A fraud intelligence layer, built in parts that can be reasoned about.
The platform is being designed as a set of components with clear responsibilities: deterministic controls the institution owns, models that add judgement, analytics that expose structure, and a workspace where a person reaches a decision that can be defended afterwards. What each component produces is described here; how they are combined internally is not.
Detection
Rules, ML, anomaly, graph
Output
Score, evidence, recommendation
Decision
Authorised bank personnel
Record
Versioned and auditable
01
The fraud intelligence engine
Four disciplines evaluate the same transaction, each contributing a different kind of reading, and each keeping the reasoning that produced it so the final position can be explained rather than asserted.
Four independent evaluations of the same transaction, resolving into one position.
What the institution receives
Risk score
Contributing evidence
Plain-language explanation
Recommended action
How these are sequenced, combined and weighted is our own work, and is not published. What we do publish is the output: one risk position, the evidence behind it, and a person who decides.
02
Capabilities
What each part of the platform is responsible for, and what it is designed to produce.
Deterministic controls the institution owns, evaluated ahead of any model.
Bank-defined thresholds, lists and conditions
Versioned rule sets with change history
Rule outcomes carried into the final explanation
Designed so a rule can be added without a model release
02
Machine learning
Supervised models that score fraud probability from historical outcomes.
Contribution to the score retained for every event
Training on institution-specific labelled outcomes
Candidate models validated before any production release
03
Anomaly detection
Deviation measured against an account's own established behaviour.
A behavioural baseline per account, rather than one shared threshold
Detection that does not depend on a labelled example existing first
Designed to reduce dependence on known fraud examples
04
Graph intelligence
Accounts, devices, beneficiaries and paths analysed as a network.
Entity resolution across identifiers
Community and cluster detection
Relationship density and transaction pathways
05
Risk decisioning
One consolidated risk position with a recommended course of action.
One consolidated position across every detection method
Configurable thresholds for alerting and prioritisation
Recommendation, not automated customer action
Alert prioritisation designed around analyst capacity
06
Explainability
The evidence behind a score, in reviewable language.
Signal-level contribution to the final score
Plain-language indicators for each contributing factor
Evidence retained with the case record
Written for investigators, reviewers and auditors alike
07
Case management
The workflow from alert to documented outcome.
Queues, assignment and status tracking
Investigation notes and attached evidence
Outcome capture with reason codes
Designed to fit existing fraud team process
08
Model governance
Control over what is deployed, and when.
Versioning across models, features and rules
Performance and drift monitoring
Validation gates ahead of release
Institutional approval in the release path
09
Audit & reporting
A reconstructable record of how each decision was reached.
Immutable action and decision trail
Model version attached to every scored event
Operational and case reporting
Built for internal audit and supervisory review
03
The workspace
Interface concepts for the teams who would work the queue: dense, traceable, and organised around the decision rather than around the chart.
TasawurAI/Risk overview
Role: Fraud analystSample data
Open alerts
34
+6 last hour
In review
12
4 analysts
Escalated
5
2 to compliance
Closed today
21
9 confirmed
Alert volume by hourLast 24h
00:0012:0023:00
By typology
Account takeover38%
Mule network24%
Velocity19%
Structuring12%
Other7%
Reference
Time
Amount
Channel
Score
Band
Status
PMT-20260918-4421
14:22:07
12,480.00
Instant
82
High
Open
PMT-20260918-4408
14:19:51
3,150.00
Card
74
High
In review
PMT-20260918-4396
14:16:33
890.00
Mobile
61
Elevated
In review
PMT-20260918-4381
14:11:02
22,000.00
Branch
58
Elevated
Open
PMT-20260918-4370
14:04:48
410.00
Mobile
44
Elevated
Assigned
PMT-20260918-4352
13:58:19
1,275.00
Card
27
Low
Closed
Interface concepts in development. All references, amounts and scores shown are sample data.
04
Built for accountable AI.
Every capability described here is being designed against the same constraint: a regulated institution has to be able to explain, monitor and reconstruct any decision the platform contributed to.