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Unde

Prioritize real risk. Investigate with full context.

Unde combines your AML rules with behavioral analytics, anomaly detection, machine learning and graph analysis, so analysts spend their time on alerts that matter, with the evidence already assembled.

unde / aml / investigations / ALR08841
Alert
ALR08841
Customer
C20418 · Individual · Retail wallet
Priority: HighUnder reviewOpened 09:14
Risk score
84
High risk
Behavior
.31
Network
.27
Device
.14
Geography
.12
Risk indicators5
  • Rapid movement of funds
    84% of inbound funds sent out within 6h
    High
  • Multiple new beneficiaries
    5 added in 72h · 4 never paid before
    High
  • New device
    DEV77 first seen 3 days ago
    Med
  • Unusual transaction volume
    6.2× the 90 day daily average
    Med
  • Cross border behavior deviation
    First transfers to 2 new corridors
    Med
Transaction graph · 72h2 linked accounts
48,00037,50012,00041,20038,90044,80049,500C20418DEV77 · new deviceACC5521ACC6630B01B04B05cross borderB06cross borderB07Amounts in QAR · dashed = shared device
Customer behavior · outbound QAR k / day
baseline avg · QAR 4.0klast 72h · QAR 186.4k
Devices
DEV77 · Android
First seen 3 days ago
New
DEV12 · iOS
Seen for 412 days
Known
Connected accounts
ACC5521
Shares DEV77
Linked
ACC6630
Shares DEV77
Linked
AI investigation summary

Activity is consistent with a pass through account pattern. Inbound transfers from two accounts sharing device DEV77 E1 were forwarded to five new beneficiaries within 6 hours E2. Volume is 6.2× the customer's baseline E3 and includes first time cross border corridors E4. KYC is current; no prior cases.

Suggested next action

Escalate to L2 review. Request source of funds from the customer.

AI recommends. Analyst approval required.

Rules find what you defined. Intelligence finds what you didn't.

Unde does not replace your AML system. It adds behavioral, statistical and network context to the alerts it produces, and surfaces risk that single rule scenarios miss.

Rules

Your existing scenarios and thresholds, kept intact and enriched with context from every connected system.

Behavioral analytics

Each customer measured against their own history and their peer group, not a single global threshold.

Anomaly detection

Deviations in volume, velocity, counterparties, channels and geography surfaced as they occur.

Machine learning

Risk scoring trained on your outcomes to rank alerts by the likelihood that they matter.

Graph analysis

Linked accounts, shared devices and beneficiary networks discovered across the Financial Operations Graph.

Prioritized alert queue

Analysts start with the alerts that carry real risk, with fewer low value alerts to clear.

Focused on meaningful risk.

01

Customer behavior

  • Customer behavior profiling
  • Transaction anomaly detection
  • Risk scoring
02

Network

  • Network analysis
  • Linked account discovery
  • Device relationship analysis
  • Beneficiary pattern analysis
03

Typologies and triage

  • Money mule pattern detection
  • AML alert prioritization
  • False positive reduction

The investigation is drafted before the analyst opens the alert.

When an alert is opened, the AI Investigator gathers context from every connected system and prepares an investigation backed by evidence. The analyst reviews, challenges and decides.

AI gathers

  • Customer profile
  • Transaction history
  • KYC status
  • Device information
  • Connected accounts
  • Beneficiaries
  • Historical behavior
  • Relevant risk indicators
  • Existing cases

AI produces

Investigation summary
A concise narrative of what happened, written for the analyst and the file.
Risk factors
Each factor with its weight and the records that support it.
Behavior deviation analysis
Current activity compared with the customer's own baseline.
Connected entity analysis
Accounts, devices and beneficiaries linked to the customer.
Evidence
Every source record referenced, versioned and retrievable.
Suggested next action
Escalate, request information or close, for the analyst to approve.

AI recommends. Humans approve.

No silent decisions

AI does not close, escalate or file on its own. Every outcome has a named approver.

Challenge built in

Analysts can reject a recommendation or request more evidence, with rationale recorded.

Filings stay human

Suspicious activity reports are drafted for review and submitted only by your team.

Patterns that only appear when entities are connected.

Examples of behavior Unde is designed to surface across the Financial Operations Graph.

Pass through accounts

Inbound funds forwarded within hours to new or unrelated counterparties.

Money mule networks

Clusters of accounts sharing devices, beneficiaries or onboarding attributes.

Beneficiary fan out

A sudden increase in new beneficiaries, often across new corridors.

Shared device clusters

One device accessing multiple customer accounts with no declared relationship.

Structuring

Amounts split to stay below reporting or monitoring thresholds.

Give your analysts the full picture.

See how Unde connects to your existing systems, investigates alerts and incidents with full context, and keeps every decision auditable.