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Visa’s AI edge: How RAG-as-a-service and deep learning are strengthening security and speeding up data retrieval

Visa’s AI edge: How RAG-as-a-service and deep learning are strengthening security and speeding up data retrieval

  • 17.03.2025 08:30
  • venturebeat.com
  • Keywords: fraud prevention, fraud detection, AI in payments, deep learning, RAG-as-a-service, fraud reduction

Visa leverages RAG-as-a-service and deep learning to enhance security, accelerate data retrieval, and streamline policy queries across 200+ countries. Their Secure ChatGPT, running internally on Azure, ensures data privacy while offering multiple AI models for tailored use cases. Additionally, Visa deploys advanced fraud detection systems using deep learning to block $40 billion in fraudulent transactions annually.

Alphabet ProductsVsentiment_satisfied

Estimated market influence

Visa

Visa

Positivesentiment_satisfied
Analyst rating: Buy

Adopted RAG-as-a-service and deep learning to enhance security, speed up data retrieval, and reduce fraud. Their AI initiatives have led to significant improvements in operations and fraud prevention.

VentureBeat

Neutralsentiment_neutral
Analyst rating: N/A

Featured Visa's advancements in AI and payment security.

Microsoft Azure

Positivesentiment_satisfied
Analyst rating: N/A

Used by Visa to secure their AI models, ensuring data remains within internal systems.

GitHub Copilot

Neutralsentiment_neutral
Analyst rating: N/A

Assists Visa developers in coding and testing, enhancing code coverage.

OpenAI

Neutralsentiment_neutral
Analyst rating: N/A

One of the models available through Visa's Secure ChatGPT service.

Mistral

Neutralsentiment_neutral
Analyst rating: N/A

A smaller open-source model used by Visa for specific AI needs.

Anthropic Claude

Neutralsentiment_neutral
Analyst rating: N/A

One of the models available through Visa's Secure ChatGPT service.

Meta Llama

Neutralsentiment_neutral
Analyst rating: N/A

One of the models available through Visa's Secure ChatGPT service.

Google Gemini

Neutralsentiment_neutral
Analyst rating: N/A

One of the models available through Visa's Secure ChatGPT service.

IBM Granite

Neutralsentiment_neutral
Analyst rating: N/A

One of the models available through Visa's Secure ChatGPT service.

Context

Analysis of Visa's AI-Driven Business Strategy

Key Business Insights and Market Implications

1. RAG-as-a-Service for Policy Queries

  • Visa leverages Retrieval-Augmented Generation (RAG) to quickly retrieve and cite policy-related information across its 200+ global markets.
  • Impact: Reduces manual data retrieval time from days to minutes, improving operational efficiency.

2. Secure ChatGPT Implementation

  • Visa developed an internal AI model ("Secure ChatGPT") running on Microsoft Azure to address employee demand while ensuring data security.
  • Features:
    • Operates behind a firewall with data loss prevention (DLP) measures.
    • Uses multiple AI models (e.g., GPT, Mistral, Claude).
    • Provides developers access to GitHub Copilot for coding assistance.
  • Impact: Balances innovation and security, reducing the risk of "shadow AI" usage.

3. Four-Layer Data Infrastructure

  • Visa's tech stack is structured as a four-layered "birthday cake":
    1. Layer 1: Data-platform-as-a-service with a data lake (hundreds of petabytes).
    2. Layer 2: Data-as-a-service for fast data access across applications.
    3. Layer 3: AI/ML ecosystem for model testing and bias mitigation.
    4. Layer 4: Product development layer for employee and client solutions.
  • Investment: $3 billion over the last decade.
  • Impact: Enhances scalability, security, and efficiency in data management.

4. Fraud Prevention with AI

  • Visa uses deep learning models (e.g., transformer-based neural networks) to detect and prevent fraud in real-time.
  • Tools:
    • Deep authorization tool for card-not-present transactions.
    • Account-to-account payment protection with instant risk scoring.
  • Results: Blocked $40 billion in fraud in 2024 alone.
  • Synthetic Data: Used to simulate and predict emerging fraud patterns.
  • Impact: Strengthens trust and security, a critical factor for financial institutions.

5. Competitive Dynamics

  • Visa's multi-model AI approach provides flexibility and scalability, potentially giving it an edge over competitors relying on single models or public AI services.
  • Market Implication: Could drive other financial institutions to adopt similar internal AI solutions to enhance security and efficiency.

6. Long-Term Effects and Regulatory Impact

  • Visa's focus on secure, internally developed AI models reduces reliance on third-party services, minimizing regulatory risks associated with data breaches.
  • Regulatory Considerations: Compliance with global financial regulations is strengthened through localized policy enforcement and real-time fraud detection.

7. Strategic Considerations

  • Visa's investment in AI infrastructure positions it as a leader in leveraging advanced technologies for payments and security.
  • Future Outlook: Continued innovation in AI-driven tools will likely enhance Visa's ability to scale globally while maintaining high standards of security and compliance.

This analysis highlights how Visa's strategic adoption of AI, particularly RAG-as-a-service and secure internal models, is transforming its operations. The focus on data infrastructure, fraud prevention, and competitive differentiation underscores the company's commitment to innovation and market leadership in the payments sector.