85% Cost Reduction: Document Processing Transformation

When a leading private bank approached us, their loan application processing was consuming enormous resources. Each application required manual review of dozens of documents—income statements, tax returns, property valuations, and identification papers. The process was slow, error-prone, and expensive.

This is the story of how we transformed their document processing workflow, achieving an 85% reduction in processing costs while improving accuracy and customer satisfaction.

The Starting Point

The bank's lending team was struggling with a growing backlog. Each loan application required:

• Manual review of 15-30 documents per application
• Data entry from documents into core banking systems
• Cross-validation of information across multiple sources
• Compliance checks for KYC and AML requirements

The average application took 3 business days to process. During peak periods, this stretched to 5+ days. Customer complaints were rising, and the cost per application was becoming unsustainable.

Our Approach

We proposed an AI-powered document processing system with four key components:

1. Intelligent Document Classification

The first step was teaching the AI to recognize different document types. Using computer vision and natural language processing, the system learned to identify:

• Identity documents (passports, ID cards, residence permits)
• Income documentation (salary slips, employment contracts, tax returns)
• Property documents (valuations, floor plans, land registry extracts)
• Financial statements (bank statements, investment portfolios)

Documents are automatically routed to the appropriate extraction pipeline based on their type.

2. Data Extraction Engine

Custom-trained models extract key data points from each document type. For a salary slip, this includes employer name, gross salary, net salary, pay period, and deductions. For a property valuation, it captures address, estimated value, valuation date, and property type.

The extraction engine handles:

• Printed and scanned documents
• Multiple languages (German, French, Italian, English)
• Various formats from different institutions
• Low-quality scans and photos

3. Cross-Validation System

Extracted data doesn't exist in isolation. The AI validates consistency across documents:

• Does the name on the ID match the name on the salary slip?
• Is the property address consistent across all documents?
• Do the stated income and bank statement deposits align?

Discrepancies are flagged for human review with clear explanations.

4. Human-in-the-Loop Workflow

We designed the system to augment humans, not replace them. Complex cases and edge scenarios are escalated with all relevant context, allowing staff to make informed decisions quickly.

Implementation Journey

The project was delivered in three phases over 12 weeks:

Phase 1: Core Engine (Weeks 1-4)

We focused on the highest-volume document types: income statements and identification documents. The AI was trained on 5,000+ historical documents, achieving 95% extraction accuracy by week 4.

Phase 2: Expansion (Weeks 5-8)

We added property documents and financial statements to the system. The cross-validation logic was implemented and tested against known cases.

Phase 3: Integration (Weeks 9-12)

The system was connected to the bank's core banking platform. Extracted data flows directly into the application workflow. Staff dashboards were deployed for exception handling.

The Results

Within the first month of full deployment, the transformation was evident:

Quantified Benefits

• Processing time: Reduced from 3 days to 4 hours average
• Staff redeployment: 10 FTE moved to higher-value customer-facing roles
• Error reduction: Rework rate dropped from 12% to under 1%
• Customer satisfaction: NPS increased by 18 points
• Annual savings: CHF 1.2 million in operational costs

Key Success Factors

Looking back at this project, several factors were critical to its success:

1. Quality training data: The bank had well-organized historical documents we could use for training
2. Clear scope: We started with a defined document set rather than trying to handle everything at once
3. Change management: Staff were involved throughout and understood the system would help them, not replace them
4. Iterative approach: We improved the models continuously based on real-world feedback
5. Executive sponsorship: Leadership was committed to the transformation

Looking Forward

The bank has since expanded the system to additional use cases including mortgage renewals, wealth management onboarding, and corporate lending. The foundation we built together continues to deliver value across the organization.