Auto-Loan Processing
After struggling with low automation accuracy and heavy manual review in loan applications, this leading European bank used Maisa to revolutionize intelligent document processing and boost customer satisfaction.
98%
accuracy
in personal document data extraction
3X
faster
loan application processing
85%
reduction
in manual review requirements
Industry
Banking & Financial Services
Department
Banking & Financial Services, Consumer / Retail Banking
The Challenge
This global bank processes thousands of customer documents daily for account openings, loan applications, and compliance. Accuracy, speed, and security are critical in maintaining customer trust and meeting strict regulations.
Before Maisa, the bank relied on OCR, RPA, and machine learning tools. While these reached ~75% field-level accuracy, they achieved only 30% full-document success rates, since one wrong field invalidated the entire document. Staff had to manually review everything, slowing down loan activation, raising costs, and eroding confidence in AI solutions.
The bank then tried GPT-based projects, which improved accuracy to ~80%, but this was still far from the target for scale. Manual review remained high, preventing efficiency gains and delivering little ROI despite major investments.
The outcome: loan applications slowed to days instead of minutes, customers faced errors and resubmissions, and back-office staff were overloaded with document checks.
The Solution
The bank’s goal with Maisa was ambitious: achieve 80–90% success rates for fully automated loan document activations, with minimal manual intervention.
Their compliance and operations teams onboarded Maisa Digital Workers themselves using natural language instructions — no coding required. Configured to follow the bank’s procedures and rules, the Digital Workers now handle the process end-to-end:
Document Intake
Receives customer documents from multiple channels, regardless of format or quality, even low-quality scans.
Intelligent Classification
Analyzes structure, layout, and patterns to classify documents with 98% accuracy, across 40+ languages and formats.
Context-Aware Extraction
Extracts unstructured data aligned with business rules and compliance needs, with every field fully traceable for audit.
Structured Data Output
Formats results into standardized JSON with consistent field names and validation rules, flagging anomalies automatically.
Quality Assurance
Cross-field validation ensures data consistency, with hallucination-resistant Digital Workers logging every step in verifiable code for full auditability.
By combining automation with human feedback loops, the system continuously learns and improves, giving teams confidence to scale.
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