Accelerating Credit Decisioning with Automated Bank Statement Analysis
A commercial bank wanted to automate the extraction and analysis of financial data from bank statements and reports to accelerate risk assessment, improve decision accuracy, and reduce manual review time across lending operations.
Outcomes
6
Vendors Evaluated
Side-by-Side
98%
Data Accuracy Achieved
4 weeks
PoC Cycle
60%
Underwriting Time Reduced
Technology Vendors Suited to Evaluation
Business Problem
The bank’s commercial lending process relied heavily on manual data entry and document review. Relationship managers and underwriters spent hours reconciling balances, identifying income patterns, and validating cash flows – delaying loan approvals and increasing operational costs. The lack of automation also limited visibility into a borrower’s real-time financial health, slowing credit decisioning and risk assessment.
Challenges
- Manual Data Processing: Loan teams spent excessive time extracting and reconciling financial data.
- Format Variability: Bank statements and financial reports differed widely across clients, complicating automation.
- Data Accuracy Risks: Manual handling increased the likelihood of errors in credit models.
- Slow Decisioning: Lengthy verification steps delayed loan approvals and customer responses.
- Scalability Constraints: High volume of commercial applications created backlogs for underwriting teams.
From Idea to Evidence with NayaOne
NayaOne enabled the bank to validate multiple OCR and data extraction vendors within a secure sandbox that replicated real-world lending conditions without exposing sensitive data.
- Synthetic Data Testing: Vendors were provided with synthetic commercial bank statements and financial reports to simulate real lending scenarios safely.
- Rapid Vendor Access: NDAs and sandbox onboarding were completed within a day, allowing instant technical setup and evaluation.
- Automated OCR & Data Parsing: Each tool was tested for field-level extraction accuracy, balance validation, and cash flow categorisation.
- Performance Benchmarking: Vendors were compared side by side on accuracy, processing speed and, scalability.
Within two weeks, the bank identified the most efficient OCR solution for automated financial data extraction – cutting underwriting turnaround time and improving decision quality.
Impact Metrics
PoC Timeline Reduction
4 weeks with NayaOne vs 12 – 18 months traditionally
Time Saved in Vendor Evaluation
1+ year
Decision Quality
The sandbox provided the bank with clear, evidence-based insights into each vendor’s real-world performance. By comparing accuracy, extraction speed, and integration ease side by side, credit and risk teams were able to identify the most reliable OCR solution.
KPIs
- Data Extraction Accuracy (%): Precision of data captured from statements and financial reports.
- Underwriting Time Reduction (%): Decrease in time required for loan analysis and approval.
- Error Rate Reduction (%): Decline in manual data corrections and reconciliation issues.
- Analyst Productivity Gain (%): Increase in the number of applications processed per underwriter.
Validate OCR and Financial Data Extraction Tools Before Deployment
Use NayaOne’s secure sandbox to test AI-powered OCR and data extraction solutions with synthetic bank statements – measuring accuracy, integration speed, and underwriting efficiency before production rollout.
