FRAUD DETECTION

Fraud detection automation that outpaces attackers

AI agents that detect fraud in real-time by understanding behavior, mapping networks, and identifying patterns that rules-based systems miss — while dramatically reducing false positives.

THE PROBLEM

Fraud is evolving faster than rules can keep up

Sophisticated fraud rings, synthetic identities, and AI-powered attacks are outpacing traditional detection systems. Every missed fraud costs money; every false positive costs trust.

$579B

Global fraud losses (2025)

Fraud scams and bank fraud schemes cost $579.4 billion globally. Losses are accelerating as attackers adopt AI and automation themselves.

90%+

False positive rate

Traditional rules-based systems flag legitimate transactions at extreme rates. Analysts spend most of their time clearing non-issues instead of investigating real fraud.

Days

Detection-to-action lag

Many fraud schemes are detected only after settlement — when recovery is difficult or impossible. Batch processing creates windows that attackers exploit.

Detection performance

Catch more fraud, flag fewer legitimate transactions, respond faster.

95%+

Fraud detection rate

Catches patterns that rules engines miss

80%

Fewer false positives

Context-aware analysis reduces noise

Low

Detection latency

Real-time transaction scoring

24/7

Continuous monitoring

No gaps in coverage, ever

CAPABILITIES

Multi-layered fraud defense

AI agents that combine behavioral analysis, network intelligence, and real-time scoring to detect fraud across every vector — from individual transactions to coordinated schemes.

Behavioral Pattern Analysis

Agents build dynamic behavioral profiles for every account — learning normal patterns and detecting anomalies that static rules can't capture, even as fraud tactics evolve.

Individual behavior baselines
Peer group comparison
Temporal pattern detection
Adaptive threshold learning

Network & Relationship Intelligence

Fraud doesn't happen in isolation. Agents map transaction networks to identify coordinated fraud rings, money mule chains, and suspicious relationship patterns across accounts.

Transaction graph analysis
Fraud ring detection
Money mule identification
Cross-account correlation

Real-Time Transaction Scoring

Every transaction is scored in real-time against multiple risk dimensions — amount, velocity, geography, device, behavior, and network signals — with decisions made before settlement.

Multi-dimensional risk scoring
Pre-authorization decisioning
Velocity and pattern checks
Device and session analysis

Emerging Threat Detection

Agents identify novel fraud patterns before they become widespread — detecting subtle shifts in transaction behavior that indicate new attack vectors being tested.

Zero-day fraud detection
Attack pattern clustering
Cross-institution signal sharing
Proactive threat alerting

HOW IT WORKS

From signal to action in real-time

01

Ingest

Agents consume transaction streams, device signals, session data, and external intelligence feeds in real-time.

02

Analyze

Multi-dimensional scoring across behavior, network, velocity, geography, and device — all within the transaction window.

03

Decide

Risk-calibrated decisions: approve, challenge, hold, or block — with confidence scores and reasoning for every action.

04

Learn

Continuous feedback loops from confirmed fraud and false positives improve detection accuracy over time.

COVERAGE

Detects fraud across every vector

Payment fraud

Account takeover

Application fraud

Synthetic identity

Money laundering

Insider fraud

Card-not-present

First-party fraud

Authorized push payment

Check fraud

Wire fraud

Crypto fraud

Frequently asked questions

How does AI-based fraud detection differ from rules-based systems?

Rules-based systems flag transactions that match predefined patterns — if the rule doesn't exist, the fraud passes through. AI agents learn what normal looks like for each account and detect deviations, even for fraud types they've never seen before. They also adapt as fraud tactics evolve, without requiring manual rule updates.

Can the agents work alongside our existing fraud detection tools?

Yes. Aetherix fraud agents integrate as an additional scoring layer alongside existing tools. They can consume alerts from your current system, enrich them with contextual analysis, and reduce false positives — or operate as the primary detection engine with your existing tools handling case management.

How do you handle the false positive problem?

Traditional systems generate massive false positive volumes because they lack context. Our agents consider the full picture — account history, peer behavior, transaction network, device signals, and temporal patterns — to distinguish genuine fraud from unusual-but-legitimate activity. This typically reduces false positives by 70–80%.

What types of fraud can the agents detect?

The agents handle payment fraud, account takeover, application fraud, synthetic identity fraud, money laundering patterns, insider fraud, and emerging attack vectors. Because they learn behavioral patterns rather than matching rules, they can detect novel fraud types that haven't been explicitly programmed.

Ready to upgrade your fraud defense?

See how Aetherix fraud detection agents can catch more fraud, reduce false positives, and protect your customers in real-time.