Industry
Client Overview
A top-10 US-based global bank with over $100 billion in assets. The client was struggling with an outdated, rule-based fraud detection system that was generating an unsustainable volume of false positive alerts. Their team of over 200 analysts was overwhelmed, leading to high operational costs, analyst burnout, and the risk of missing genuine fraudulent transactions. They needed a modern, intelligent solution that could integrate with their complex legacy systems and scale across millions of daily transactions.
Client Testimonial
"The AI platform CIS built has fundamentally changed our approach to financial crime. We're not just reacting to alerts anymore; we're proactively identifying threats we never would have seen before. The 60% reduction in false positives freed up our best analysts to focus on complex investigations. This project was a masterclass in execution, delivered on time and with a level of professionalism and security that exceeded our expectations." - VP, Financial Crime Compliance
Problem
The client's existing fraud detection engine was producing over 10,000 alerts per day, with more than 95% being false positives. This inefficiency was costing them millions annually in wasted analyst hours and was creating significant compliance risk.
Key Challenges
-
01
System Integration : The new solution had to securely pull real-time data from a 20-year-old mainframe core banking system.
-
02
Data Quality : Transaction data was stored in multiple, inconsistent formats across different silos.
-
03
Scalability & Performance : The system needed to process over 50 million transactions daily with sub-second latency for real-time decisioning.
-
04
Explainability : Regulators required that all AI-driven decisions be explainable and auditable.
Our Solution
CIS deployed a cross-functional DevSecOps Automation Pod and an AI/ML Rapid-Prototype Pod to design, build, and deploy a custom, real-time fraud detection platform.
Secure Data Fabric : We built a secure API gateway to extract and unify data from the legacy mainframe, cleansing and transforming it into a consistent format for the AI models.
Hybrid AI Model : We developed a hybrid AI engine combining unsupervised learning (to detect novel anomalies) and supervised learning (trained on the bank's historical fraud data) for maximum accuracy.
Cloud-Native Architecture : The solution was built on AWS using a scalable, microservices-based architecture to handle the massive transaction volume and ensure high availability.
Explainability Dashboard : We created a custom dashboard for compliance officers that provided a clear, human-readable explanation for every decision the AI model made, ensuring full regulatory compliance.
Implementation & Execution
-
Phase 1 (Weeks 1-4)
Conducted deep-dive workshops and a 2-week paid trial to build a proof-of-concept, validating the data connection to the mainframe.
-
Phase 2 (Weeks 5-12)
Developed the secure data pipeline and began training the initial set of unsupervised learning models.
-
Phase 3 (Weeks 13-20)
Built the core supervised learning models and the explainability dashboard, running the new system in parallel with the old one (in "shadow mode").
-
Phase 4 (Weeks 21-26)
Fine-tuned the models based on real-world performance, significantly reducing the false positive rate.
-
Phase 5 (Week 27)
Deployed the system in a phased rollout, starting with one product line and expanding across the enterprise.
-
Phase 6 (Ongoing)
Established an MLOps pipeline for continuous monitoring, retraining, and governance of the AI models, managed by our Maintenance & DevOps POD.
Positive Outcome
1. 60% Reduction in False Positives
Freed up an estimated 80,000 analyst hours annually, resulting in multi-million dollar operational savings.
2. Increased True Positive Rate
The system identified 15% more genuine fraud cases than the legacy system in its first six months of operation.
3. Regulatory Approval
The explainability dashboard was praised by internal auditors and external regulators for its transparency.
4. Scalable Platform
The new platform is now being extended to cover AML and insider trading use cases, providing even greater ROI.
Why Choose Us
-
CMMI Level 5 Process
Ensured the project was delivered on a predictable schedule and budget, critical for a large enterprise.
-
ISO 27001/SOC 2 Security
Our certified security posture gave the client confidence in handling their most sensitive data.
-
Legacy Modernization Skills
Our unique ability to integrate with their decades-old mainframe was the key technical enabler.
-
100% In-House Team
Ensured a stable, accountable team for the duration of this mission-critical project.
-
Full IP Transfer
The bank owns 100% of the powerful new AI platform we built for them.
-
Deep FinTech Domain Knowledge
We understood the nuances of financial crime compliance from day one.
-
Expert AI/ML Talent
Our team possessed the specialized skills to build a sophisticated, hybrid AI model.
-
Risk-Free Trial
The initial 2-week trial proved our capabilities and de-risked the engagement for the client.
-
End-to-End Ownership
We handled everything from initial architecture to ongoing MLOps, providing a true turnkey solution.
Conclusion
This project demonstrates CIS's ability to solve complex, enterprise-scale challenges in the banking sector. By combining deep technical expertise in AI and legacy modernization with a world-class, secure delivery process, we were able to deliver a solution that provided massive operational savings, strengthened compliance, and created a platform for future innovation.