was deployed bank-wide, with continuous monitoring of accuracy, adoption, and exception rates. A compliance review module was added to allow regulators and internal audit teams to trace model behavior.
The system could automatically extract key figures and insights from unstructured documents such as scanned financial statements or spreadsheets.
Using standardized financial ratios, trend detection algorithms, and peer benchmarks, the engine generated dynamic risk scores within seconds.
Every score was paired with a plain-language explanation, including supporting figures, insights into trends, and clearly stated reasons for any risk alerts.
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