Achieving End-to-End Payment Reconciliation Across Customer Advice, Banking, and SAP Records

Customer Overview

A fintech company specializing in working capital and trade finance solutions helps businesses access funding for growth, cash flow management, supplier payments, inventory purchases, and cross-border trade.

As demand increased across markets and industries, the company needed to process higher application volumes without slowing down credit decisions or weakening risk assessment quality.

Timely financial analysis was central to its lending process. However, the growing volume of borrower financial statements created pressure on analysts, making faster and more consistent financial reviews a business priority.

The Challenge

For working capital and trade finance providers, decision speed directly affects customer experience. Businesses often need funding tied to urgent commercial needs, such as supplier commitments, inventory cycles, or time-sensitive trade opportunities.

However, the company’s financial review process depended heavily on manual preparation. Analysts had to collect, review, organize, and normalize data from balance sheets, income statements, cash flow statements, and supporting schedules before meaningful credit analysis could begin.

As application volumes grew, the organization faced several challenges:

Lengthy financial review cycles delaying credit decisions.

Heavy analyst effort spent on data preparation instead of risk evaluation.

Inconsistent financial statement formats across borrowers and industries.

Difficulty processing scanned, image-based, and low-quality documents.

Manual normalization slowing down financial comparison and analysis.

Pressure to maintain credit quality while improving turnaround time.

The Solution

ScryAI implemented its Financial Spreading solution to automate the preparation of borrower financial information used in credit evaluation.

Automated Financial Statement Extraction

The solution extracts financial data from balance sheets, income statements, cash flow statements, and supporting schedules, reducing the need for analysts to manually capture information from each document.

Intelligent Format Interpretation

The platform interprets financial statements across different layouts, reporting structures, document formats, and scan qualities. This allows the company to process borrower submissions more consistently, even when documents vary by industry or source.

Structured Financial Data Preparation

Extracted data is categorized, normalized, and organized into a structured format ready for financial review. This gives analysts faster access to clean, review-ready information for credit evaluation.

Analyst-Focused Credit Review

Automated preparation reduced manual effort, giving analysts more time to assess borrower performance, identify risk indicators, and support lending recommendations. The solution reduced financial review effort by 80%, improved extraction and classification accuracy to over 90%, and enabled faster credit decisions without increasing analyst workload.

Results at a Glance

Metric	Outcome
Financial Review Speed	Cut review effort by 80%
Credit Decision Readiness	Delivered structured borrower data earlier in the lending cycle
Extraction Accuracy	Achieved over 90% accuracy in financial data capture and classification
Analyst Productivity	Shifted analyst time from manual spreading to credit risk evaluation
Borrower Response Time	Enabled faster turnaround for working capital financing decisions
Volume Scalability	Handled rising application volumes without matching headcount growth
Governance Fit	Supported secure deployment within compliance-driven workflows
Review Consistency	Created standardized financial views across varied borrower submissions

Faster Financial Reviews for Better Working Capital Decisions

With ScryAI Financial Spreading, the fintech company replaced manual financial statement preparation with faster access to reliable, structured data, enabling quicker credit assessments and more responsive financing decisions.

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