IDYC360 helps banks, payment providers and lending institutions understand entity behaviour, assess emerging risk and explore potential mule networks through behavioural, geo-behavioural and graph-based intelligence—with privacy central to its design.
Conceptual illustration of connected context.
Activity gains meaning through the entities involved, their behaviour over time and the relationships between them. IDYC360 brings these perspectives together to help teams understand connected risk and identify where to investigate next.
Support fraud and investigation teams with a connected view of entity behaviour, beneficiary relationships and potential mule activity.
Discuss a banking use case ↗Activity spread across entities and accounts can be difficult to interpret when each transaction is examined in isolation. A beneficiary’s changing behaviour and surrounding relationships can add important context.
Assess behavioural changes around payers and beneficiaries to identify activity that warrants review.
Explore shared counterparties and connected patterns that may indicate coordinated activity.
Consider available geographic attributes alongside behavioural and relationship signals.
Use graph-based analysis to examine the wider context around an entity of interest.
A beneficiary starts receiving payments from a widening group of entities. Reviewing the change alongside the participants’ behaviour and shared connections helps an analyst decide where to investigate next.
Help risk teams interpret payment activity through entity behaviour and connected relationships, with privacy central to the analytical approach.
Discuss a payments use case ↗Payment events can appear unrelated even when they form part of a wider pattern. Understanding how payers and beneficiaries interact helps teams assess emerging risk beyond a single payment.
Consider new payment activity in relation to an entity’s previous patterns and relationships.
Explore changes in counterparty networks and repeated interactions between participants.
Bring behavioural and network insights into timely risk assessment, within the agreed data and integration scope.
Review contributing signals alongside risk scores to support investigation and prioritisation.
Several payers begin interacting with the same set of beneficiaries. An entity-centred view helps a risk team explore the shared connections and distinguish a pattern worth reviewing from isolated events.
Explore behavioural and relationship patterns around disbursements and repayments where relevant data is available, helping teams investigate potential fraud.
Discuss a lending use case ↗Individual disbursement or repayment records may not reveal relationships between participants. Bringing those records into an entity-centred view can help a fraud team examine patterns that need closer attention.
Explore beneficiary relationships around disbursement activity using the institution’s available data.
Examine behavioural changes and shared participants in repayment activity where the data supports it.
Use network context to investigate repeated connections across relevant entities.
Use explainable risk signals to guide fraud investigation alongside the lender’s existing processes.
A lender observes repeated connections between entities involved in otherwise separate disbursement or repayment activity. Exploring those relationships can help its team assess whether further investigation is needed.
Focused on fraud intelligence and investigation support, rather than creditworthiness assessment or default prediction.
The same entity-centric perspective supports different institutional challenges. The data available, the investigation workflow and the evaluation scope shape each deployment.
Keep privacy central to the handling of data and the design of intelligence workflows.
Connect risk assessments with behavioural and network signals that analysts can examine.
Help teams explore relationships and prioritise questions that deserve further review.
Agree on the problem, the data and the evidence needed to assess whether IDYC360 fits your institution.
Identify the fraud pattern or investigation question your team wants to examine.
Review data availability, privacy requirements and the intended integration workflow.
Assess the relevance, interpretability and operational usefulness of the results against agreed objectives.
Examples on this page are illustrative, not customer case studies. Risk signals support investigation and do not establish fraud. Available insight depends on the data provided and the agreed deployment scope.
Have a related investigation or research use case?
We welcome discussions with teams exploring behavioural and network-based fraud intelligence.
Discuss your use case, available data and evaluation goals with our team to define a focused pilot.