Executive Summary: How to Catch Fraud Before It Touches Your Books
- Fraud is shifting upstream-into real-time payments and procurement workflows, not just transactions.
- AI and data platforms are the new front line, but only when built on a sound architecture.
- Knowing what to outsource vs. own in-house is as important as the technology itself.
- This guide gives CFOs, CIOs, COOs, and CHROs a practical, evidence-backed approach to get ahead of fraud.
Fraud can be exposed at any point, not just during your close cycle. According to Deloitte’s 2025 analysis of authorized push payment fraud in the U.S., APP fraud losses could climb from $8.3 billion in 2024 to nearly $15 billion by 2028. The window between a fraudulent transaction and a posted ledger entry is shrinking – and so is your margin for error.
For enterprise leaders, this isn’t just a technology problem. It’s a business continuity and governance problem. Fraud is becoming faster, more AI-enabled, and harder to detect with controls designed for yesterday’s workflows.
What follows will show you how to approach real-time fraud detection through a combined data and cyber lens – covering strategy, architecture, what to outsource, and how far to take AI-driven automation – anchored in analyst data and practical operating experience.
How CFOs and CIOs Should Use AI and Data to Detect Fraud Before It Hits the Ledger
The opportunity is significant. Deloitte’s 2025 ‘Using AI to fight insurance fraud’ report estimates that AI-driven detection across the claims lifecycle could save US insurers $80-$160 billion by 2032 – while the estimate is specific to P&C insurance, the principle holds broadly: AI applied across the transaction lifecycle dramatically reduces fraud losses in banking, finance operations, and enterprise procurement alike.
The gap, however, is execution. McKinsey’s 2025 ‘AI in the workplace’ (Superagency) report finds that nearly all companies invest in AI, yet only about 1% consider themselves at full maturity. Most are operating pilot programs rather than fully developed, production-grade detection systems.
A practical three-step strategy for executive teams:
- Build a unified data platform that connects payments, invoices, procurement, and GL/sub-ledger data into one coherent view-eliminating the blind spots fraudsters exploit.
- Layer real-time AI scoring across transactions, counterparty behavior, and access patterns to surface anomalies before they settle.
- Close the loop operationally-give finance, risk, and operations teams clear workflows to act on signals, not just reports.
Scalence’s Data Intelligence capabilities are designed around exactly this kind of unified, analytics-ready foundation.
How to Stop Suspicious Transactions Before They Reach the General Ledger
Consider a mid-market manufacturer whose AP team processes 3,000 invoices a month. A vendor account is quietly compromised, and payment details are changed. With transaction-only controls, the fraud clears. With a real-time streaming architecture monitoring AP, procurement approvals, and access logs simultaneously, the anomaly surfaces before the payment posts.
That architecture typically looks like:
- Streaming ingestion from payments, AP, procurement, ERP, and HR systems
- A real-time scoring engine combining AI/ML models with rules-based controls
- Policy-driven actions-hold, step-up verification, or alert-triggered before ledger entry
McKinsey’s research on how agentic AI is reshaping fraud and AML controls shows that this isn’t just about smarter alerts – it’s about orchestrating end-to-end workflows that respond faster, with human oversight focused where it matters most.
Cloud infrastructure is the enabler: secure landing zones, identity governance, and observability form the control plane. Scalence’s Cybersecurity Services and Data Governance and Compliance offerings are built to make this architecture operational, auditable, and SOX-aligned from the start.
Why Existing Tools Miss Invoice, Procurement, and Insider Fraud – and What Executives Can Do
Most fraud-detection tools are designed around card transactions and payment rails. They weren’t built to catch PO manipulation, approval collusion, or insider access abuse – and that’s exactly where fraud is migrating.
The root causes are predictable: siloed procurement and finance systems, inconsistent data quality, and access governance that hasn’t kept pace with cloud and hybrid environments.
Executives who close this gap typically take three steps:
- Connect procurement, AP, HR, and finance data into a shared fraud-risk view using integrated data management practices
- Apply behavioral analytics to flag unusual vendor patterns, duplicate approvals, or access changes
- Align team incentives so fraud signals aren’t dismissed as noise or compliance box-checking
For a real-world example of what this looks like at scale, Scalence’s work on elevated identity security support for a major financial institution shows how identity governance and upstream controls catch risk that transactional tools miss.
Which Fraud and Security Functions Should You Outsource-and Which Must Stay In‑House?
The decision isn’t binary. A useful framework for executives:
Keep in-house: Risk appetite, fraud policy design, regulatory accountability, and the data strategy that underpins all detection.
Consider outsourcing: 24×7 monitoring, specialist threat research, fraud analytics operations, and platform management – provided providers demonstrate deep integration with your financial systems and can report at board level.
When evaluating providers, ask specifically:
- Do they have fraud-specific use cases, not just generic SIEM coverage?
- Can they integrate with your ERP, AP, and identity systems?
- Will they co-own the improvement roadmap, or simply respond to incidents?
How Far Should CFOs and CIOs Go in Automating Fraud Checks With AI Agents?
Automation should match your risk tolerance and governance maturity. A practical maturity curve:
- Phase 1 – Decision support: AI surfaces priority alerts and recommendations; humans act.
- Phase 2 – Partial automation: AI holds or routes flagged transactions; humans review exceptions.
- Phase 3 – Targeted full automation: High-volume, low-risk scenarios run autonomously with full audit trails.
McKinsey’s perspective on agentic AI and financial crime confirms this phased approach-effectiveness comes from combining dynamic risk models with strong human oversight on high-impact decisions. Scalence’s work operationalizing agentic AI for enterprises addresses exactly how to govern this transition without losing control.
Start Before Fraud Finds Your Gaps
Fraud moves faster than most control cycles. Waiting for a material incident to trigger architecture investment is the most expensive path available to any executive team.
If you’re ready to close the gap between your current tools and the real-time controls your environment needs, talk to our team or reach out at inquiries@scalence.com. We’ll help you map your data, cyber, and operational landscape-and define where to act first.
FAQ: Executive Questions on Stopping Fraud Before the Ledger
How can finance teams implement AI fraud detection without creating alert fatigue?
Start with a narrow, high-confidence use case-like duplicate invoice detection or payment-routing anomalies. Tune models to reduce noise before expanding scope. The goal is fewer, more actionable signals, not more alerts.
How do we reduce false positives in AI fraud detection without missing real fraud?
Combine rules-based controls with behavioral ML models and enrich signals with contextual data (user, vendor, access history). Regularly retrain models on confirmed fraud cases. Deloitte’s US insurance fraud study on AI-enabled detection points to multimodal data integration as a key differentiator.
Why do current fraud tools miss invoice and PO fraud upstream?
Most tools monitor payment rails, not procurement workflows. Invoice and PO fraud lives in approvals, vendor master data, and access changes-areas that require cross-system data integration and behavioral analytics to detect. Our blog on 5 proven ways to use data analytics for online fraud detection covers practical approaches.
What should a CFO or CIO ask an MSSP about fraud detection?
Ask whether they can integrate with your ERP and AP systems, whether detections are customized to your environment, and how they report risk at a board level-not just incident counts.
