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Coforge: Where AI engineering meets industry expertise.

Learn about our company, our vision and values, and the 45,000+ professionals enabling businesses to harness the power of AI.

Learn more about Coforge
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Banking & Financial Services
Insurance
Travel
Healthcare & Life Sciences
Public Sector
Hi-Tech
Telecom, Media, Automotive, and Energy
Retail, Consumer Packaged Goods (CPG), and Hospitality

Capabilities

Engineering Enterprise Platforms: Pega Data Enterprise Platforms: Appian Cloud Enterprise Platforms: Salesforce Quality Engineering Enterprise Platforms: ERP AI-Led Business Process Solutions Enterprise Platforms: MuleSoft Experience Global Capability Centers

Who We Are

About Us Newsroom Leadership Partners Locations Careers Awards & Recognitions ESG Learn more about Coforge
Coforge: Where AI engineering meets industry expertise.

Learn about our company, our vision and values, and the 45,000+ professionals enabling businesses to harness the power of AI.

Learn more about Coforge
Composable Enterprise

Resources

Case Studies Conversation with Pathbreakers Blogs White Papers Tech PoV and Insights

Resources

Case Studies
Conversation with Pathbreakers
Blogs
White Papers
Tech PoV and Insights

Coforge Trust AI: Responsible AI and Compliance

Responsible AI that satisfies regulators and auditors, not just design documentation.

The Problem

 

"We operate in a regulated environment, financial services, insurance, and healthcare, where one unexplained AI decision, one bias incident, or one audit finding can freeze the entire programme. The risk isn't the AI. It's the absence of a governance architecture that can prove, under examination, that the AI is controlled, monitored, and fair."

 

 

 

What Trust AI Does

Built-In Guardrails
Responsible AI controls are embedded in the system architecture from day one, not compliance documentation produced after deployment to satisfy an audit already underway.
Continuous Bias Monitoring
Real-time detection of output bias across protected characteristics surfaced to operations teams, not discovered in annual reviews or regulatory examinations.
Explainability for Regulated Decisions
Every AI-assisted decision comes with a traceable rationale that satisfies both technical reviewers and regulatory examiners, including SR 11-7, the EU AI Act, and fair lending requirements.
Audit-Ready Evidence Trail
A structured, complete record of model behavior, decision logic, and governance controls, ready for examination, not assembled under time pressure when the request arrives.

How Trust AI Makes an Impact

 

AI deployment in high-stakes environments where credit, claims, hiring, and clinical decision support become defensible. Not because the AI is perfect, but because the governance architecture demonstrates it is controlled, monitored, and explainable to the standard regulators actually require.

Regulatory Frameworks Addressed

 

  • SR 11-7 (model risk management)

  • EU AI Act (high-risk AI obligations)

  • Fair lending (adverse action explainability)

  • DORA (operational resilience)

 

 

 

How To Tell if Trust AI is the Right Solution

Regulatory scrutiny on AI decisions
Regulators or auditors have asked, or are expected to ask, for evidence of how AI-assisted decisions are governed and explained under SR 11-7 or equivalent frameworks.
Unexplainable model outputs
AI systems producing decisions that operations or legal teams cannot explain to customers, examiners, or counsel with sufficient confidence and traceability.
EU AI Act compliance exposure
High-risk AI use cases in scope for EU AI Act obligations, with no current architecture to demonstrate conformity or generate required technical documentation.