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.

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Case Study —

Fortune 50 Bank Develops News Analytics Using Snorkel Flow

A Fortune 50 US bank needed to identify various companies' mentions by role and sentiment in unstructured news. Black-box applications and inflexible, low-performing models stunted the project. With Snorkel Flow, the bank built a news analytics application that achieved:

Speedup compared to hand-labeling
To develop the first custom ML model
Performance gain over legacy solution
Accuracy for contract classification

News articles labeled in minutes

Contracts processed in minutes

Read about News Analytics

Approach —

Data-Centric AI

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



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



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



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



Version and audit data like code, leading to more responsive and ethical deployments.



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



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.


Case Study —

Big Four US Bank Builds Financial Spreading using Snorkel Flow

A Big-4 US bank wanted to extract financial line items from company statements. With Snorkel Flow, the bank built financial spreading application with custom-trained ML models to parse textual data with spatial context and achieved:

Accuracy of extraction models
To develop the first custom ML model
More extractions
Accuracy for contract classification

Of hours of manual labeling time saved

Contracts processed in minutes

Read about Financial Spreading

Why Snorkel Flow —

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

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

Case Study —

Top US Bank Develops Contract Intelligence using Snorkel Flow

A top-3 US bank wanted to classify and extract contract information using a custom-trained model. With Snorkel, they developed a contract intelligence AI application with 99% accuracy in <24 hours.

Model accuracy achieved with Snorkel Flow
To develop the first custom ML model
Documents labeled in hours
Accuracy for contract classification
Develop the first custom ML model
Contracts processed in minutes

Read about Contract Intelligence

Platform —

Snorkel Flow

The only AI platform that lets you label data programmatically, train models efficiently, improve performance iteratively, and deploy applications rapidly.



Label & Build
Label and build training data programmatically in hours without months of hand-labeling


Integrate & Manage
Automatically clean, integrate, and manage programmatic training data from all sources


Train & Deploy
Train and deploy state-of-the-art machine learning models in-platform or via Python SDK


Analyze & Monitor
Analyze and monitor model performance to rapidly identify and correct error modes in the data