Step 1: Learn Normal Behavior

ML model analyzes historical data to understand what "normal" looks like:

• Transaction patterns: Typical amounts, frequencies, vendors, approval paths
• Account behavior: Expected balance ranges, normal transaction types, seasonal patterns
• User activity: Standard working hours, approved access levels, typical transaction volumes
• Relationships: Correlations between variables (high revenue months = high commission expense)

Example: Analyzing 2 years of expense reports, model learns that typical hotel expenses in New York range $200-$400, meals $30-$80, transportation $15-$50. Employee patterns consistent (business travelers expense weekly, office staff rarely).

Step 2: Define Anomaly Threshold

Determine how far from normal constitutes an anomaly:

• Statistical approach: Flag points beyond 3 standard deviations from mean
• Percentile approach: Flag bottom/top 1% of observations
• ML-based scoring: Assign anomaly score 0-100, flag above threshold (e.g., 85)
• Adaptive threshold: Automatically adjust based on investigation outcomes

Tradeoff: Lower threshold catches more anomalies (higher sensitivity) but more false positives. Higher threshold reduces false positives but may miss subtle issues.

Step 3: Score New Data

As new transactions arrive, model calculates anomaly score:

New expense report: Hotel $850 (expected $200-$400, score: 92), meal $180 (expected $30-$80, score: 88), submitted by office employee who typically doesn't travel (score: 85). Combined anomaly score: 94—flag for review.

Model considers multiple factors simultaneously—amount, category, employee, timing, approval path—to identify unusual combinations even when individual elements might be acceptable.

Step 4: Alert & Investigation

High-scoring anomalies trigger alerts with context:

• What's unusual: "Hotel expense 3.2x higher than employee's historical average"
• Similar patterns: "12 other expenses from this employee in past 30 days, all flagged as unusual"
• Risk indicators: "Receipt image quality poor, weekend submission, immediate reimbursement requested"
• Recommended action: "Review receipts, validate business purpose, check for duplicate submissions"

Step 5: Continuous Learning

Investigation outcomes train the model:

• Confirmed fraud: Increase weight on similar patterns
• Legitimate but unusual: Adjust model to reduce similar false positives
• New normal: Company opened NYC office, higher hotel expenses now typical—model adapts

Model accuracy improves over time as it learns organization-specific patterns and investigator feedback.

1. Fraud Detection

Expense fraud: Employee submits fabricated receipts, duplicate expenses, personal charges as business expenses. Anomaly detection identifies: unusual expense patterns (frequency, amounts), receipt manipulation (image editing), behavior changes (expense volume spike).

AP fraud: Fictitious vendors, invoice schemes, payment diversion. Detection: New vendors with unusual payment terms, invoices without POs, bank account changes before payment, round-number amounts.

Payroll fraud: Ghost employees, unauthorized raises, time card manipulation. Detection: Employees with no associated activity, compensation changes without approvals, time entries inconsistent with badge swipes.

Impact: Detected $2.8M fraud scheme involving fictitious vendors that manual audits missed. Pattern: invoices just below approval thresholds, payments to newly created vendors, bank accounts in unusual jurisdictions. Anomaly score: 97.

2. Data Quality Monitoring

GL anomalies: Unusual journal entries, account balance spikes, out-of-pattern transactions. Detection: Entry amounts significantly different from historical (depreciation calculation error), postings to dormant accounts, entries without proper documentation.

Master data issues: Duplicate vendors/customers, data entry errors, incomplete records. Detection: Similar names with slight variations, addresses inconsistent with entity, missing required fields, tax IDs that don't validate.

Reconciliation exceptions: Items that don't match between systems. Detection: Transactions in one system but not another, amount discrepancies beyond expected tolerances, timing differences beyond normal.

Impact: Identified $400K GL error before month-end close—depreciation formula incorrectly applied to asset category. Caught because journal entry amount 15x higher than historical pattern for that account.

3. Operational Risk Identification

Process failures: Automation breakdowns, workflow exceptions, SLA violations. Detection: Processing time spikes, error rate increases, volume drops, queue backlogs.

System anomalies: Performance degradation, integration failures, data sync issues. Detection: Query response time increases, failed batch jobs, missing data loads, API error rates.

Control weaknesses: Segregation of duties violations, approval bypasses, unauthorized access. Detection: Same user creates and approves transactions, access to incompatible functions, after-hours activity by users who typically work business hours.

Impact: Detected invoice processing automation failure 18 hours after it began—flagged by anomaly in AP queue volume and processing time. Prevented month-end backlog by fixing issue early.

4. Revenue Leakage Detection

Billing errors: Undercharging, missing invoices, incorrect pricing. Detection: Invoice amounts below contract rates, customers not billed in expected cycles, pricing inconsistent with contract terms.

Contract compliance: Revenue recognition errors, milestone billing misses, renewal misses. Detection: Revenue amounts inconsistent with signed contracts, deliverables completed but not billed, auto-renewal dates passing without billing.

Discount/rebate abuse: Unauthorized discounts, double-counting rebates, stacking promotions. Detection: Discounts exceeding approval limits, customers receiving multiple concurrent promotions, rebate claims without supporting orders.

Impact: Recovered $1.2M annual revenue by identifying customers systematically undercharged due to contract upload error. Anomaly detection flagged billing amounts 35% below contracted rates.

5. Compliance Monitoring

Policy violations: Spending beyond limits, unauthorized vendors, inappropriate categories. Detection: Transactions violating policy rules, spend concentration with single vendor, categories inconsistent with department.

Regulatory compliance: AML red flags, sanctions screening, unusual transaction patterns. Detection: Transactions from high-risk jurisdictions, amounts structuring (just below reporting thresholds), counterparties on watchlists.

Tax compliance: Transfer pricing anomalies, nexus triggers, withholding errors. Detection: Intercompany pricing outside arm's length range, state-specific thresholds exceeded, withholding rates inconsistent with jurisdiction.

Impact: Avoided regulatory fine by identifying transactions with sanctioned jurisdictions before quarterly filing. Caught by anomaly detection flagging wire transfers to unusual countries from employee expense accounts.

6. Predictive Risk Scoring

Customer credit risk: Payment delays, financial deterioration, churn signals. Detection: Payment timing drift (historically 30 days, now 45+), reduced order frequency, support ticket volume spikes.

Vendor risk: Delivery issues, quality problems, financial instability. Detection: Late deliveries increasing, defect rates rising, payment terms tightening (requesting deposits, COD).

Project risk: Budget overruns, schedule delays, scope creep. Detection: Burn rate exceeding plan, milestone slippage, change order frequency, team turnover.

Impact: Early warning system identified 23 at-risk customers 60 days before payment default—allowed proactive credit management, reduced bad debt by $800K.

Statistical Methods:

Simple, interpretable, no training required. Calculate mean and standard deviation, flag points beyond 2-3 standard deviations. Works well for numeric data with normal distributions.

Limitation: Assumes normal distribution, struggles with seasonal patterns, can't detect multivariate anomalies (combination of factors all acceptable individually but unusual together).

Clustering-Based Detection:

Group similar transactions into clusters, flag items that don't belong to any cluster or form very small clusters. Identifies outliers without defining "normal" explicitly.

Example: Cluster expenses by amount/category/employee. Most form clear groups (frequent travelers, office staff, executives). Single expense doesn't fit any cluster—$5K "office supplies" from employee who typically expenses $200/month—flagged as anomaly.

Isolation Forests:

ML algorithm that isolates anomalies by randomly partitioning data. Anomalies require fewer partitions to isolate because they're different from majority. Fast, handles high-dimensional data well.

Best for: Large transaction volumes, multiple variables to consider simultaneously, real-time detection requirements.

Autoencoders (Deep Learning):

Neural network learns to compress data then reconstruct it. Normal transactions reconstruct accurately, anomalies have high reconstruction error because model hasn't learned their patterns.

Best for: Complex, high-dimensional data (transaction records with 50+ fields), subtle anomalies in patterns rather than individual values.

Time Series Analysis:

Models temporal patterns—daily sales, monthly expenses, quarterly revenue. Flags when new data deviates from forecasted values considering seasonality and trends.

Example: Revenue forecast for February: $2.4M ±$150K based on historical patterns. Actual: $1.8M—flagged as significant anomaly triggering investigation (major customer delayed order).

Graph-Based Detection:

Models relationships between entities (vendors, customers, employees, accounts). Flags unusual connections or patterns in relationship networks.

Example: Vendor A pays Vendor B who pays Employee C who approves Vendor A's invoices—circular relationship indicates potential collusion. Graph analysis identifies suspicious network structure.

1. Start with High-Value Use Case

Select initial use case with clear ROI and manageable scope. Expense fraud detection (high fraud losses, clear patterns) better starting point than complex revenue recognition anomalies. Build success, expand from there.

2. Ensure Data Quality

Anomaly detection requires clean historical data to learn normal patterns. If training data contains errors/fraud, model learns bad patterns. Cleanse historical data before training, or start with known clean subset.

3. Tune Sensitivity Appropriately

Balance false positives (alert fatigue, wasted investigation time) vs. false negatives (missed fraud/errors). Start conservative (higher threshold, fewer alerts), tune based on investigation hit rate. Target: 30-50% of flagged items confirm issues.

4. Provide Investigation Context

Don't just flag anomalies—explain why. "Transaction amount 5.2x employee's historical average," "Vendor bank account changed 2 days ago," "Similar pattern detected in confirmed fraud case #2847." Context enables faster, more effective investigation.

5. Close the Feedback Loop

Track investigation outcomes—confirmed fraud, false positive, legitimate but unusual. Feed back to model for continuous improvement. Models that learn from investigations outperform static approaches by 40-60%.

6. Monitor Model Performance

Track metrics over time: alert volume, investigation hit rate, fraud losses, time to detection. When performance degrades (hit rate drops), retrain model on recent data or adjust thresholds. Business changes require model adaptation.

7. Combine with Rules for Critical Controls

Use ML anomaly detection for discovery and pattern recognition, but maintain explicit rules for known critical controls. "Always flag wire transfers over $100K" regardless of anomaly score. Hybrid approach: rules + AI.

Challenge: "Too many false positives—team ignores alerts."

Solution: Increase anomaly threshold to reduce alert volume. Prioritize alerts by risk score and potential impact. Implement feedback loop so model learns what team considers meaningful. Provide self-service investigation tools so team can quickly validate/dismiss. Target <20 alerts daily for team to maintain attention.

Challenge: "Model flags legitimate business changes as anomalies."

Solution: Retrain model quarterly to incorporate business evolution into "normal." Implement concept drift detection—automatically identify when patterns shift significantly, trigger retraining. Allow manual model updates for known changes (acquisition, new product launch). Balance stability (don't overreact to noise) with adaptability.

Challenge: "Can't explain why ML flagged transaction—auditors/users don't trust."

Solution: Use explainable AI techniques (SHAP values, LIME) showing which factors drove anomaly score. Provide comparison to normal—"Amount: $850 (typical: $200-$400), Vendor: New (typical: established), Timing: Weekend (typical: weekday)." Maintain audit trail of all flagged items and investigation outcomes.

Challenge: "Sophisticated fraud evolves to avoid detection."

Solution: Regularly retrain on recent data including confirmed fraud cases. Use ensemble models combining multiple detection approaches—harder for fraudsters to evade all simultaneously. Monitor for "too perfect" patterns (fraudsters trying to stay just below detection threshold often create unnaturally consistent patterns).

Challenge: "Seasonal business makes normal patterns difficult to define."

Solution: Use time-aware anomaly detection considering day-of-week, month, quarter patterns. "Compare to same month last year" not "compare to last month." Segment by business cycle (holiday season vs. regular season). Model seasonality explicitly in time series approaches.

Self-Healing Systems: Beyond flagging anomalies, AI will automatically resolve simple cases. "Duplicate invoice detected—flagging as exception, notifying vendor, updating system to prevent payment. No human intervention required." Escalate only complex cases requiring judgment.

Predictive Anomaly Detection: Rather than detecting anomalies after they occur, predict likely anomalies before they happen. "This expense pattern indicates employee likely to submit fraudulent claim in next 30 days—recommend preventive audit." Shift from reactive to preventive.

Cross-Domain Anomaly Networks: Detect anomalies by connecting patterns across finance, operations, HR, sales. "Vendor invoice spike correlates with employee promotion, both correlate with sales opportunity, suggesting possible kickback scheme." Insights invisible when analyzing domains in isolation.

Continuous Model Optimization: AI optimizes own detection algorithms automatically—testing new techniques, adjusting thresholds, retraining schedules—based on performance metrics. Human oversight on strategy, AI handles tactical optimization.

Collaborative Intelligence: Anomaly detection systems across organizations share insights (preserving privacy). "This fraud pattern detected at 15 companies in your industry this month—adjusting your detection rules proactively." Network effect makes everyone's detection better.