Delayed detection
Processing batches overnight means bad actors have already moved funds by the time your team is alerted.
AI-native Monitoring
AI-native transaction monitoring built for modern risk teams.
Deterministic rules, behavioural analysis, customer risk profiles, and analyst workflows in one AI-native monitoring stack.
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The problem
Traditional transaction monitoring was not built for adaptive risk operations.
High-volume alert queues
Rigid rules can create low-value alerts that consume analyst time and make higher-risk activity harder to prioritize.
Fragmented systems
Siloed tools, file uploads, and disconnected customer data slow investigations and make context harder to reconstruct.
Evidence at scale
As transaction volumes grow, teams need a consistent record of controls, alerts, investigations, and decisions.
Why it matters
Risk teams need AI-native infrastructure, not disconnected dashboards.
Teams are under pressure to process more alerts, move faster, and manage more complex patterns of suspicious activity. AI-native monitoring gives operations teams better context, more flexible workflows, and sharper detection quality.
The Remllo approach
AI-assisted detection layered on top of operational control.
- Low-latency decisionsEvaluate events as they arrive and, for supported inline integrations, return an allow, review, or block outcome.
- Customer risk contextConnect transaction history and supported non-transaction events to customer-level context for clearer review.
- Automated case creationTurn high-risk signals into organized, workable cases immediately without manual escalation.
- Built for operationsEmpower your risk team with collaborative workspaces designed for rapid resolution, not just dashboards.
Capabilities
AI-native tooling for transaction monitoring at scale.
01
API & CSV Ingestion
Seamlessly ingest real-time API webhooks or bulk upload historical data via secure CSVs.
02
Dynamic Rules Engine
Configure, test, and manage custom monitoring rules with less day-to-day engineering dependence.
03
Behavioural Context
Use transaction history and configured signals to give analysts context beyond a one-size-fits-all threshold.
04
Centralized Alert Inbox
Give analysts a structured queue of flagged transactions with investigation context and history.
How it works
From signal capture to analyst action.
01
Ingest
Push transaction events through API, adapters, or CSV into the WatchTower engine.
02
Profile
Build behavioural context for customers and counterparties as transaction history grows.
03
Detect
Run configured controls and rules against each event to produce a risk outcome.
04
Alert
Automatically flag suspicious transactions and generate contextual alerts for analysts.
05
Resolve
Review linked identity data, annotate notes, and dismiss or escalate the case securely.
Customer fit
Built for AI-native risk operations.
For fintechs and regulated businesses that want machine-assisted monitoring without losing control over explainability and governance.
Fintechs & Neobanks
Monitor transfers, payments, and other configured transaction events as they arrive.
Microfinance Banks
Shift securely from manual ledger reviews to automated transaction scoring.
Marketplaces
Identify suspicious seller payouts and coordinated buyer-seller collusions.
Crypto Platforms
Apply configured monitoring controls to supported on-ramp and off-ramp transaction flows.
Product connection
Powered by the Remllo ecosystem.
Remllo WatchTower provides the monitoring and investigation workflow. Remllo Identity can add supported KYC, KYB, sanctions, and PEP context when the products are integrated.
Why Remllo
Why Remllo fits AI-native monitoring programs.
- Built for African realities and regulatory nuances
- API + CSV flexibility for any infrastructure setup
- Identity-aware monitoring linking the transfer to the user
- Reviewable decision history for investigation and compliance evidence
FAQ
Frequently asked questions
It is a system used by financial institutions to review transaction activity, identify suspicious patterns, and support investigation and reporting workflows.
Remllo WatchTower evaluates events as they arrive. Supported inline integrations can use an allow, review, or block outcome, while monitored deployments run alongside the transaction flow. Batch CSV paths are also supported.
Yes. If your institution doesn't have a modern API infrastructure, you can securely upload batch CSV files for processing and alert generation.
Absolutely. The platform includes a purpose-built workspace for risk analysts to review alerts, track history, and document their decisions.
Yes. When integrated with Remllo Identity, WatchTower can include supported KYC, KYB, sanctions, and PEP context in an investigation.
Integration time depends on source-system readiness, payload quality, and operating model. API-ready teams can start quickly, while CSV-based teams can begin with controlled uploads and testing.
Upgrade to AI-native transaction monitoring.
Use Remllo to combine real-time detection, explainable workflows, and AI-assisted risk operations where enabled.
Book a demo