Deploy AIfor Regulated Finance

For high-risk AI systems like credit scoring and fraud detection, we build in model documentation, risk assessments, explainability layers (SHAP, LIME), bias auditing, and human oversight mechanisms from the design phase. Every model ships with technical documentation aligned to Articles 9–15 of the EU AI Act.

It depends on the use case, but we start with a data audit during discovery. For fraud detection, we typically need 6–12 months of transaction history with labelled fraud cases. For credit scoring, income and repayment data plus application history. We assess data quality, volume, and gaps during the first 2–3 weeks and advise on what's needed before model development begins.

Yes. Most of our AI work integrates into existing fintech systems rather than replacing them. We deploy models as microservices that connect to your payment processing pipeline, core banking system, or compliance workflow via APIs. Our engineers have integrated AI components into platforms built on Java, .NET, Node.js, and various core banking architectures.

A typical timeline is 3–4 months from discovery to production for a single use case. The first 2–3 weeks are discovery and data audit, weeks 4–8 are proof-of-concept development, and weeks 8–16 cover production engineering, testing, and deployment. More complex implementations involving multiple models or agentic workflows take 4–6 months.

Every production model includes drift detection, performance monitoring dashboards, and automated alerting. When accuracy degrades beyond defined thresholds, naturally as fraud patterns evolve or customer behaviour shifts, we trigger automated retraining pipelines or flag the model for manual review. MLOps infrastructure is part of every deployment.