Industry
Client Overview
A US-based, Series A startup aiming to disrupt the B2B payments space. They had secured initial funding based on their vision but needed to build a robust, AI-powered fraud detection system to gain market trust and meet regulatory requirements. Their existing prototype was slow, inaccurate, and lacked the security features needed to handle sensitive financial data.
Client Testimonial
"CISIN didn't just build a model; they built a fortress. Their understanding of FinTech security requirements and their CMMI 5 process gave our investors and early customers the confidence they needed. We went from a risky prototype to a compliant, production-grade system in under four months." - Roger Sterling, CTO
Problem
The client needed to replace their proof-of-concept fraud detection system with a production-ready engine that was fast, highly accurate, and compliant with financial industry security standards like PCI-DSS and SOC 2.
Key Challenges
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01
Real-Time Performance : Transactions needed to be scored for fraud risk in under 100 milliseconds.
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Data Security : Handling highly sensitive financial data required a fortified, auditable infrastructure.
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Scalability : The system had to be able to handle a projected 100x increase in transaction volume over the next 18 months.
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04
Model Accuracy : The model needed to minimize false positives (which insult good customers) while catching sophisticated fraud patterns.
Our Solution
We deployed a dedicated FinTech Mobile Pod augmented with experts from our Cyber-Security Engineering Pod.
Secure Architecture : We designed and implemented a secure, multi-layered architecture on AWS, leveraging KMS for encryption and IAM for strict access control, meeting SOC 2 criteria from day one.
Custom ML Model : We developed a hybrid machine learning model combining a gradient-boosted tree for known patterns and a graph-based neural network to uncover new, collusive fraud rings.
MLOps Pipeline : We built a full MLOps pipeline using Kubeflow and MLflow for automated model retraining, deployment, and performance monitoring.
Data Engineering : A real-time data pipeline was constructed using Kafka and Spark Streaming to process and enrich transaction data for the model.
Implementation & Execution
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Phase 1 (Weeks 1-2)
Deep dive into requirements, architectural design, and setting up the secure AWS environment.
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Phase 2 (Weeks 3-6)
Data pipeline construction and initial model development and backtesting.
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Phase 3 (Weeks 7-10)
Model refinement, MLOps pipeline setup, and API development for integration.
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Phase 4 (Weeks 11-12)
Staging deployment, rigorous penetration testing, and performance load testing.
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Phase 5 (Week 13)
Production deployment with a canary release strategy.
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Phase 6 (Ongoing)
Continuous monitoring, model performance analysis, and scheduled retraining.
Positive Outcome
1. 99.8% Fraud Detection Rate
The new system successfully identified nearly all fraudulent transactions, a 40% improvement over the prototype.
2. Sub-80ms Latency
Average transaction scoring time was well below the 100ms target, ensuring a seamless user experience.
3. SOC 2 Compliance Achieved
The robust architecture and documentation were instrumental in the client's successful SOC 2 Type 1 audit.
4. Secured Series A
The demonstrable, production-grade AI system was a key factor in the client closing their $15M Series A funding round.
Why Choose Us
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Guaranteed Talent
Access our specialized FinTech Mobile and Cyber-Security Engineering Pods to handle highly sensitive data deployments.
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Process Maturity
Leverage our CMMI-5 certified workflows to move safely from an unpredictable prototype to a production-grade system in under 4 months.
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Full IP Ownership
Retain complete structural rights over your proprietary hybrid machine learning models and graph-based networks.
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Startup-Tuned Models
Deploy lightweight, high-performance algorithms optimized specifically to achieve sub-80ms transaction scoring latencies.
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Scalable In-House Teams
Build foundational infrastructure backed by a massive team capable of scaling alongside your 100x transaction volume spikes.
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Security at the Core
Ensure institutional market trust with multi-layered architectures that meet SOC 2 and PCI-DSS requirements from day one.
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20+ Years of Proof
Mitigate technical risk by using deeply battle-tested financial engineering processes rather than unproven freelance networks.
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Global Delivery, Local Touch
Collaborate through localized management while exploiting global delivery mechanics to close a $15M Series A funding round.
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Future-Proof Technology
Maintain long-term system integrity with fully automated Kubeflow and MLflow MLOps data retraining pipelines.
Conclusion
By partnering with CISIN, the startup was able to leapfrog the typical development cycle, building an enterprise-grade, secure, and scalable AI system on a startup's timeline. This de-risked their business model, accelerated their path to market, and provided the technical credibility needed to secure significant funding.