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Banking & Financial Services

AI for banks.

Structured drafting goes to AI; judgment stays with your bankers. One governed platform meets banking-grade compliance, so every business group ships its own AI without rebuilding the platform underneath.

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Deployed in Production at.

A leading global bank (operating across Asia)

This bank, a multiple-time winner of top international banking awards, runs Dify Enterprise on Red Hat OpenShift across six business units.

The largest single deployment is in international banking: around 3,000 relationship managers use 80+ Dify workflows for customer dossiers, account plans, credit approvals, competitive analysis, and real-time business data access.

The platform runs across five environments (Dev, SIT, UAT, Test, Production), with release and governance standards aligned to the bank's core systems. Concurrent capacity was scaled from an initial 5 to over 50 through database tuning and a microservices architecture.

Read the full storyHow a Leading Global Bank Scaled AI Across Six Business Units on Dify Enterprise
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  • 6

    Business units on one Dify platform

  • 80+

    Production workflows in international banking alone, serving 3,000 RMs

  • 3,000

    Relationship managers using Dify workflows

  • 5

    Release environments (Dev→Prod), on par with core systems

Source: Dify Enterprise deployment at a leading global bank

Why
This Industry, Why Now.

The constraints

Banks operate inside three structural realities. Every product, pricing and customer interaction is regulated end to end, which means AI systems need the same governance and audit trails as any core banking system.

Banks also sit on top of deep existing IT estates — core banking platforms, trading systems, CRM and KYC systems — that AI has to work with, not around.

And high-value decisions in banking carry real consequences: wrong credit, wrong advice, wrong risk flag. Auditability, explainability and data residency are not optional requirements.

The role of AI

For this industry, the platform question is not whether to adopt AI. It is whether one governed platform can carry all the business lines while maintaining the controls each one requires.

Leading banks answer this by running Dify Enterprise as a shared foundation, with each business unit owning its own workspace and workflows.

Patterns We See.

Bank-wide platform, business-unit ownership
Instead of separate AI projects per business group, leading banks run one Dify Enterprise environment. Each business unit owns its own workspace, its own workflows, and its own data connections. The platform carries the governance, compliance controls, and deployment standards. Business units carry the application logic for the workflows they understand best.
High-frequency document workflows
Customer dossiers, account plans, credit memos, market commentary, research reports — banking involves high volumes of structured document work across all business lines. Dify workflows handle the drafting, summarisation, and extraction steps, so relationship managers and analysts can focus on judgment and client-facing work.
Banking-grade DevOps, self-hosted
Banks expect a full Dev, SIT, UAT, Test and Production ladder, equivalent to what they run for core systems. Dify Enterprise supports this deployment model, running entirely within the bank's own infrastructure with no data leaving the perimeter. Environment promotion follows the bank's own release management process.

Discuss Banking & Financial Services Workflows with Dify.

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