Client Overview & Impact
Unzer, a leading German payments company and a part of KKR’s investment portfolio, specializes in providing seamless, integrated payment solutions to businesses across Europe. To maintain its stronghold in the competitive payments industry, Unzer needed to streamline its risk assessment process for merchants by improving the speed and accuracy of financial statement analysis.
Business Challenges
Unzer faced several operational challenges that impeded the efficiency of its merchant onboarding and risk assessment workflows:
Manual Financial Statement Processing
- Risk assessments required detailed analysis of merchant-provided financial statements, which were processed manually.
- This led to a prolonged turnaround time (TAT) and limited scalability.
Extended Time for Merchant Risk Assessment
- The average processing time for reviewing and analyzing financial statements was approximately 4 hours per document, delaying the overall onboarding process.
Multilingual Document Handling
- Unzer operates across multiple European markets, necessitating the handling of financial statements in English, German, Danish, and Swedish.
- This linguistic diversity added complexity to the document processing workflow.
Consistency and Accuracy Issues
- Ensuring consistent and accurate analysis across different languages and document structures was a significant challenge, impacting the reliability of risk assessments.
Glib.ai’s Solution: Comprehensive Automation for Financial Statement Analysis
Glib.ai implemented a tailored solution that automated and streamlined Unzer’s financial statement analysis, incorporating AI-driven IDP (Intelligent Document Processing) and Natural Language Processing (NLP) for multilingual support. Key components of the solution included:
Automated Data Extraction and Classification
- Glib.ai’s technology automatically extracted key financial data points from statements, such as revenue, profit margins, and cash flow, regardless of document format or language.
- This step reduced human intervention and ensured consistency.
Multilingual Processing Capabilities
- The system was equipped to handle documents in English, German, Danish, and Swedish, enabling seamless analysis across Unzer’s diverse European markets.
- Advanced NLP algorithms ensured that the contextual meaning of financial terms was preserved and accurately processed in each language.
Drastic Reduction in Processing Time
- By automating the data extraction and validation process, Glib.ai brought the processing time down from 4 hours to just 10 minutes per financial statement, accelerating the entire risk assessment workflow.
Enhanced Data Validation and Accuracy
- Integrated cross-referencing mechanisms validated extracted data against internal and third-party databases to ensure high accuracy and reliability.
- The automated system flagged inconsistencies or high-risk entries, allowing the risk team to prioritize manual reviews only when necessary.
Custom Reporting and Output Formats
- The extracted data was output in JSON, CSV, and Excel formats, facilitating integration with Unzer’s internal systems for further analysis and decision-making.
Solution Workflow
Diagram Description:
Detailed Workflow:
- Document Upload: Merchants submit financial statements in various languages via a secure portal.
- Automated Extraction: Glib.ai’s IDP technology extracts critical data points from financial statements.
- Multilingual Processing: NLP algorithms ensure accurate data interpretation across English, German, Danish, and Swedish.
- Data Validation: AI cross-references data to identify potential risks or inconsistencies.
- Report Generation: Data is formatted into custom reports for Unzer’s risk team.
- Quick Onboarding: Accelerated analysis supports faster merchant approvals and onboarding.
Results and Impact
The implementation of Glib.ai’s solution significantly improved Unzer’s operations:
Reduced Processing Time
- Brought the TAT for financial statement analysis down from 4 hours to 10 minutes, representing a 75% reduction in processing time.
Enhanced Operational Efficiency
- Freed up valuable resources, enabling the risk assessment team to focus on higher-priority reviews and strategic tasks.
Improved Multilingual Accuracy
- The automated system maintained consistent accuracy across multiple languages, reducing errors and ensuring uniform data interpretation.
Scalability
- The new workflow allowed Unzer to handle a higher volume of merchant applications without the need for proportional increases in staffing.
Better Risk Management
- Automated data validation flagged high-risk merchants promptly, improving the reliability of risk assessments and minimizing potential financial exposure.
Quantitative Impact Summary
| Metric | Pre-Glib.ai Implementation | Post-Glib.ai Implementation |
|---|---|---|
| Turnaround Time (TAT) | 4 hours | 10 minutes |
| Manual Effort Reduction | High dependency on FTEs | Over 75% reduction |
| Multilingual Processing | Limited, manual translations | Fully automated, multilingual |
| Data Accuracy | Variable, language-dependent | Consistent across languages |
| Merchant Onboarding Speed | Delayed due to long TAT | Accelerated, streamlined |
Conclusion
Glib.ai’s IDP and automation solutions provided Unzer with a faster, more efficient approach to financial statement analysis, significantly reducing processing time and improving multilingual data accuracy. By automating critical processes and integrating advanced NLP for language support, Unzer now offers a seamless and timely risk assessment process, solidifying its position as a leader in the payments industry and ensuring customer satisfaction through faster onboarding.
