What challenges did Temenos face before AI implementation?

Banks face rising incidents of financial crime, including fraud and money laundering Manual monitoring leads to delayed detection of suspicious transactions Traditional systems result in high operational costs and limited scalability COVID-19 pandemic stressed banking operations and required increased resiliency

What was the impact of the AI solution?

Over 50% reduction in total cost of ownership (TCO) for banks Significantly improved detection rates above industry averages Enabled swift deployment and operational resiliency during COVID-19 Enhanced effectiveness in fraud and money laundering prevention

Rank#2,021of 3,107

Source

MicrosoftLive source

Temenos supports banks in fighting financial crime through AI on Azure

Use case typeFraud detectionUpdated Jun 13, 2026

The partnership between Temenos and Microsoft leverages the security and scalability of Microsoft Azure to deliver AI-powered Financial Crime Mitigation (FCM) SaaS. This platform aids banks globally in their fight against financial misconduct such as fraud, money laundering, and sanctions violations. The solutions provided include precise transaction monitoring, sanction screening, and fraud prevention, all enabled by Azure’s robust infrastructure. The implementation is marked by over 50% savings in TCO and detection rates that far outperform industry averages. Designed to be swift and practical, it was especially valuable during the COVID-19 pandemic, promoting operational resiliency for many financial institutions.

Organization: Temenos
Industry: Finance
Location: Switzerland
Published: April 2020

Reported outcomes

−50%

costCost savings

Strategic outcomes

Risk & complianceImproved financial crime detection and preventionRisk & complianceDeployed AI-powered financial crime mitigation SaaSSpeed & agilityEnabled swift deployment and resiliencyScale & capacityLeveraged scalable global banking infrastructure

Catalog median for cost savings deployments: −45% across 345 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 4

1AI-driven transaction monitoring for detecting suspicious activities
2Automated sanction screening for real-time compliance
3Fraud prevention using AI and machine learning models

Banks face rising incidents of financial crime, including fraud and money laundering
Manual monitoring leads to delayed detection of suspicious transactions
Traditional systems result in high operational costs and limited scalability
COVID-19 pandemic stressed banking operations and required increased resiliency