Hidden Fraud Rings Are Costing Your Portfolio.
We Find Them.
Forensic audits for NBFCs using historical data. No system integration required.
Forensic audits for NBFCs using historical data. No system integration required.
Finding Coordinated Borrower Rings That Rules & Bureau Checks Miss
Kael Defense performs retrospective forensic audits on historical borrower and lending data to surface coordinated fraud rings, shared guarantors, application clusters, and disbursement networks missed by rules, CIBIL, and EWS.
Forensic Visibility into Borrower Risk
Analyse historical borrower, guarantor, device, and application data
Data that reveals hidden fraud patterns
Analyse borrower behavior, application timing, guarantor overlaps, disbursement routing, velocity anomalies, and emerging fraud networks with precision.
Forensic Investigation into loan Portfolio Risk
Surface hidden borrower networks and disbursement patterns buried in your historical loan data.
Forensic capabilities built for NBFC fraud intelligence
Kael Defense combines forensic graph analysis, behavioral modeling, and risk scoring on historical borrower and lending data — with full data control and explainable intelligence — to surface coordinated fraud rings, hidden networks, and anomalies missed by rules and bureau checks.
Secure Offline Deployment
We run our engine in a secure, offline environment using historical lending and borrower data, with full data control and alignment with internal NBFC compliance workflows.
Behavioral Analytics
Analyse application timing clusters, disbursement velocity spikes, geographic anomalies, and behavioral shifts across historical borrower and lending data.
Forensic Risk Signals
Identify suspicious application patterns, guarantor overlaps, disbursement anomalies, and velocity patterns during retrospective audits to guide deeper investigation.
Device & IP Graph
Visualize relationships between borrowers, guarantors, devices, and IPs using graph analysis to uncover coordinated fraud rings and hidden networks missed by rule engines.
Explainability Engine
Understand exactly why a borrower cluster or application was flagged through clear, investigator-friendly explanations backed by graph signals and behavioral indicators.
Borrower & Network Risk Scoring
Generate forensic risk scores for borrowers, guarantors, and networks during retrospective audits by combining behavioral indicators, linkage strength, anomaly detection, and graph centrality metrics.
HOW KAEL ARCHITECTS COORDINATED FRAUD DETECTION
1
Data Ingestion
We securely ingest historical borrower applications, guarantor details, disbursement records, device fingerprints, and IP logs from internal systems for offline forensic analysis
2
Behavioral Analysis
The data is used to analyze historical borrower behavior to identify application velocity clusters, disbursement spikes, guarantor overlaps, and geographic inconsistencies that indicate coordinated fraud.
3
Anomaly Detection
High-risk borrower clusters and networks are flagged using graph-based signals, linkage scores, and behavioral deviations that traditional rules, CIBIL checks, and EWS failed to surface.
4
Insights & Alerts Delivery
Explainable risk scores, visual borrower network graphs, and investigation-ready insights to support deeper review, case management, and RBI-compliant reporting are generated
Borrower Activity Timeline
Forensic view of historical borrower applications, disbursement velocity, repayment patterns, and behavioral anomalies uncovered through offline analysis.
Behavioral Insights
Analyze application velocity, guarantor overlaps, disbursement clustering, and coordinated fraud signals across large historical borrower datasets.
Ingests Your Historical Lending Data
Ingest historical exports from core lending systems, loan management software, or internal databases. No live integration or API required during forensic audits.
Anomaly Signals Over Time
Visualize risk scores and linkage signals evolved over historical periods to understand how coordinated fraud rings formed and spread.