Fintech Operations, Risk & Analytics Modernisation on AWS

Fintech platforms need scalable analytics to monitor operations, risk indicators, fraud signals, and transaction trends. This engagement assesses current analytics capabilities and defines a secure, AWS-aligned modernisation strategy.

Ideal for fintech platforms, financial operations teams, and risk and analytics leadership.

Key Features

• Operations & Risk Diagnostics: Assessment of transaction flows, operational KPIs, fraud controls, data latency, and regulatory constraints across fintech platforms.

• AWS-Native Risk & Analytics Architecture: A future-state architecture for real-time transaction monitoring, fraud detection, risk scoring, and compliance-aligned analytics.

• Real-Time Fraud, AML & Risk Processing: Event-driven patterns for anomaly detection, AML checks, dispute indicators, and high-volume transaction analytics.

• Unified Data Lake & Transaction Store: A governed data foundation for transactions, behavioural signals, operational telemetry, and risk datasets.

• Analytics & KPI Insights: Standard analytics patterns for transaction anomalies, payment performance, exposure trends, reconciliation gaps, and compliance metrics.

• Regulatory-Ready Security & Compliance Baseline: Controls aligned with PCI DSS, SOC 2, and financial regulations, covering IAM boundaries, encryption, audit logging, and monitoring.

• KPI Dashboards: Operational, risk, compliance, and fraud dashboards built on standardised, governed datasets.

• AI-Driven Risk & Fraud Intelligence: Support for fraud prediction, behavioural scoring, credit-risk modelling, and real-time anomaly alerts.

• Modernisation Roadmap: A sequenced plan for operational uplift, risk automation, fraud intelligence, and AI maturity.

Deliverables

• Current-State Assessment Documentation: Documentation covering operational diagnostics, data quality findings, risk workflow analysis, reporting maturity, and compliance posture.

• Target AWS Architecture Blueprint Pack: Architecture diagrams and specifications covering data lake and transaction storage design, streaming patterns for fraud and AML, analytics stack design, and governance alignment.

• Fintech Data Governance & Standards Kit: A set of artefacts covering data classification, retention standards, lineage and cataloguing rules, access-control models, and compliance-ready data handling procedures.

• Fraud & Risk Pipeline Design Documents: Technical designs for ingestion and scoring workflows, AML and anomaly-detection patterns, reconciliation logic, dispute handling integrations, and event routing.

• Reporting & Dashboard Specifications: KPI definitions, dashboard wireframes, data models, and metric rules for operational, risk, compliance, and fraud dashboards.

• AI/ML Enablement Assets: Guidance covering feature engineering, model evaluation criteria, risk-scoring pipelines, and deployment patterns for SageMaker or EKS.

• Security & Compliance Controls Package: A documented control set covering IAM policies, encryption strategies, logging and monitoring baselines, audit trail templates, and compliance evidence models.

• Phased Modernisation Execution Plan: A prioritised delivery plan covering foundation setup, analytics uplift, fraud and risk automation rollout, and staged AI or ML adoption.

• Implementation & Rollout Guidance: A practical execution guide covering PoC validation, data onboarding, dashboard rollout, and go-live readiness.

What You Will Achieve

• Modern analytics architecture on AWS
• Improved operational and risk visibility
• A clear roadmap for analytics and AI adoption

Business Value

• Reduced operational risk
• Improved compliance visibility
• Faster data-driven decisions

Engagement Timeline Delivered as a structured engagement, typically completed within 3 to 6 weeks.