FenoriTechnologies
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Case Intelligence

Start with the case already understood.

We built Case Intelligence: a system that resolves an incoming signal to the institution’s canonical entity and assembles the full institutional context (current risk state, transaction and behavioral history, counterparties, ownership, prior alerts and dispositions, related cases and supporting evidence) into the case before an investigator opens it.

StatusReference architecture
Institutional problemAlert investigation begins without cross-system context
Technical foundationEntity resolution, cross-system context, graph, evidence provenance
Existing systems retainedMonitoring, screening, KYC and case management
Human authorityInvestigator determines disposition
Evaluated onResolution accuracy, preparation time, evidence completeness
DemonstrationArchitecture walkthrough on request
DataSynthetic
DeploymentInstitution-governed AWS environment
TypeOperational intelligence systemDomainFinancial servicesStatusReference architectureComponents7

The institutional problem: alert triage without context.

A raw alert is rarely enough to make a decision. It identifies a transaction, a screening match or another isolated signal, but the institution usually knows far more about the customer than the system generating the alert can see.

That knowledge is fragmented across transaction monitoring, KYC, screening, payments, fraud and case management. So the first hours of a financial crime investigation are spent reconstructing what the institution already knows, one system at a time. The investigation starts with gathering, not judgment.

Inputs and source systems.

The system consumes signals and records the institution already produces. An incoming signal is not treated as a complete case; it becomes the starting point.

  • Transaction monitoring alerts
  • Screening and watchlist matches
  • Fraud signals
  • KYC and registry data
  • Payments and counterparty records
  • Prior alerts, dispositions and cases

How the system is composed.

What happens when an event enters.

Existing institutional systemsTransaction monitoringKYC and registry dataScreeningPayments and counterpartiesFraudPrior alerts and cases
↓
Entity resolutionOne entity across multiple systems
↓
Case intelligenceRisk stateNetwork and relationshipsTransaction and behavioral historyPrior alerts and dispositionsRelated casesRelevant patternsSupporting evidence
↓
Prepared case
↓
Human analysis and judgment

Core technical components.

  • Entity resolution
  • Cross-system context assembly
  • Risk-state retrieval
  • Network and relationship graph
  • Evidence retrieval with provenance
  • Prepared-case handoff
  • Human authority layer

Resolution and assembly logic

When a signal arrives, the first operation is resolution: determining which entity the signal actually concerns. The same customer may appear differently across KYC systems, payments infrastructure, screening tools and case management. The system resolves those references to a canonical entity so information held in separate systems can be read together. Where resolution is ambiguous, the ambiguity is surfaced to the investigator rather than silently merged.

Example: a cross-system investigation.

A screening system raises a near match on Halvex Marine Supply AS, a mid-sized corporate customer. On its own, the alert carries a name-similarity score and little else. Entities and figures in this walkthrough are synthetic.

  1. The signal enters and resolves to the canonical entity: Halvex appears under three name variants across KYC, payments and the case system. Resolution binds them to one entity record.
  2. Context assembly retrieves the current risk rating, twenty-six months of transaction behavior, the ownership chain, and a prior alert on counterparty Corvette Trading FZE closed eight months earlier as a false positive.
  3. The cross-system view surfaces what no single source showed: Corvette Trading FZE now appears as a 40% owner in Halvex’s updated ownership chain, registered three weeks before the flagged payment pattern began.
  4. The prepared case opens with that picture assembled (entity, network, history, prior decisions and the new intersection), each element carrying its source and retrieval record.
  5. The investigator’s first minutes go to the question that matters: whether the ownership change and the payment pattern together warrant escalation.

How it sits inside the institution.

Evidence and audit behavior

The system records which sources were queried and which evidence was retrieved, so every element of a prepared case can be traced to the information it was assembled from. A prepared case is not a black box: the investigator can see why each piece of information is there.

Where human authority remains

The officer determines what the evidence means and what consequential action, if any, follows. The system removes the preparation around judgment, not the judgment.

Working with what already exists

Case Intelligence does not require an institution to remove functioning monitoring, screening, KYC or case systems. They remain the sources. The system becomes relevant where the outcome requires the institution to understand that information together.

What the system outputs.

  • A prepared case: resolved entity, assembled context, contributing signals, prior decisions and evidence
  • A provenance record for every retrieved element
  • A handoff into the institution’s existing case workflow

What it deliberately does not do.

  • It does not require replacing functioning monitoring, screening, KYC or case systems: they remain important sources of institutional information.
  • It does not generate alerts or replace existing controls; the incoming signal is the starting point, not the product.
  • It does not make dispositions. The officer determines what the evidence means and what consequential action, if any, follows.
  • It does not assemble context invisibly. Every element of a prepared case is traceable to the sources and evidence from which it was built.

The institution should not have to rediscover what it already knows every time an alert fires.

Related builds

Built around your problem, not this one.

An institution may need this build, part of it, or something entirely different. The institution determines the outcome. The technology follows.

Discuss this outcome