GenAI for Fraud Detection and Response in Financial Services

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Digital fraud is growing, attackers change tactics every week, and teams remain caught between false positives that annoy good customers and late alerts that arrive after the money is gone.

The leap forward isn’t just detecting more—it’s detecting better and acting earlier: enriching signals in real time, reasoning with context, and executing response playbooks with control and traceability.

This article lays out the business pain, how we can address it today with GenAI combined with your internal data, and which AWS components let you move from a simple “alert list” to an orchestrated anti-fraud operation.

What’s the problem today?

• Too many false positives: static rules block legitimate customers and drive up cost per case.
  
  
• Fragmented signals: transactions, devices, behavior, KYC/AML, open banking… all in silos. Fraud hides in the relationships.
  
  
• Slow investigations: analysts open eight systems, copy and paste evidence, and manually draft suspicious activity reports.
  
  
• Reactive response: decisions come late; fraud is “confirmed” only after it hits the account, generating losses.
  
  

What changes with Data + GenAI

• Contextual (multimodal) detection: models that cross-reference transaction, identity, device, network, and free text to cut false positives without losing coverage. With services like Amazon Fraud Detector, you can train detectors on your history plus Amazon’s expertise and deploy them as low-latency APIs.
  
  
• Graph reasoning: fraud gives itself away in the connections. With Amazon Neptune/Neptune Analytics and Graph ML techniques (GraphStorm), you can spot collusion patterns and “mules” that isolated rules can’t see.
  
  
• Investigator copilot: an assistant that gathers evidence, summarizes the case, and proposes the next action (block, intensify monitoring), citing policies and procedures via Knowledge Bases for Amazon Bedrock.
  
  
• Playbooks that actually execute: with Bedrock AgentCore you orchestrate investigation and remediation steps with agent identity, secure tools (controlled browser, code interpreter), and end-to-end observability.
  
  

Layered architecture on AWS

1. Real-time signals
   Ingestion and feature generation with Amazon Kinesis/MSK and AWS Lambda; behavior search and enrichment with Amazon OpenSearch (text + vectors) to compare devices, descriptions, and patterns.
   
   
2. Hybrid detection
   
   

• Amazon Fraud Detector for event-specific scoring (enrollment, login, payment, withdrawal).
  
  
• Your own models in Amazon SageMaker where you need specific architectures.
  
  
• Neptune/Neptune Analytics + Graph ML for suspicious communities and money flows.
  
  

3. Generative intelligence and orchestration
   
   

• Amazon Bedrock to summarize cases, generate hypotheses, and explain decisions; Knowledge Bases to ground answers in AML/KYC policies; Guardrails for tone.
  
  
• Bedrock AgentCore (currently in preview) to run playbooks: collect evidence, open tickets, apply blocklists, or trigger step-up actions with agent identity and full traceability.
  
  

4. Investigation and operations
   
   

• Amazon Q Business as the analyst’s internal search (policies, procedures, case notes) with cited answers.
  
  
• Amazon Connect + Contact Lens if the case involves customer interaction (verification, education, reimbursement), with conversation analytics and script compliance.
  
  

5. Data, security, and governance
   Amazon S3/Lake Formation/Glue/Athena for a governed data lake; IAM/KMS/PrivateLink/CloudTrail/CloudWatch for least-privilege access, encryption, and auditing of every decision. For organizations such as central banks, AWS publishes reference guides for ML-based detection.

Where we see the value

1. Millisecond authorizations
   An “out-of-pattern” purchase arrives via streaming. The pipeline enriches with device ID, geolocation, and recent frequency; Fraud Detector scores it; the graph reveals links to previously flagged accounts; and the agent orders a step-up (OTP + biometric challenge) instead of a hard decline—reducing friction for the legitimate customer while stopping real fraud.
   
   
2. Assisted triage and investigation
   Hundreds of daily alerts are prioritized by risk and novelty. The analyst opens a case and the investigation copilot automatically gathers related movements, customer contacts, prior chats, and applicable rules; it proposes hypotheses with citations (RAG over your AML manual) and drafts a Suspicious Activity Report (SAR) for human review.
   Result: less time per case and explainable decisions.
   
   
3. Claims fraud (insurance) and collusion
   A set of low-value claims shares an adjuster, a repair shop, and a temporal pattern. With Neptune Analytics + Graph ML, atypical communities emerge; the agent triggers a playbook: freeze pending payouts, request additional evidence, and notify Legal with a generated evidence package (summary, graphs, and appendices).
   
   

Security, compliance, and control

Data and prompts stay within your perimeter via private endpoints, encryption in transit and at rest, and no retention outside your account. Guardrails prevent exposure of personal information in generated responses. CloudTrail records who executed what, with which parameters, and when. In AgentCore, agents operate with their own identity and least privilege, and their trajectories are observable for audit.

How Switch tackles these challenges

• Fast impact. We deploy an “immediate-value” anti-fraud flow: assisted triage and SAR drafts generated with Amazon Bedrock + Knowledge Bases; Amazon Fraud Detector on critical authorization events; and collusion detection via graphs in Amazon Neptune.
  
  
• Security by design. We govern data in Amazon S3 + Lake Formation, enforce least-privilege access with IAM, handle encryption and key management with KMS, ensure private connectivity via PrivateLink, and provide a full audit trail with CloudTrail.
  
  
• Measurable outcomes. From sprint one, we deliver dashboards for precision/recall, stage times, and business impact, so business and compliance can see progress and tune thresholds with evidence.
  
  

The goal isn’t to “see more alerts”—it’s to lose less money while reducing friction. With hybrid detection, an investigation copilot, and executable playbooks on Amazon Bedrock and AgentCore, you move from reacting late to preventing and responding with speed and traceability.

Get in touch to explore how we can bring these use cases to life in your environment.

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