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AI fraud detection system analyzing transaction network graphs with real-time risk scoring
Predictive & Analytics Solutions

AI Fraud Detection & Prevention Software

Stop fraud before it happens with real-time AI that analyzes hundreds of signals simultaneously — catching sophisticated fraud patterns that rule-based systems miss entirely.

🛡️ Financial AI · Fraud Prevention · Risk Intelligence

94%

Fraud Detection Rate

60%

False Positive Reduction

<100ms

Decision Latency

$485B

Annual US Fraud Losses

Get a Free DemoTalk to an Expert
FraudShield AI — Transaction MonitorLIVE
TXN-8821$1,240New York, NY
APPROVED
TXN-8822$8,900Lagos, NG
FLAGGED
TXN-8823$340Los Angeles, CA
APPROVED
TXN-8824$15,000Unknown VPN
BLOCKED
TXN-8825$2,100Chicago, IL
REVIEW
99.7%
Accuracy
<50ms
Decision
$2.1M
Saved/mo

🏆 Awards & Recognition

Recognized by the industry's most trusted platforms

Implementation

How It Works

01

Signal Collection

Collect transaction data, device signals, behavioral biometrics, and contextual features via lightweight SDK or API integration.

02

Feature Engineering

200+ derived features calculated in real time: velocity checks, network centrality scores, behavioral deviation scores, and more.

03

Ensemble Scoring

Multiple ML models (gradient boosting, neural networks, graph neural networks) vote on risk score with explainability output.

04

Decision & Action

Risk score triggers approve/review/decline decision. High-risk cases routed to analyst queue with full investigation context.

Core Capabilities

What Our Solution Delivers

Behavioral Biometrics

Analyze typing patterns, mouse movements, touch gestures, and device usage patterns to detect account takeover attempts.

Graph-Based Fraud Detection

Identify fraud rings and money laundering networks by analyzing relationship graphs between accounts, devices, and transactions.

Real-Time Scoring

Sub-100ms risk scoring for every transaction, login, and account action. Integrate via REST API with any platform.

Synthetic Identity Detection

Detect synthetic identities created by combining real and fabricated information — the fastest-growing fraud type in financial services.

Adaptive Learning

Models continuously learn from confirmed fraud cases and false positives, adapting to new fraud patterns without manual rule updates.

Device Intelligence

Device fingerprinting, VPN/proxy detection, emulator detection, and geolocation anomaly analysis for account protection.

Case Management

Integrated investigation workbench with transaction timelines, network visualizations, and evidence packaging for law enforcement.

Explainable AI

Every fraud decision includes human-readable explanations of contributing factors for regulatory compliance and analyst review.

Real-World Impact

Industry Use Cases & Results

🏦Digital Banking

Account opening fraud reduced 91%; charge-offs reduced $3.8M; legitimate customer approval rate improved 12%.

Neobank had 1.8% account opening fraud rate from synthetic identities costing $4.2M in first-year charge-offs. Synthetic identity detection combining document verification, behavioral analysis, and network graph analysis at account opening. Account opening fraud reduced 91%; charge-offs reduced $3.8M; legitimate customer approval rate improved 12%.
🛒E-Commerce

Chargeback losses reduced 73% to $2.2M; false positive rate reduced from 4.2% to 0.8%; revenue from recovered legitimate transactions up $3.1M.

Online marketplace had $8.2M annual chargeback losses with 4.2% false positive rate blocking legitimate customers. Real-time transaction scoring with device intelligence, behavioral biometrics, and seller-buyer network analysis. Chargeback losses reduced 73% to $2.2M; false positive rate reduced from 4.2% to 0.8%; revenue from recovered legitimate transactions up $3.1M.
🛡️Insurance

Fraud detection rate improved from 8% to 34% of fraudulent claims; $12M annual savings; SIU team efficiency improved 4x.

Auto insurer had 12% claims fraud rate with manual investigation team reviewing only 8% of claims. AI claims fraud scoring analyzing claim patterns, claimant history, provider networks, and geospatial anomalies. Fraud detection rate improved from 8% to 34% of fraudulent claims; $12M annual savings; SIU team efficiency improved 4x.
🏥Healthcare

Fraudulent billing detected and recovered: $5.9M in year 1; 3 provider fraud rings identified and referred to law enforcement.

Healthcare network had $6.8M annual billing fraud from upcoding, phantom billing, and provider collusion. Medical billing fraud detection analyzing procedure patterns, provider networks, and billing anomalies against clinical norms. Fraudulent billing detected and recovered: $5.9M in year 1; 3 provider fraud rings identified and referred to law enforcement.
💳Payments

Fraud rate reduced to 0.04%; false positive rate reduced to 0.6%; merchant churn from false declines reduced 44%.

Payment processor had 0.18% fraud rate with 3.2% false positive rate causing merchant dissatisfaction and revenue loss. Merchant-specific fraud models with real-time velocity checks, card testing detection, and cross-merchant fraud ring identification. Fraud rate reduced to 0.04%; false positive rate reduced to 0.6%; merchant churn from false declines reduced 44%.
💰Lending

First-payment default rate reduced to 0.6%; $7.8M annual loss reduction; legitimate approval rate maintained at 94%.

Online lender had 3.4% first-payment default rate from identity fraud and straw borrowers costing $9.2M annually. Loan application fraud scoring combining identity verification, income verification, behavioral signals, and network analysis. First-payment default rate reduced to 0.6%; $7.8M annual loss reduction; legitimate approval rate maintained at 94%.

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Versatile Deployment

Applications Across Industries

Financial Services & Banking

  • Transaction fraud detection
  • Account takeover prevention
  • New account fraud
  • Wire transfer fraud
  • Check fraud detection
  • Money laundering detection

E-Commerce & Retail

  • Payment fraud prevention
  • Chargeback reduction
  • Promotion & coupon abuse
  • Account sharing detection
  • Return fraud prevention
  • Marketplace seller fraud

Insurance

  • Claims fraud detection
  • Application fraud
  • Provider fraud & billing abuse
  • Workers comp fraud
  • Healthcare fraud detection
  • Auto insurance fraud

Healthcare

  • Medical billing fraud
  • Prescription drug fraud
  • Identity theft detection
  • Provider credentialing fraud
  • Clinical trial fraud
  • Medicare/Medicaid fraud

Telecommunications

  • Subscription fraud
  • SIM swap detection
  • International revenue share fraud
  • Roaming fraud
  • Account takeover
  • Device financing fraud

Government & Public Sector

  • Benefits fraud detection
  • Tax return fraud
  • Unemployment fraud
  • Grant fraud detection
  • Procurement fraud
  • Identity fraud in services
INTERACTIVE FEATURE DEMOS

Every Feature. Live Demo.

Click any feature below to see an interactive demo of exactly how it works in production.

Real-Time Transaction Risk Scoring — Live DemoLIVE
TRANSACTION RISK SCORING — LIVE
TXN-8821$4,200Lagos, NGHIGH RISK

Every transaction is scored against 200+ risk signals — location anomalies, velocity patterns, device fingerprints — and flagged or blocked in under 50ms.

Frequently Asked Questions

Serving Orange County & Southern California

fraud detection software Orange CountyAI fraud prevention Californiamachine learning fraud detectiontransaction fraud scoringaccount takeover preventionsynthetic identity detectionreal-time fraud detection APIfinancial fraud AIchargeback prevention softwareinsurance fraud detection AI

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