Top U.S. bank uses Snorkel Flow for Rapid AI application Development
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- Snorkel Flow
Tech Stack
- AI
- Machine Learning
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Services
- Software Design & Engineering Services
- System Integration
About The Customer
The customer is a top U.S. bank, a major financial institution with a large-scale operation. The bank deals with a vast amount of documents daily, requiring efficient and accurate processing to maintain operational efficiency and compliance. As a leading player in the finance and insurance industry, the bank continuously seeks innovative solutions to enhance its business operations and customer service. The bank's commitment to leveraging advanced technologies like AI and machine learning underscores its dedication to staying at the forefront of digital transformation in the financial sector.
The Challenge
The bank faced a significant challenge in processing a large volume of documents for a time-sensitive use case. Hand-labeling the data required for this task was estimated to take over a month, which was not feasible given the urgency of the situation. The bank needed a solution that could expedite the data labeling process and enable the rapid development of AI applications to classify and extract information from their documents.
The Solution
To address the challenge, the bank implemented Snorkel Flow, an AI platform designed to streamline the development of machine learning models. Snorkel Flow enabled the bank to quickly build AI applications that could classify and extract information from their documents. The platform's ability to automate the data labeling process significantly reduced the time required to prepare the data for model training. This allowed the bank to develop and deploy AI applications in a fraction of the time it would have taken using traditional methods. The flexibility of Snorkel Flow also meant that the resulting AI applications could be easily adapted to new problems and business lines, providing a scalable solution for the bank's diverse needs.
Operational Impact
Quantitative Benefit
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