From Chaos to Security: How AI Invoice Processing Agent could have saved Rs 2.16 Crore for Dr. Reddy's

November 3, 2025: Cybercriminals gained unauthorized access to email communications between Group Pharmaceuticals (Bengaluru) and Dr. Reddy's Laboratories (Hyderabad). The hackers intercepted ongoing correspondence and created a spoofed email address that closely mimicked Group Pharmaceuticals' legitimate domain - a growing threat in cybersecurity in finance.

The hackers employed a multi-layered social engineering and technical attack:

1. Email Interception: They gained access to the email chain between the two companies, giving them context and timing.

2. Domain Spoofing: They created an email address visually similar to the legitimate one exploiting human tendency to miss subtle differences.

3. Payment Diversion: They provided new bank details, betting that the finance team wouldn't verify them against historical records.

4. Urgency Exploitation: The tone implied routine payment processing, creating psychological pressure to act quickly without questioning.

An AI Invoice Processing Agent is a software solution that leverages advanced artificial intelligence including Vision-language models (VLMs) and large language models (LLMs)/ Multi modal LLMs to automate the entire lifecycle of invoice management. From reading invoice documents and extracting data to approving payments and updating ERP records, these agents work autonomously, slashing manual effort , drastically reducing error & preventing fraud.

Unlike rigid rule-based systems, AI agents reason through exceptions, learn from historical patterns, and adapt to new threats, exactly what the Dr. Reddy's case required.

· What It Does: Invoices arrive via email, portals, scans, or as image files. The AI agent instantly detects and ingests documents in any format.

· Impact: 90% reduction in manual data entry errors; 24-hour automated processing.

· What It Does: Traditional document processing systems depended on OCR engines and isolated NLP components, which often struggled with complex layouts or low-quality scans. Modern AI agents now use Unified LLM-driven Document Intelligence, delivering far more accurate and flexible results.

· Vision-language models read documents both visually and semantically, understanding structure and meaning together.

· They extract key fields such as invoice numbers, dates, vendor information, line items, and totals with strong contextual awareness.

· The agent transforms raw document data into a clean, structured digital record (usually JSON), ready for downstream processing.

· Impact: 80% reduction in processing time across operational workflows. It recognizes variations and adapts quickly to new invoice formats with impressive accuracy.

· What It Does: Cross-references extracted info against the vendor master database

· This includes:

• Checking vendor and bank account authenticity
• Detecting duplicates and anomalies
• Ensuring the invoice matches purchase orders (PO) and goods receipts (multi-way matching)

· Impact: 85-95% accuracy in vendor matching; immediate anomaly alerts.

· AI agent monitors for suspicious changes such as new email, unexpected bank account alterations and deviations from past transaction patterns. If anything is unusual, the agent raises alerts, blocks payments, and prompts human intervention.

· Assigns a risk score based on vendor history, transaction size, frequency, account changes, etc. Triggers automated holds when risk exceeds thresholds.

Impact:

• Phishing and spoofing detection accuracy: 98%+
• 80% faster fraud detection & real-time alerts.

· What It Does: Automatically matches invoices against POs and goods receipts. Identifies duplicates and flags discrepancies.

· Impact: 99% accuracy in duplicate detection; 75% fewer reconciliation errors.

· What It Does: Routes invoices based on risk profile. Sends alerts to approvers with context. Maintains audit trails.

· Impact: Payment processing time reduced to hours; 100% compliance audit trail.

· What It Does: Uses feedback to improve detection. Updates risk models based on emerging fraud patterns.

· Impact: System accuracy improves 2-3% monthly.

AI would have identified that the requested bank account was new and untrusted compared to the master vendor database. Any request to change bank details would be blocked pending multi-factor human and portal-based verification.

The LLM reads not just field values, but reasons: “This payment request comes from a vendor communication pattern that deviates from historical records.” It would instantly flag such pattern breaks for review.

The agent verifies sender domains, authenticates messages via DMARC/SPF/DKIM, and alerts on any email spoofing or unusual sender behaviors.

Requests that fail validation are automatically routed for escalation - requiring human checks and explicit confirmations before payments proceed.

Every action, anomaly, and override is digitally logged, ensuring transparency for future audits and compliance.