Industry
Client Overview
Our client is a major US-based payment processing company, handling over 100 million transactions annually for mid-sized e-commerce businesses. Their existing fraud detection system was based on a rigid, rule-based engine that was struggling to keep up with sophisticated fraud patterns. The system generated a high number of false positives, frustrating legitimate customers, while simultaneously failing to catch new, emerging threats, leading to significant chargeback losses. They needed a dynamic, intelligent solution that could adapt in real time and protect their merchants and their own bottom line.
Client Testimonial
"The AI platform CIS built for us has been nothing short of transformative. It's not just a tool; it's a core part of our business's defense system. We've reduced chargeback losses by 60% and, just as importantly, cut our false positive rate by half. Their team's expertise in both AI and secure financial systems was evident from day one. They are a CMMI Level 5 partner in every sense of the word." - VP of Risk Management, FinPay Solutions
Problem
The client's legacy system could not distinguish between a genuine customer making an unusual purchase and a fraudster using stolen credentials. This led to a lose-lose situation: blocking good customers and allowing bad transactions through, resulting in eroding merchant trust and millions in annual losses.
Key Challenges
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01
High False Positives : The rigid rules engine frequently flagged legitimate transactions, leading to a poor customer experience and lost revenue.
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02
Inability to Detect New Threats : The system was blind to new fraud techniques until rules were manually updated, which was always a step behind the criminals.
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03
Scalability Issues : The existing platform could not handle peak transaction volumes, leading to processing delays.
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04
Lack of Actionable Insights : The system provided alerts but no context, making it difficult for risk analysts to make quick, informed decisions.
Our Solution
CIS was engaged to design, develop, and deploy a custom, AI-powered fraud detection platform from the ground up. Our solution was a microservices-based system that could analyze transactions in real-time, scoring them for fraud risk based on hundreds of data points.
Real-Time Anomaly Detection : We developed a suite of machine learning models (including isolation forests and autoencoders) that learned the "normal" behavior for each customer and merchant, allowing the system to instantly flag statistically significant deviations.
Predictive Modeling : We trained a gradient-boosted model on years of historical transaction data (both fraudulent and legitimate) to predict the likelihood of a new transaction being fraudulent.
Intelligent Data Enrichment : The platform integrated with third-party APIs to enrich transaction data in real-time with information like device fingerprinting, IP geolocation, and email address reputation.
Analyst Decision Dashboard : We built a web-based dashboard that presented a clear, intuitive view of high-risk transactions, including the specific factors that contributed to the risk score, empowering analysts to make faster, more accurate decisions.
Implementation & Execution
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Discovery & Architecture
We began with a 2-week workshop with the client's risk and IT teams to map out existing pain points and design a future-state architecture on AWS.
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Data Engineering
Our team built a robust data pipeline using AWS Kinesis and S3 to ingest and process the real-time stream of transaction data.
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Model Development
Our data scientists experimented with multiple algorithms in a sandboxed environment, rigorously back-testing them for performance.
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MLOps Pipeline
We established a full MLOps pipeline using AWS SageMaker and GitLab CI/CD to automate model training, deployment, and monitoring.
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Secure Infrastructure
The entire solution was built on a secure, multi-account AWS infrastructure following ISO 27001 and SOC 2 best practices.
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Phased Rollout
We first deployed the model in a "shadow mode" to monitor its predictions without blocking transactions, allowing us to fine-tune it before a full, live deployment.
Positive Outcome
The new platform delivered a significant and measurable impact on the client's business within six months of going live.
1. 60% Reduction in Chargeback Losses
The AI's superior accuracy in catching fraud led to a dramatic decrease in financial losses, saving the company an estimated $4.2 million annually.
2. 50% Decrease in False Positives
Legitimate customers experienced a much smoother checkout process, improving customer satisfaction and reducing cart abandonment.
3. 90% Faster Review Times
The analyst dashboard provided all the necessary context in one place, reducing the average time to review a flagged transaction from 10 minutes to under a minute.
4. Proactive Threat Detection
The system now automatically identifies and adapts to new fraud patterns without manual intervention, keeping the client ahead of emerging threats.
Why Choose Us
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Deep FinTech Expertise
We understood the nuances of payment processing and risk management.
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Verifiable Process Maturity
Our CMMI Level 5 approach ensured a high-quality, predictable outcome.
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Security-First Mindset
We built the platform to meet the stringent security demands of the financial industry.
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Full-Stack AI Team
We provided everything from data engineering to MLOps and front-end development.
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Focus on Business Metrics
Our goal was not just to build a model, but to reduce chargebacks and false positives.
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Transparent Collaboration
The client had full visibility into our progress through shared Jira boards and weekly demos.
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Scalable Cloud Architecture
The solution was built to scale as the client's transaction volume grows.
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100% In-House Talent
The client's sensitive financial data was handled exclusively by our vetted employees.
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Long-Term Partnership
We continue to provide ongoing maintenance and model retraining services.
Conclusion
By partnering with CIS, the client replaced an outdated, reactive system with a proactive, intelligent fraud detection platform. This not only saved them millions in losses but also created a more secure and trusted ecosystem for their merchants, turning a major cost center into a competitive advantage.