GJEF NexusAI™ prepares ingested ledgers and transaction feeds to full AI-readiness, then runs explainable outlier detection so teams can approve, reject, or escalate findings with a clear audit trail.
Detection only runs after the same prepare step used across NexusAI™: standardise, dedupe, and impute so readiness is lifted before modelling. Fraud is one classification path of the production AI Detection Pipeline.
Numeric outliers above the median on financial columns are prioritised as fraud candidates; below-median patterns may surface as leakage. Each finding carries category, monetary exposure, record reference, confidence, and an AI explanation.
A transparent, rule-weighted simulator for education — not a live model score. Production detection uses prepared datasets and statistical outlier analysis inside NexusAI™.
Adjust factors that commonly drive fraud risk scores in financial workflows.
Relative weight of each input in this demo’s scoring function.
Detection runs after readiness scanning and prepare steps so models are not fed raw, inconsistent columns.
Each anomaly includes category, exposure, record reference, confidence, and a plain-language AI explanation.
Approve, reject, or escalate; decisions are stored and written to the governance audit trail.
Critical / high / medium / low with open, reviewing, and resolved workflows for operations.
Project impact from detected signals with trend views and narrative interpretation.
The in-product assistant can explain the pipeline and walk reviewers through open anomalies.
Subscribe to NexusAI™, connect sources, complete readiness, then execute the detection pipeline with full auditability.