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.

Request a demo

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.

Request a demo



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.
Read more



Problem

The bank estimated that, for a time-sensitive use case, hand-labeling data would take over a month.

Solution

With Snorkel Flow, the team produced a solution that was over 99% accurate in under 24 hours.

Results

The resulting AI application could be quickly and easily adapted to new problems and business lines.

99.1%

Snorkel Flow accuracy

<24hrs

from problem start

>250K

documents processed


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.
Read more



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


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.
Read More



Problem

The bank estimated that, for a time-sensitive use case, hand-labeling data would take over a month.

Solution

With Snorkel Flow, the team produced a solution that was over 99% accurate in under 24 hours.

Results

The resulting AI application could be quickly and easily adapted to new problems and business lines.

99.1%

Snorkel Flow accuracy

<24hrs

from problem start

>250k

# documents processed

Data-centric AI

Snorkel AI is leading the shift from model-centric
to data-centric AI development to make AI practical.
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Accelerated

Save time and costs by replacing manual labeling with rapid, programmatic labeling.
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Adaptable

Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets.
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Collaborative

Incorporate subject matter experts' knowledge by collaborating around a common interface–the data needed to train models.
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Accurate

Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data.
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Governable

Version and audit data like code, leading to more responsive and ethical deployments.
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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.
Credit approval
Predict credit-worthiness with fairness and precision using extensive data and organizational resources.
Cyber risk management
Investigate suspicious IP addresses or traffic patterns from network data to prevent cybersecurity breaches.
Compliance monitoring
Extract loan rates, credit scores, or other custom attributes from contracts, emails, reports, balance sheets, S1, and other sources to monitor compliance.
Fraud detection
Identify fraud and money laundering patterns by extracting client ID, IBAN number, and transaction details.
Customer service
Predict issues and route interactions to the right team or fine-tune IVR or chatbot responses.
Intelligent pricing
Complement traditional pricing models, enabling more accurate prediction and confidence intervals.
Know your customer (KYC)
Confirm customer identity to open more accounts, improve ACH success rates, and reduce fraud.

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

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Customize state-of-the-art models by training with your data & adapt to changing data or goals with a few lines of code.
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Leverage cutting-edge ML to go beyond simple rules and retain the flexibility to audit and adapt.
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Label thousands of data points programmatically in hours while keeping your data in-house and private.

With conventional approaches

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Hand-labeled ML is hugely expensive, with usually no way to iterate, adapt, be privacy compliant, audit, or reuse.
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Pre-trained vendor models often don’t work on your data, no way to customize, adapt, or audit.
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Rules-based approaches often don’t perform well on complex data or adapt easily to data or goal changes.


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.
Read more



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.

Solution

The bank used Snorkel Flow to develop an AI-powered news analytics application that monitors target companies' press coverage in unstructured data feeds.

Results

With Snorkel Flow, the team achieved a 25+ point performance gain over a legacy vendor system and internal heuristic approaches.

45x

faster compared to hand-labeling

+90

F1 score for news analytics application

+25%

performance gain over black box vendor system



Case Study

Fortune 50 Bank

A Fortune 50 bank achieved a 25+ point performance gain over a black box vendor solution for news analytics application with Snorkel Flow- in just a few weeks.
Read more



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%

point performance gain over black box vendor system


Case Study

Fortune 50 Bank

A Fortune 50 bank achieved a 25+ point performance gain over a black box vendor solution for news analytics application with Snorkel Flow- in just a few weeks.
Read More



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.

Solution

The bank used Snorkel Flow to develop an AI-powered news analytics application that monitors target companies' press coverage in unstructured data feeds.

Results

With Snorkel Flow, the team achieved a 25+ point performance gain over a legacy vendor system and internal heuristic approaches.

45x

faster compared to hand-labeling

+90

F1 score for news analytics application

+25%

point performance gain over black box vendor system

The platform for data-centric AI development

The platform for data-centric AI development

Dive in

[get_press_posts]
Press
Blog
Research
Case studies
Press
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March 21, 2022
Snorkel AI welcomes industry leaders to the team

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August 9, 2021
This hot startup is now valued at $1 billion for its A.I. skills

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February 24, 2021
The Data-First Enterprise AI Revolution

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July 14, 2020
Meet The Stanford AI Lab Alums That Raised $15 Million To Optimize Machine Learning

Blog
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February 4, 2022
Making Automated Data Labeling a Reality in Modern AI

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Date: Jan 25, 2022
The Principles of Data-Centric AI Development

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Date: Jan 5, 2022
Meet the Snorkelers

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Date: Jul 9, 2021
How to Use Snorkel to Build AI Applications

Research
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2022
Universalizing Weak Supervision

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2021
Ontology-driven weak supervision for clinical entity classification in electronic health records

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2017
Rapid Training Data Creation with Weak Supervision

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2016
Data Programming: Creating Large Datasets Quickly

Customer Stories
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February 26, 2022
Genentech used Snorkel Flow to extract information from clinical trials

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February 18, 2022
Google used Snorkel to build and adapt content classification models

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2019
Intel used Snorkel to accelerate sales and marketing agents

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2019
Apple built a Snorkel-based system to answer billions of queries in multiple languages

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The Future of

Data-Centric AI


August 3-4, 2022 | Virtual

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