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Banking & Insurance

Fraud Detection Platform

Layered fraud detection for lending origination and insurance claims, combining rules, models, document forensics and network analysis.

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In development

The problem

The challenge

Fraud has become organised and tool-assisted. Fabricated documents, synthetic identities and coordinated application or claims rings exploit gaps between siloed checks, while blunt controls add friction for genuine customers.

Current industry pain points

  • Siloed checks that never connect related applications or claims
  • Investigators overwhelmed by unprioritised referrals
  • Manipulated documents that pass visual inspection
  • Detection rules that are slow to update as fraud patterns change

The solution

How it works

A layered detection pipeline evaluates every application or claim through deterministic rules, machine-learning risk scores, document forensics and entity-network analysis, producing an explainable fraud score and routing decision.

Decision flow
  1. Event intake (application / claim)
  2. Deterministic rules
  3. ML risk scoring
  4. Document forensics
  5. Entity graph
  6. Explainable fraud score
  7. Case management
  8. Feedback loop

In detail

Inside the platform

01

Layered detection

Hard rules evaluate first for known fraud patterns, followed by model scoring and network checks. Each layer contributes reason codes to the final assessment.

02

Document forensics

Uploaded PDFs and images are analysed for metadata anomalies, font and layout inconsistencies and arithmetic errors.

03

Network analysis

Shared devices, contact details, addresses and counterparties link related cases into networks for investigation.

04

Investigator workflow

Prioritised queues, case evidence and outcome capture feed confirmed results back into rules and models.

Capabilities

Key capabilities

  • Real-time rules

    Low-latency evaluation of known fraud patterns.

  • Explainable scoring

    Model scores with contributing factors.

  • Document tamper detection

    Forensic analysis of financial and claims documents.

  • Graph analytics

    Detection of linked applications and claims.

  • Case management

    Investigator queues and evidence tracking.

  • Continuous learning

    Confirmed outcomes improve detection.

Trust

Security, integration and outcomes

Security by design

  • Encryption at rest and in transit
  • Role-based access to case data
  • Immutable audit trails
  • Data minimisation in analytics

Integrations

  • Loan origination systems
  • Claims management systems
  • Identity verification providers
  • Device intelligence providers
  • Event streams (Kafka)
  • SIEM and case tools

Business outcomes

  • Earlier detection of fraudulent activity
  • Better-targeted investigations
  • Less friction for genuine customers
  • Visibility of organised fraud networks

Outcomes depend on each organisation’s data, products and processes. We do not publish accuracy or ROI figures without independently verifiable evidence.

FAQ

Frequently asked questions

Does this work for both lending and insurance?

Yes. The detection pipeline is shared; rules, features and models are configured for each domain.

Discuss fraud detection for your organisation

Let’s discuss it. Tell us what you are working on and an engineer — not a sales script — will respond.

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