DeepFrauds.AI — Most Innovative Fraud Detection & Prevention 2024

Six Specialized AI Models.
Full Enterprise Fraud Coverage.

Supervised learning · Anomaly detection · GenAI narrative — each model built for its fraud domain.

From invoice manipulation to payroll ghost employees — six AI models combining statistical detection, predictive risk scoring, and GenAI explanation. Each model continuously adapts to your transaction patterns. Each finding narrated in auditor-ready language with fraud typology mapping.

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LightGBM · Supervised SOM · Anomaly Detection Ensemble Methods GenAI Narrative ACFE Standards Real-time Scoring
6
Specialized AI models — one per fraud domain
85%
Recall on confirmed fraud cases
LightGBM supervised · labeled dataset
<5%
False positive rate on anomaly models
SOM · tuned per client population
100%
Transaction coverage — no sampling
Real-time + batch modes
6 Fraud Models
One Model Per Domain. Built for Depth, Not Breadth.
Each model is purpose-built for its fraud scheme — trained on domain-specific transaction patterns, tuned to your industry, and narrated in the language your compliance team actually uses.
Supervised Classification · LightGBM + Ensemble
Invoice Payment Fraud AI
Continuous learning fraud detection for every payment transaction — with precision risk scoring and real-time alerts.
Real-time Supervised
Detects duplicate invoices, split transactions, and fictitious supplier patterns across ERP data
Continuously learns from confirmed fraud labels — model retrains on validated alerts from your team
Precision risk score per transaction: probability estimate calibrated on your historical fraud rate
Vendor network graph analysis — flags unusual payment routing, new bank accounts, address clustering
GenAI explanation layer — each alert narrated with typology classification and recommended next action
Recall 85% · Precision 97% F1 Score 0.91 ACFE Invoice Fraud
Performance Metrics
Recall
85%
Precision
97%
F1 Score
0.91
Metrics from supervised evaluation on labeled fraud dataset. Calibrated per client during PoC on actual transaction history.
Live Alert Sample
HIGHVendor #4821 · €14,200 · duplicate ref
MEDNew bank account · 3rd payment today
Hybrid · Statistical Rules + GenAI Reasoning
Accounting Fraud AI
Statistical anomaly detection on general ledger data — combined with GenAI contextual reasoning for each flagged entry.
Anomaly Detection GenAI
Ledger anomaly detection — identifies entries that deviate significantly from historical distribution patterns
Unusual account pair detection — unexpected debit/credit combinations, cross-entity postings
Volume and timing analysis — off-hours entries, period-end clustering, approval bypass patterns
Combines statistical z-score outlier detection with predictive analytics trained on ACFE fraud typologies
GenAI explains each anomaly in audit language: what is unusual, what it could indicate, what to verify
Anomaly Score Z-Score Outliers ACFE Typologies
Detection Output
Anomaly thresholdσ > 2.5
Alert precisionAnalyst-validated
Coverage100% of entries
Anomaly models report outlier scores, not binary fraud labels. Precision is measured on analyst-confirmed alerts post-review — not on unlabeled data.
Anomaly Sample
σ=3.8Weekend credit · unusual account pair
σ=2.9Period-end manual override · €48K
Supervised + Rule-based · Expense Pattern Analysis
Reimbursement Fraud AI
Detects suspicious expense claim patterns and produces calibrated fraud risk scores for every reimbursement request.
Pattern Analysis Risk Scoring
Duplicate claim detection — same merchant, date, amount across multiple submissions and employees
Policy breach flags — over-limit claims, unauthorized categories, weekend/holiday clustering
Behavioral outlier detection — individual claim patterns compared to peer group and historical baseline
Receipt image analysis integration — flags mismatched amounts, altered dates, reused receipts
Risk score per claim: low / medium / high — calibrated to your expense policy and historical fraud rate
Peer Benchmarking Policy Enforcement Receipt Validation
Risk Distribution
High risk
8%
Medium risk
15%
Low / clean
77%
Typical distribution on enterprise expense dataset. High-risk tier is analyst-reviewed; clean tier is auto-approved. Reduces manual review by ~77%.
Alert Sample
HIGHSame receipt · 3 claims · 2 employees
MEDWeekend dining · 4.2× peer average
GenAI + Predictive Analytics · Quantitative Models
Financial Statement Fraud AI
Earnings manipulation detection combining established quantitative models with LLM contextual interpretation.
Beneish M-Score Predictive
Beneish M-Score (8-variable) — earnings manipulation probability from DSRI, GMI, AQI, SGI, DEPI, SGAI, TATA, LVGI
Altman Z-Score — financial distress prediction, going concern early warning signal
Ratio trend analysis — year-over-year deviation detection across key financial ratios
Fictitious revenue patterns — channel stuffing, premature recognition, bill-and-hold indicators
GenAI scenario reports — narrates manipulation hypothesis, cites relevant PCAOB procedures, scores overall risk
Beneish M-Score Altman Z-Score PCAOB AS 2401
Quantitative Signals
M-Score threshold< -2.22
Z-Score distress< 1.81
DSRI concern> 1.31
Beneish (1999) thresholds applied. M-Score < -2.22 = probable manipulation. Model interprets component scores — Python computes all values, LLM narrates.
Output Sample
HIGHM-Score: -1.78 · manipulated zone
WATCHDSRI: 1.47 · revenue inflation signal
Unsupervised · Self-Organizing Map (SOM)
Supplier Payments AI
Self-Organizing Map (SOM) neural network for real-time anomaly detection across all supplier payment transactions — no fraud labels required.
SOM Neural Net Unsupervised
SOM clusters normal transaction patterns from your supplier history — new anomalies surface automatically without pre-labeled fraud data
Real-time scoring: each new transaction mapped to the trained topology — distance from nearest cluster node = anomaly score
Ghost supplier detection — payment patterns inconsistent with legitimate vendor behavior (timing, amounts, frequency)
Change detection — model flags when a known supplier's transaction pattern shifts significantly over time
Works from day one — no historical fraud labels needed, model bootstraps on clean transaction history
SOM · Unsupervised Anomaly Score <5% False Positives
Anomaly Detection Output
Detection methodCluster distance
False positive rate< 5%
Coverage100% of txns
SOM outputs distance-from-cluster scores, not fraud probabilities. Threshold is tuned per client population. FPR <5% achieved after calibration phase (typically 2–4 weeks).
Alert Sample
OUTLIERVendor #882 · dist=4.7 from cluster
DRIFTVendor #114 · pattern shift detected
Supervised + Anomaly Detection · HR + Payroll Cross-check
Payroll Fraud AI
Automatically recognizes fraud patterns and red flags inside enterprise payroll datasets — from ghost employees to unauthorized rate changes.
HR Cross-check Ghost Detection
Ghost employee detection — payroll entries with no HR record, no access badge swipes, no system logins
Unauthorized compensation changes — rate modifications outside approved HR workflow, backdated adjustments
Duplicate employee detection — same bank account, address, or SSN fragment across multiple payroll records
Overtime and bonus anomalies — statistically unusual patterns compared to role, department, and seniority peers
Model reduces cost of payroll fraud by surfacing high-risk payslips for review before payment runs
Ghost Employee Rate Manipulation HR Integration
Fraud Signal Types
Ghost employee
38%
Rate manipulation
27%
Duplicate records
19%
Bonus anomaly
16%
Distribution of confirmed payroll fraud typologies across ACFE occupational fraud case studies — used to weight model training priorities.
Detection Philosophy
The Right Method for Each Fraud Type.
Not all fraud is detectable the same way. Supervised models need labeled fraud history. Anomaly models work without it. GenAI explains what neither can narrate alone. We use each where it belongs.

Supervised Learning

When you have confirmed fraud labels. LightGBM trains on your historical fraud cases — producing calibrated probability scores. Recall, Precision, and F1 are meaningful because ground truth exists.

Invoice Fraud Payroll (hybrid)

Anomaly Detection

When fraud labels are scarce or absent. SOM and statistical methods learn what "normal" looks like — then surface what doesn't fit. Output is an anomaly score, not a fraud probability.

Supplier Payments (SOM) Accounting Reimbursement

GenAI Narrative

When numbers alone aren't enough. LLMs receive structured model outputs and produce auditor-ready explanations — typology classification, recommended procedures, and risk narrative. Python always computes; LLM always explains.

All 6 models
Architecture Principles
Built for Reliability, Not Just Performance.
Two rules that govern every model in the suite — non-negotiable.

Why These Rules Matter

Financial fraud detection has consequences. A wrong alert costs analyst time and damages supplier relationships. A wrong explanation misleads an auditor and creates liability. Every design decision in the suite is made with these stakes in mind.

The combination of deterministic arithmetic and honest model output means every finding can be reproduced, contested, and explained to a regulator — without the black-box problem that makes most AI fraud tools unusable in audit contexts.

Python calculates. LLM narrates.

Every numeric result — scores, ratios, thresholds, amounts — is computed deterministically in Python. The LLM receives only structured outputs and generates language. It never performs arithmetic.

Honest model output language.

Supervised models report Recall, Precision, F1 — because ground truth exists. Anomaly models (SOM, statistical) report anomaly scores and false positive rates — not fraud probabilities. We never claim certainty we don't have.

Human validates. AI surfaces.

No model makes a final fraud determination. Every high-risk alert routes to an analyst. Approval workflows ensure human sign-off before any case escalation or regulatory report.

DeepFrauds.AI — Most Innovative Fraud Detection & Prevention 2024

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Run the Enterprise Fraud Suite on your own transaction data. Models calibrate on your actual fraud patterns during a 30-day PoC. No labeling work required to get started.

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