Risk & assurance
Fraud Control System
AI-powered fraud detection.
From first signal to final action.
An AI-driven Fraud Detection Engine continuously analyses field activity, movement and claims to flag suspicious patterns early, then guides each alert through review, investigation and closure. AI supports the checks where it adds value, while authorised teams review the evidence and decide the outcome.
Plan your product walkthroughDetecting an unusual pattern and resolving a reported incident are different jobs. An alert needs context; an investigation needs evidence, authorised reviewers and a record of the decisions made. Arukus brings those two needs into a connected operating approach.
AI is applied to suitable detection and review tasks alongside configured checks. It helps teams identify patterns faster and focus investigation effort, while authorised reviewers remain responsible for examining the evidence and deciding the outcome.
What better could look like
Keep signals and evidence distinct
Review unusual activity before deciding whether investigation is warranted.
Give investigation work structure
Retain case references, committee responsibilities and decisions.
Make follow-through visible
Review actions alongside the investigation record.
The people using the product
Reviewer
Examines an exception and its source context.
Investigator
Organises evidence and investigation follow-up.
Committee member
Reviews the case and records decisions.
Authorised management
Reviews outcomes and assigned actions.
A signal starts a review.
Make the route from an exception to an investigation explicit.
Exception signal
Movement and collection rules identify a record needing attention.
TO EXPLORE
Examine the signals behind a collection exception
The early-warning approach compares field-agent stops and movement with scheduled locations and updates in the application. Rules can examine a stop without a corresponding activity record, a mismatch with the expected location, proximity to certain customer records or gaps in location data.
The rule engine generates alerts by severity. Authorised reviewers can inspect the assigned task, activity updates and location context, then change the severity or mark an alert as no fraud. A weak signal, a device problem and a genuine concern should not be treated as equivalent.
Give every reported incident a case record
Fraud concerns can be reported directly or referred from the audit workflow when an authorised approver confirms an observation for investigation. Submission creates a case reference and notifies the relevant users according to configured action-wise email lists.
The case moves into an investigation process with an assigned committee. Access is controlled at menu and activity level, allowing the organisation to distinguish who may create, edit, review or perform a particular action. Editing the initial incident is restricted once the investigation committee is established in this process.
Keep committee review and decisions together
Regional, zonal and apex committee workflows support meeting scheduling and participation. Meeting records capture discussion, recommendations and decisions, while authorised members can provide their opinions at the relevant stage.
This matters when a case requires more than one level of review. The next committee should be able to understand the earlier recommendation and the supporting record, rather than reconstructing the matter from separate emails. The exact committee structure and escalation route are configured to the organisation.
Follow the recommended action
Once decisions are completed, responsible people are notified of their action items and update progress in the application. The actionable report shows status, pending work and overdue dates, with filters for office, status and case reference. Incident details and reports can be downloaded for authorised review.
A case should not disappear from management attention simply because a meeting has concluded. The action owner, due date and completion evidence need to remain visible until the agreed response is recorded.
Scope the right fraud workflow
Collection-related signals are documented for lending operations. Other enterprises may need an incident-investigation workflow for their own processes, but detection rules cannot be assumed to transfer unchanged. Discovery should establish available data, access, investigation roles, confidentiality and the review criteria before promising a new detection scenario.
What we can cover in a walkthrough
- Identify a concern
- Gather the context
- Review the evidence
- Record the outcome
Useful examples to bring: An anonymised incident; investigation stages; action report
Where the product can help
A collection record that needs investigation
A lending team sees an inconsistency and needs to examine the related activity. A structured review keeps the concern, supporting information and responsible investigator connected.
An operational exception outside lending
An organisation may want to examine unusual expenses or field records. The first step is to establish whether the required data, signals and investigation workflow fit the product.
Questions for a considered decision
Does an alert mean fraud has occurred?
No. An alert indicates an exception requiring examination. Authorised reviewers inspect the context, adjust severity or mark it as no fraud.
Can an audit observation become a fraud case?
The supplied workflow supports referrals from audit when an authorised approver confirms an observation for the fraud-management process. The modules and integration must be in scope.
Can we use our own committee structure?
The documented workflow includes regional, zonal and apex committees. We review your governance and decision responsibilities to establish the applicable configuration.
Does an alert mean fraud has been established?
An exception is a reason to examine a record. Reviewers need the source context, evidence and an authorised investigation process before reaching a conclusion or deciding an action.
How should we test the investigation process?
Use anonymised examples covering a valid concern and an explainable exception. Follow assignment, evidence review, committee decisions and closure, including who may see sensitive case material.
What does implementation involve?
We agree configuration, data preparation, required interfaces, acceptance examples, training and rollout responsibilities. The delivery plan follows the scope.
How are deployment and integrations decided?
We review the chosen product, available interfaces, hosting requirements and operating owners. A named external system is not assumed to be connected out of the box.
Capabilities to explore in your walkthrough
- 01Movement and activity-based collection exception rules
- 02Severity-based alerts and reviewer assessment
- 03Direct incident reporting and audit referrals
- 04Investigation committee assignment
- 05Regional, zonal and apex committee workflows
- 06Meeting records, action owners and case reporting
Earlier signals. Clearer investigations.
AI supports appropriate detection and review tasks, helping teams identify suspicious patterns, organise relevant records and focus attention on cases that need investigation. The investigation team reviews the evidence and retains responsibility for findings, decisions and closure.
Discuss an AI pilotSee Fraud Control System in the context of your business.
Bring a sample process, a recent operating report and the people responsible for the work. We will focus the walkthrough on your requirements, available configuration and implementation scope.
Plan your product walkthrough