Accelerating Credit Risk Assessments with AI Automation

Client Overview

A leading Fortune Global 500 financial institution needed to accelerate its credit risk assessment process by automating financial spreading activities.

The institution handled large volumes of financial statements, where analysts manually extracted data from PDF documents and populated financial spreading templates in Excel. This created delays, increased operational dependency, and limited the team’s ability to scale credit analysis efficiently.

Problem Statement

For a global financial institution, credit risk assessment speed directly impacts decision-making, portfolio monitoring, and client responsiveness.

The existing financial spreading process was manual, time-intensive, and resource-heavy. Analysts spent significant time extracting financial data from statements, validating figures, and entering information into Excel templates.

Key challenges included:

• Financial spreading required 24 to 48 hours for processing
• Manual data entry increased operational effort and turnaround time
• Limited automation reduced scalability across multiple financial statements
• Complex financial formats required careful interpretation and validation
• Inconsistent templates created challenges in maintaining transparency and standardization

For leadership, the challenge was not only process efficiency. It was about creating a scalable, accurate, and reliable credit risk workflow that could support faster financial decisions without compromising control.

Solution Provided

Decimal Point Analytics developed an AI-powered financial spreading automation solution designed to extract, structure, and populate financial data with speed and accuracy.

The solution combined table detection models, LLM-based templating, and customized automation logic to process financial statements from PDFs and transfer the required data into spreading Excel templates.

The solution included:

AI-driven data extraction
Automated extraction of financial tables and relevant data points from PDF statements.

LLM-based templating
Used intelligent templating to interpret financial statement structures and map extracted data into the required format.

Automated Excel population
Enabled direct data transfer into spreading templates, reducing manual effort and repetitive analyst intervention.

Custom financial logic
Built client-specific logic to handle complex calculations, including gross sales, depreciation, amortization, and other financial line-item variations.

Human validation layer
Maintained a review mechanism to ensure accuracy, control, and confidence in the final output.

Outcome

The AI-powered solution significantly improved credit risk assessment efficiency while maintaining high accuracy and transparency.

Key outcomes included:

• 90% reduction in processing time, from 48 hours to under 6 hours
• Near 100% accuracy achieved with human validation
• Faster credit risk assessments across financial spreading workflows
• Improved scalability for handling multiple financial statements simultaneously
• Enhanced consistency and transparency in financial analysis
• Reduced dependency on repetitive manual data entry

Key Takeaway

AI-powered financial spreading can help financial institutions move beyond manual, resource-intensive credit analysis workflows.

By automating data extraction, templating, and Excel population, Decimal Point Analytics enabled the client to reduce turnaround time, improve scalability, and strengthen the reliability of credit risk assessments.

For financial institutions managing high-volume credit risk assessments, speed and accuracy are no longer separate priorities. With the right AI-led automation framework, both can be achieved at scale.

Explore how Decimal Point Analytics can help your organization accelerate credit risk operations with intelligent automation.