Link signals instead of multiplying alerts
An isolated signal rarely proves fraud. A useful capability brings together business events, transactions, identities, sessions, devices, network addresses, behaviour and technical logs to surface scenarios that genuinely require investigation.
Custom use cases
- Detection of atypical transactions, duplicates, unusual amounts, suspicious sequences or deviations from expected behaviour.
- Account fraud detection: account takeover, credential changes, abnormal sign-ins, mass account creation or process bypass.
- Correlation of customer, partner, API, CRM, ERP, payment, ticketing, application-event and security-log signals.
- Alert qualification through risk scores, business context, history, explainable rules and thresholds aligned with accepted risk.
- Detection of emerging patterns in large data volumes without confusing a technical anomaly with confirmed fraud.
Architecture, governance and controls
- Source mapping, data quality, correlation identifiers, time windows and retention rules.
- Collection and normalization pipeline compatible with existing tools: logs, SIEM, Elastic, Splunk, business databases, APIs or a data warehouse.
- Combination of business rules, statistical scoring and AI models where data, volume and governance justify it.
- Alert explainability, audit trail, false-positive management, human review and feedback mechanisms to improve the model.
- Privacy scoping: purposes, minimization, access, security, retention, traceability and decisions around sensitive data.
Expected deliverables
- Fraud-scenario scoping, available sources, blind spots and success criteria.
- Data model and correlation plan usable by data, security, fraud and business teams.
- Prototype, rules, scores or dashboards according to the agreed scope.
- Alert-handling process, quality metrics and a human-review loop.
- Roadmap to industrialize, secure and evolve the capability without unnecessary lock-in.
Does AI replace fraud analysts?
No. The goal is to reduce noise, enrich context and prioritize investigations. Sensitive decisions remain governed by rules, traceability and appropriate human review.
Can this build on Elastic, Splunk or existing tools?
Yes. Scoping starts with existing sources and tools to avoid an oversized platform or duplicate collection.
Do we need a big data platform first?
No. Volume can justify some approaches, but effective correlation can start with a focused business perimeter and reliable, governed data.
How is privacy handled?
Purposes, access, data used, retention, security and the role of human review should be defined from the start with the relevant stakeholders.