How a large bank saved thousands of hours of manual labor by automating credit agreement analysis
Company Size
1,000+
Region
- America
Country
- United States
Product
- Cortical.io SemanticPro
Tech Stack
- Natural Language Processing
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Natural Language Processing (NLP)
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Fraud Detection
- Regulatory Compliance Monitoring
Services
- Data Science Services
About The Customer
The customer is a Tier 1 bank in the US. The bank was exploring ways to improve efficiencies through automation. The bank’s goal was to find an appropriate extraction tool to help automate the risk assessment process and, provided the quality of results is sufficiently reliable, reduce the operational risks of wrong covenant assessments. The bank's VP Strategy & Emerging Technologies, Martha, was responsible for identifying areas where automation can improve process efficiencies in the organization. She continuously scouts new technologies to keep abreast of developments on the market and conducts internal interviews with line-of-business owners to assess optimization potentials. One of the areas identified for improvement was the wholesale credit department, headed by Rick, whose team spent a lot of time thoroughly reviewing financial information in order to assess credit risks.
The Challenge
The bank was looking for ways to improve efficiencies through automation, particularly in the area of credit risk assessment. The complexity in language and structure of wholesale credit agreements made automation difficult. These agreements are complex, 100+-page-documents that are very bespoke because banks use different lending systems. Especially covenants use highly specific, use-case related formulations. Some extractions are straightforward, and the values can be extracted “as is” (no inference necessary), for example: named dates, commitments, rates, parties. Others are more challenging and require fine-grained distinctions between options or covenants (eg redeem deal options, termination options). Here an automation system has to comprehend domain logic based on document structure and relations of extracted items. Pricing-related tables contain many different parameters (applicable margin, interest payment schedules, commitment tables) and it is extremely difficult to automate the extraction of pricing information. This is why the review of credit agreements was still done manually, which cost a lot of time and money to the bank, not mentioning the high error rate of such a mundane task.
The Solution
The bank leveraged the meaning-based capabilities of Cortical.io SemanticPro to extract key information from credit agreements and automate the classification of covenants. After having looked at the different solutions on the market, Martha decided to test Cortical.io natural language processing capabilities and apply them to the analysis of credit agreements. The main reason why she kicked-off a project with Cortical. io was the easiness and speed with which the system could be adapted to Rick’s use case: only a limited amount of example credit agreements (less than 50) was needed for training the system. This greatly limited both the initial investment and overall risk of this project. After an initial base knowledge phase where the system had learnt from relevant source material in an unsupervised manner, two loan officers from Rick’s team provided input by reviewing a small set of documents. After that, the system kept incorporating feedback and created more accurate models (continuous learning, aka human in the loop).
Operational Impact
Quantitative Benefit
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