Onboarding exposure
Stolen credentials and synthetic identities can pass basic checks without stronger verification context.
AI Fraud Detection
AI fraud detection for fast-moving digital finance.
Connect supported login, device, and transaction events to customer context so analysts can investigate account-takeover indicators and payment anomalies earlier.
Risk Assessment
Real-time fraud scoring
12
Sarah Jenkins
Login from trusted IP
TXN-4921
Card payment
The problem
Manual reviews and static rules miss modern fraud patterns.
Account takeovers
Legacy systems only examine transactions, ignoring critical non-financial events like suspicious PIN resets or sudden device changes.
Subtle patterns
Small coordinated test transactions can look harmless in isolation and become clearer only when related activity is reviewed together.
Siloed investigations
Risk teams lose hours jumping between different tools to piece together the forensic trail of a coordinated attack.
Why it matters
Fraud detection has to adapt to behaviour, not just thresholds.
Fraudsters test systems across multiple sessions, devices, and events. AI-assisted detection helps teams connect weak signals across the customer journey before the losses compound.
The Remllo approach
Identity-aware, AI-assisted fraud monitoring.
- Event-level monitoringMonitor both financial (transfers) and non-financial events (login attempts, device changes) simultaneously.
- Behavioural signalsTrack what normal behavior looks like for an account and route notable anomalies into alerts.
- Rules + AI assistanceCombine deterministic controls with behavioural context and AI-assisted rule drafting where enabled.
- Identity-aware contextCross-reference suspicious events with available KYC verification status to gauge the true risk level.
Capabilities
Fraud tooling designed for adaptive detection.
01
Real-Time Signal Detection
Surface sudden changes in transaction velocity or configured account activity as events arrive.
02
Non-Transaction Event Tracking
Monitor logins, failed logins, device changes, PIN changes, password resets, new beneficiaries, and profile updates alongside transactions.
03
Customer Risk Scoring
Every customer carries a live risk score that rises and falls with their behaviour across all events.
04
Collaborative Investigations
Escalate high-risk alerts directly to human investigators with full visual context.
How it works
How AI fraud detection works in practice.
01
Ingest
Streams of behavioral and transactional events flow securely into Remllo via API.
02
Analyze
The system maps the event against the customer’s historical footprint and known fraud vectors.
03
Detect
Rules flag configured combinations, such as a large transfer after a password reset. Supported inline integrations can use the resulting decision.
04
Alert
A high-priority case is generated and assigned to a fraud investigator for review.
05
Resolve
Operations teams review the evidence, document a decision, and take the appropriate action in their operating systems.
Customer fit
Built for fraud teams under pressure to move fast.
Designed for payment apps, wallet providers, lenders, and consumer fintech products facing evolving fraud threats.
Fintechs
Surface account takeover indicators and suspicious payment activity for review.
Digital Lenders
Identify configured loan-stacking and identity-risk signals during application and servicing.
Crypto Exchanges
Flag erratic withdrawal behaviors immediately following a new device login.
E-commerce
Identify card-testing velocity and high-risk buyer-seller collusion.
Product connection
Powered by the Remllo ecosystem.
Remllo WatchTower provides event monitoring and investigation workflows. Remllo Identity can add supported verification and watchlist context when the products are integrated.
Why Remllo
Why teams adopt Remllo for AI fraud detection.
- Beyond just money: we track PIN changes and IP anomalies entirely
- API-first flexibility allows you to send any custom event type
- Identity integration links suspicious activity to available verification evidence
- Structured investigations keep signals, notes, evidence, and decisions together
FAQ
Frequently asked questions
Remllo combines configurable rules with behavioural context to surface notable changes in transaction activity and supported account events for analyst review.
WatchTower can evaluate configured transaction-velocity patterns and supported account events such as PIN resets, device changes, failed logins, and beneficiary changes.
Yes. WatchTower builds behavioural context from transaction history, such as typical transfer sizes, velocity, and counterparty patterns, then flags notable deviations for review.
Absolutely. Our case management interface is designed specifically for fraud investigators to review payloads, add notes, and make operational decisions.
WatchTower is designed for low-latency server-side decisioning, but final end-to-end latency depends on the customer network, source system, payload size, and deployment path.
Need AI-native fraud detection infrastructure?
Bring behavioural signals, transaction monitoring, and analyst workflows together with Remllo.
Book a demo