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Case Studies > Top 3 US bank Leverages AI and NLP to streamline financial document processing

Top 3 US bank Leverages AI and NLP to streamline financial document processing

Technology Category
  • Analytics & Modeling - Natural Language Processing (NLP)
  • Analytics & Modeling - Machine Learning
  • Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
  • Finance & Insurance
Applicable Functions
  • Business Operation
  • Quality Assurance
Services
  • Software Design & Engineering Services
  • System Integration
  • Training
The Challenge
Analysts at this top 3 US bank spend hundreds of hours a year manually reviewing financial documents to find information on interest rate swaps. This manual process is time-consuming and takes away from their ability to assist customers proactively. The team recognized the potential of using AI and NLP to streamline 10-K processing but lacked the training data required to train a model that could automatically identify and extract interest rate swaps from 10-Ks accurately across multiple formats.
About The Customer
The customer is a top 3 US bank, which is a major financial institution with a large team of financial analysts. These analysts are responsible for reviewing financial documents, such as 10-Ks, to find specific information like interest rate swaps. The bank is focused on improving operational efficiency and leveraging advanced technologies to enhance their services. With a significant number of employees and a vast amount of financial data to process, the bank is looking for innovative solutions to reduce manual workload and improve accuracy in data extraction.
The Solution
The bank leveraged programmatic labeling and weak supervision to encode analyst expertise as labeling functions (LFs). This approach allowed them to train a custom NLP model that could automatically identify and extract interest rate swaps from 10-Ks. The model achieved an F1 score of 83 in just a few weeks. By using Snorkel Flow, the team was able to generate 70,000 labels per minute programmatically, significantly speeding up the training process. This solution not only reduced the time spent on manual document review but also improved the accuracy and consistency of the extracted data.
Operational Impact
  • The implementation of the custom NLP model saved over 2000 hours per year for financial analysts, allowing them to focus on more value-added tasks.
  • The use of Snorkel Flow enabled the team to generate labels programmatically at a rate of 70,000 labels per minute, drastically reducing the time required for model training.
  • The solution was developed and deployed in just six weeks, demonstrating the efficiency and effectiveness of the approach.
Quantitative Benefit
  • 2000+ hours per year saved for financial analysts.
  • 6 weeks to build a production-quality AI application.
  • 70,000 labels per minute programmatically generated via Snorkel Flow.

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