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FinTech Industry Solution

Autonomous Fraud Detection System

Legacy rule-based systems produce high false positives and fail to catch emerging synthetic identity fraud. Focus20 designs distributed, event-driven Agentic AI frameworks that monitor transaction streams, learn behavioral anomalies, and update risk scoring dynamically with sub-50ms latency.

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Business Value

Real-Time Financial Security

In FinTech, milliseconds matter. By transitioning from batch-processed fraud models to real-time streaming AI, banks and payment processors can intercept illicit transfers before they settle, saving millions in chargeback losses while vastly improving the legitimate customer experience.

60%
Reduction in False Positives
<50ms
Transaction Scoring Latency

The Agentic Workflow

1. Agentic Stack

Streaming: Amazon MSK.
Scoring Engine: SageMaker endpoint via AWS Lambda.
Graph DB: Amazon Neptune.
Resolution Agent: Bedrock LLM.

2. Reasoning Loop (ReAct)

> Lambda hook intercepts transaction
> Queries Neptune Graph for entity overlap
> Submits vector to SageMaker anomaly model
> Bedrock Agent parses "High Risk" score
> Issues synchronous API block command to Core Banking app

Enterprise Technical Architecture

High-throughput, low-latency AWS infrastructure designed explicitly for banking workloads.

Fast-Data Streaming

  • Streaming: Amazon Managed Streaming for Apache Kafka (MSK) ingests millions of events globally.
  • Entity Resolution: Amazon Neptune creates real-time knowledge graphs of synthetic identity clusters.
  • Inference: SageMaker endpoints evaluate behavior patterns in single-digit milliseconds.
  • Intervention: An AI Resolution Agent deployed on Bedrock auto-blocks threats or escalates ambiguous cases.

Cloud Migration Highlight

Migrated a Tier-1 payments provider's Microsoft .NET Core API stack from Azure App Services to AWS Elastic Kubernetes Service (EKS) for immense scalability during peak trading hours.

graph LR TX[Transaction] --> MSK[Amazon MSK] MSK --> L[Lambda Hook] L --> NEP[(Neptune Graph)] L --> SM[SageMaker Model] SM --> AG[AI Resolution Agent] AG -->|Block| CORE[Core Banking] AG -->|Escalate| REV[Human Ops Queue]

Deployment Timeline

Securing financial perimeters without interrupting established transaction flows.

Phase 1: Days 1 - 21

Shadow Mode Execution

We duplicate the transaction stream (via Kafka MirrorMaker) into an isolated AWS environment. The AI models score transactions in "Shadow Mode"—flagging incidents but not executing blocks, allowing Risk teams to validate accuracy.

Phase 2: Weeks 4 - 8

Agentic Co-Pilot

The AI system begins escalating high-confidence fraud attempts to a "Human Ops Queue," accompanied by a natural-language breakdown of exactly *why* the transaction was flagged based on the Neptune graph relationships.

Phase 3: Weeks 9 - 14

Real-Time Auto-Resolution

Full MSK integration is locked in. The Resolution Agent now has permission to trigger synchronous `` and `` API commands against the core banking infrastructure within 45 milliseconds of the transaction request.

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