Global Fintech Platform

The Challenge

A fintech platform processing high volumes of financial transactions had three compounding problems. First, its data ledger architecture was not designed for analytical workloads: transactional reconciliation required full table scans and manual intervention, making the process slow, error-prone, and increasingly unscalable as transaction volumes grew. Second, fraud detection was reactive rather than predictive — by the time irregularities were caught, damage had already occurred and the investigation trail was cold. The platform had no trained classification system capable of identifying anomalous patterns in real time across a live transaction stream. Third, cloud infrastructure costs had grown without systematic governance: staging and production environments had accumulated configuration drift, over-provisioned resources, and no FinOps discipline applied across tiers, resulting in spend that had no relationship to actual load.

Our Solution

GYSP rearchitected the data ledger with a new indexing layout optimised for transactional workloads, boosting reconciliation efficiency by 60% without any disruption to data integrity. On top of the improved data foundation, an ensemble fraud detection system was engineered using five complementary classification algorithms, Logistic Regression, SVM, Random Forest, XGBoost, and AdaBoost, each contributing independent signal to a combined model that achieved 92% accuracy on test data. The ensemble architecture was specifically chosen to reduce false negatives by leveraging the differing sensitivities of each algorithm against different fraud pattern types. The predictive models were integrated directly into production infrastructure via Amazon EKS, with auto-scaling Kubernetes clusters ensuring the fraud scoring layer could handle peak transaction loads without manual intervention. A real-time streaming architecture sustained 99% system uptime across intensive processing periods. In parallel, GYSP conducted a FinOps audit across all staging and production deployment configurations, eliminating over-provisioned resources, rationalising instance sizing, and applying cost governance across both tiers — cutting total cloud infrastructure spend by 30%.

Key Deliverables

• New data ledger indexing layout boosting transactional reconciliation efficiency by 60% while preserving strict data integrity
• Ensemble fraud detection system combining Logistic Regression, SVM, Random Forest, XGBoost, and AdaBoost for 92% classification accuracy
• Real-time fraud monitoring architecture handling intensive transaction stream processing loads at production scale
• Auto-scaling isolated production clusters on Amazon EKS (Kubernetes) running live predictive model inference
• 99% system uptime sustained across peak transaction volumes through resilient streaming infrastructure design
• FinOps audit across staging and production deployment tiers cutting total cloud infrastructure spend by 30%
• Multi-cloud configuration governance eliminating resource drift between staging and production environments

Services Delivered

• AI/ML Development
• Cloud & DevOps Engineering
• Data Engineering

Tech Stack

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