Anomaly AI for Consumer Goods

Anomaly AI for CPG unifies ERP, accounts payable, purchase orders, supplier, inventory, and GL data into a single analytical model. It applies explainable machine learning and CPG specific business rules to identify high risk transactions, vendors, and behaviors before losses occur.

Datavail enhances the solution by accelerating data ingestion, operationalizing models, and supporting investigation workflows for finance, audit, and supply chain teams.

Key Use Cases

Accounts Payable & Invoice Anomalies

Duplicate or near duplicate invoices

Pricing and quantity anomalies vs. PO

Invoices bypassing 3 way matching

Period end and unusual timing patterns

Vendor & Co Manufacturer Risk

Payments to inactive or blocked vendors

Sudden bank or payment detail changes

Abnormal invoice frequency or value spikes

Shared vendor attributes (bank, address, tax ID)

Inventory & Supply Chain Anomalies

Invoices not aligned with received goods

Abnormal inventory adjustments or write offs

GL postings inconsistent with historical patterns

Internal Controls & Audit

Segregation of duties violations

Manual overrides and exception abuse

Anomalous approval behavior

How It Works:

1. Ingest ERP, AP, PO, supplier, inventory, and GL data is ingested into an AWS data lake via files or CDC

2. Model Data is transformed into invoice and transaction centric datasets with behavioral and relational features.

3. Detect Unsupervised ML models and deterministic business rules generate composite, explainable risk scores.

4. Investigate Users review prioritized alerts with full transparency into anomalies and rule triggers.

**Deliverables **

• No labeled fraud data required

• Explainable, audit ready results

• Unified view across finance and supply chain

• Early detection of risk before losses occurs

• AWS native, scalable, and secure

• Faster time to value leveraging Datavail delivery expertise