A leading North American financial institution with a strong international footprint spanning 34 countries, faced a critical challenge—its fraud detection system could no longer keep up with the demands of modern, real-time banking. 

In financial services, milliseconds matter. Every delay risks failed transactions, fraud exposure, SLA breaches, and regulatory penalties. For this global bank, maintaining customer trust meant ensuring payments moved quickly, securely, and without disruption. 

Read the full case study here. 

The Challenge: Outdated fraud detection in a real-time world 

The bank’s legacy fraud detection platform struggled under massive and growing transaction volumes. Complex data hops meant transactions could disappear mid-flow, and identifying these issues required hours—sometimes days—of manual effort. 

Key pain points included: 

  • Transaction failures undetected until customers complained (“Where is my payment?”) 
  • Limited visibility into payment and fraud systems 
  • SLA breaches due to slow Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR) 
  • High operational cost and risk of regulatory non-compliance 
  • Manual triaging of lost transactions causing delays in customer resolution 

The bank needed a modern, intelligent, and proactive approach to payment monitoring and fraud detection. 

The Solution: AI-powered resiliency with ignio™ AIOps 

Digitate’s ignio AIOps platform delivered end-to-end visibility, intelligence, and automation across the bank’s payments and fraud detection ecosystem. 

By creating a digital blueprint of the FraudIT engine—spanning infrastructure, application, and data feed layers—ignio enabled: 

  • Continuous monitoring of 10M+ daily transactions 
  • Instant alerts for lost transactions or anomalies 
  • Proactive health checks to detect potential failures before impact 

ignio also monitored critical back-office data flows and ATM/POS networks with unmatched precision: 

  • 3.6B+ in-motion transactions tracked annually 
  • 6M ATM/POS transactions handled monthly 
  • 250+ data sources and 3000+ business-critical files monitored continuously 

The Outcome: Faster detection, higher availability, improved trust 

With ignio AIOps in place, the bank transformed its operational resiliency and customer experience: 

Metric 

Outcome 

Mean Time to Detect (MTTD) 

95% faster detection of anomalies 

SLA Compliance 

100% improvement in capturing missing transactions 

Mean Time to Resolve (MTTR) 

83% faster resolution of application failures 

ATM/POS error detection time 

97% reduction 

Feed monitoring efficiency 

8,000+ hours/year saved 

Server-related issues 

40% reduction, improving app availability 

 

Business value unlocked 

  • Uninterrupted payments: Transactions monitored and recovered before impacting customers 
  • Regulatory compliance: No SLA breaches for missing transactions post-deployment 
  • Reduced operational load: Automation cut thousands of manual investigation hours 
  • Customer trust restored: Faster resolution and fewer payment failures 

Key takeaways

This leading financial institution didn’t just modernize its fraud detection—it built a resilient digital foundation for future growth. With ignio AIOps, it moved from reactive firefighting to proactive, AI-driven payment assurance, ensuring that every transaction counts. 

Ready to modernize your IT operations? Schedule a demo with us today. 

Read the full case study here. 

FAQs: AI in Banking Resiliency and Fraud Detection

Why is real-time payment monitoring important in banking?

In modern banking, delays in detecting transaction failures can cause financial loss, regulatory penalties, and customer dissatisfaction. Real-time monitoring ensures proactive detection and resolution. 

ignio™ uses AI and automation to detect anomalies, recover lost transactions instantly, and prevent failures before they occur, reducing downtime and risk. 

They identify early warning signs in payment and fraud systems, enabling fixes before customers are impacted. 

In this case, ignio™ saved over 8,000 hours annually by automating transaction monitoring and resolution workflows. 

Yes AI-powered systems can detect and resolve incidents faster, significantly reducing SLA breaches and improving overall service availability.