AI for banking
From customer experience to cybersecurity, Snorkel Flow provides banking innovators with a data-centric platform to build custom AI applications powered by programmatic data labeling.
Case study
Top U.S. bank
A top U.S. bank uses Snorkel Flow to quickly build AI applications that classify and extract information from their documents.
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Problem
The bank estimated that, for a time-sensitive use case, hand-labeling data would take over a month
99.1%
Snorkel Flow accuracy
Solution
With Snorkel Flow, the team produced a solution that was over 99% accurate in under 24 hours.
<24hrs
from problem start
Results
The resulting AI application could be quickly and easily adapted to new problems and business lines.
>250k
documents processed
Data-centric AI
Snorkel AI is leading the shift from model-centric
to data-centric AI development to make AI practical.
Accelerated
Save time and costs by replacing manual labeling with rapid, programmatic labeling.
Adaptable
Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets.
Collaborative
Incorporate subject matter experts' knowledge by collaborating around a common interface–the data needed to train models.
Accurate
Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data.
Governable
Version and audit data like code, leading to more responsive and ethical deployments.
Private
Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators.
Use cases
AI solutions for banking
AI applications built using Snorkel Flow can boost revenues through increased personalization for customers and employees, and lower costs through efficiencies generated by higher automation, reduced errors rates, and better resource utilization.
Contract intelligence
Identify, extract, and organize custom data from complex contracts to reduce manual operations and improve workflows.
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News analytics
Extract entities, events, and relationships from news to provide analytics for sentiment, summarization, topics, and trading signals.
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Financial Spreading
Identify, extract, and organize custom data from complex contracts to reduce manual operations and improve workflows.
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Credit Approval
Extract entities, events, and relationships from news to provide analytics for sentiment, summarization, topics, and trading signals.
Conversational AI
Identify, extract, and organize custom data from complex contracts to reduce manual operations and improve workflows.
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Cyber risk management
Extract entities, events, and relationships from news to provide analytics for sentiment, summarization, topics, and trading signals.
Compliance monitoring
Extract entities, events, and relationships from news to provide analytics for sentiment, summarization, topics, and trading signals.
Fraud detection
Extract entities, events, and relationships from news to provide analytics for sentiment, summarization, topics, and trading signals.
Customer service
Extract entities, events, and relationships from news to provide analytics for sentiment, summarization, topics, and trading signals.
Intelligent pricing
Extract entities, events, and relationships from news to provide analytics for sentiment, summarization, topics, and trading signals.
Know your customer (KYC)
Extract entities, events, and relationships from news to provide analytics for sentiment, summarization, topics, and trading signals.
A radically new approach to AI
Conventional AI approaches rely on generic third-party models, or brittle rule-based systems, or armies of human labelers. With Snorkel Flow, programmatically labeling unlocks a new workflow that accelerates AI app development.
With Snorkel Flow
Customize state-of-the-art models by training with your data & adapt to changing data or goals with a few lines of code.
Leverage cutting-edge ML to go beyond simple rules and retain the flexibility to audit and adapt.
Label thousands of data points programmatically in hours while keeping your data in-house and private.
With conventional approaches
Hand-labeled ML is hugely expensive, with usually no way to iterate, adapt, be privacy compliant, audit, or reuse.
Pre-trained vendor models often don’t work on your data, no way to customize, adapt, or audit.
Case study
Fortune 50 bank
In just weeks, a Fortune 50 bank achieved a 25+ point performance gain over a black box vendor solution for news analytics application with Snorkel Flow.
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Problem
The bank needed an accurate way to tag companies in unstructured news text, link them to identifiers (e.g., stock tickers), and classify mentions by sentiment and other aspects.
45x
faster compared to hand-labeling
Solution
The bank used Snorkel Flow to develop an AI-powered news analytics application that monitors target companies’ press coverage in unstructured data feeds.
+90
F1 score for news analytics application
Results
With Snorkel Flow, the team achieved a 25+ point performance gain over a legacy vendor system and internal heuristic approaches.
+25%
performance gain over black box vendor system
The platform for data-centric AI development
- Label programmatically to solve manual labeling bottlenecks
- Continuously update and analyze models to guide development
- Adapt to real-world changes with a few clicks, not manual relabeling
Are you ready to dive in?
Label data programmatically, train models efficiently, improve performance iteratively, and deploy applications rapidly—all in one platform.