What challenges did Finastra face before AI implementation?

Low rate of successful cloud migration among banks (only 31%). Significant barriers to cloud adoption due to compliance, legacy systems, and fragmented data sources. Need to increase productivity, reduce repetitive tasks, and improve customer engagement. Difficulty integrating AI with existing IT and data infrastructures. Pressure to accelerate time-to-market and drive innovation amid regulatory constraints.

Source

MicrosoftEvidence: Low35/100

Financial Institutions Boost Productivity and Compliance with Generative AI Agents

Use case typeRisk assessmentUpdated Jun 13, 2026

Microsoft, together with technology partners such as Finastra, KX, LSEG, and Personetics, is accelerating AI transformation in the financial services sector through the deployment of generative AI agents. These agents are deeply integrated within core banking and financial services workflows, automating repetitive tasks, supporting real-time data analysis, improving compliance, and driving strategic decision-making. Using Microsoft Cloud for Financial Services, Azure AI Foundry, Copilot Studio, Microsoft 365 Copilot, and Microsoft Fabric, financial organizations are overcoming legacy technology barriers, unifying fragmented data sources, and securely moving workloads to the cloud. Partners provide industry-specific solutions, such as intelligent meeting preparation, automated compliance checks, and customer insight generation. As a result, these technologies have enhanced employee productivity by up to 30% and increased revenue by an estimated 6%. The case highlights the collaborative role of multiple partners, the lowering of compliance obstacles, and the acceleration of cloud and AI adoption.

Organization: Finastra
Industry: Finance
Location: Global
Published: December 2024

Reported outcomes

Revenue: Up to 6% higher

Revenue & growth

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Productivity: 22%

microsoft.comDec 12, 2024Blog postInferred claimLow evidence strength

Productivity improvements between 22% and 30% across organizations adopting the solution.

Normalized claim

Productivity: 30%

microsoft.comDec 12, 2024Blog postInferred claimLow evidence strength

Productivity improvements between 22% and 30% across organizations adopting the solution.

Normalized claim

Revenue: 6% increase

microsoft.comDec 12, 2024Blog postInferred claimLow evidence strength

Reported up to 6% increase in revenue.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Finastra, KX, LSEG, Personetics
Provider: Microsoft
Maturity: Unknown
Linked source: microsoft.com

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 4

1Generative AI Agents for Financial Services Operations
2Automated Meeting Preparation and Compliance Analysis
3AI-driven Customer Insight and Risk Assessment

Low rate of successful cloud migration among banks (only 31%).
Significant barriers to cloud adoption due to compliance, legacy systems, and fragmented data sources.
Need to increase productivity, reduce repetitive tasks, and improve customer engagement.
Difficulty integrating AI with existing IT and data infrastructures.
Pressure to accelerate time-to-market and drive innovation amid regulatory constraints.

Implementation of Microsoft Cloud for Financial Services to meet compliance and security standards.
Software agents and generative AI assistants built using Azure AI Foundry, Copilot Studio, and Microsoft 365 Copilot.
Data integration and analytics with Microsoft Fabric for unified, AI-ready data access across clouds.
Partner-driven industry-specific solutions for meeting prep, customer insights, compliance, and fraud detection.

Technologies

Microsoft Cloud for Financial Services Azure AI Foundry Microsoft 365 Copilot Copilot Studio Microsoft Fabric

Reported up to 6% increase in revenue.

Architecture

Generative AI agents are developed and deployed by Microsoft partners on Microsoft Cloud for Financial Services. These agents integrate with Azure AI Foundry for AI model development and orchestration, Copilot Studio for agent creation and workflow automation, and leverage Microsoft 365 Copilot for user productivity scenarios. Microsoft Fabric unifies on-premises and multi-cloud data sources, making data accessible for AI workloads and partner application integration. Integration points include core banking systems, compliance/risk analysis modules, Teams apps such as Financial Meeting Prep, and advanced analytics workflows.

Sources & evidence1

Evidence: Low35/100Evidence strength