SECURIX Developed Product
Elastic Fraud Detection
An AI-powered fraud detection layer inside your existing Elastic Stack that combines rules, machine learning and AI-assisted triage, with fewer false positives and documented reasoning behind every decision.
Solution overview
Elastic Fraud Detection combines rules, machine learning and AI-assisted investigation to identify suspicious activity using data in your existing Elastic Stack. Analysts review cases directly in Kibana, with relevant context, evidence and reasoning assembled for them. A focused proof of concept uses your data to evaluate detection quality, false-positive handling and investigation speed.
Your business needs
Your fraud team needs better detection and investigation capacity
Alert quality
False positives consume analyst time and delay attention to cases that require investigation.
Changing fraud patterns
Static rules can miss unusual or coordinated behavior outside predefined thresholds.
Investigation context
Gathering account, device and transaction history manually slows decisions and adds repetitive work.
Who this is for
Fraud and security teams
Banks using Elastic that need to improve fraud detection and reduce the manual effort involved in investigating false positives.
Head of fraud detection / Fraud prevention
A proof of concept using your own data to evaluate detection quality, false-positive handling and investigation speed.
Fraud Analysts
Relevant cases with context, evidence and documented reasoning to support investigation.
Elastic administration / Security engineering
Detection, investigation and workflow automation built within your existing Elastic Stack.
Our deliverables
Detection and investigation within your Elastic environment
A focused six-week proof of concept uses your historical and live data to deploy initial detection jobs, an AI triage agent and workflows, and validate their value.
- Rule-based detection and machine learning — Known fraud patterns stay covered by rules. Machine learning picks up unusual behaviour outside those rules.
- Investigation in Kibana — Analysts investigate suspicious activity across transaction, authentication, device, user and security data inside the existing Elastic Stack.
- Documented decisions — Evidence and reasoning are recorded for every decision, forming a traceable audit trail.
- AI-assisted triage — An AI triage layer gathers context, weighs the evidence and helps decide which alerts need a person.
- Workflow automation — Likely false positives can be documented and closed automatically. Real risks reach the analysts with a substantial part of the investigation already done.
- Validation in a proof of concept — Assessing fraud detection, how false positives are handled and investigation speed against your own data.
- Replacing existing fraud controls or platforms
- A standalone fraud interface
- Complete detection of credit fraud
- Comprehensive detection of internal fraud
- Deepfake Detection
- Comprehensive regulatory advice and compliance certification
- Replacing human fraud investigators
The service does not guarantee prevention of all fraud losses.
How we deliver
Validate the approach in a six-week proof of concept
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Use historical and live data from your existing Elastic Stack as the basis for the proof of concept.
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Deploy initial detection jobs, the AI triage agent and workflows.
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Validate fraud detection, false-positive handling and investigation speed.
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Proceed to production hardening and managed operation only once the proof of concept has demonstrated measurable value.
Product journey
Validate
A focused six-week proof of concept using your historical and live data.
Go into production
Production hardening once the proof of concept has demonstrated measurable value.
Operate
Managed operation with continuous tuning and expansion.
Detection and investigation
Three layers that complement each other
Rules for known risks
Detect known fraud patterns using predefined rules and thresholds.
Machine learning for unusual behaviour
Identify unusual behaviour that falls outside static rules.
AI-assisted triage
Gather context, weigh the evidence and help identify the alerts that need a person. Likely false positives can be documented and closed automatically. Evidence and reasoning are recorded for every decision.
Technology partners
Fraud detection built natively in Elastic
The service uses your existing Elastic Stack, with investigation workflows directly in Kibana.
Frequently asked questions
Good to know before your proof of concept starts
No. The service complements your rule-based controls with machine learning, AI-assisted triage and workflow automation inside your existing Elastic Stack.
The service uses transaction, authentication, device, user and security data already available in Elasticsearch. The six-week proof of concept uses your historical and live data to validate the approach.
Yes. Likely false positives can be documented and resolved automatically. Evidence and reasoning are recorded for every decision. Alerts requiring human attention are passed to analysts with the context gathered during triage.
No. The service is built natively within your existing Elastic Stack, with investigation workflows directly in Kibana. It does not require a separate fraud platform or major data migration.
Contact
Let’s discuss your next step
Tell us about your fraud detection priorities. Together, we’ll clarify the scope and how a proof of concept can validate the approach.
