Fraud

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Tax & Revenue Administration

AI can help tax administrations protect revenue earlier — improving the decisions that determine who should be registered, which returns warrant intervention, whether a refund should be released, and where scarce officer capacity should be focused.

30 Sep 2026 tax revenue integrity

Business Domain

Network-Level Fraud Scoring

AI fraud scoring built for the throughput and latency constraints of a payment network — detecting account takeover, CNP fraud, and coordinated fraud rings across all connected institutions.

10 Nov 2024 fraud payments

Business Domain

Payments

Payments are built on a continuous sequence of decisions. AI creates an opportunity to bring additional context to each one — increasing successful transactions, reducing losses, and lowering the operational cost of exceptions.

1 Nov 2024 payments fraud

Business Domain

Fraud & Financial Crime

Fraud and financial crime require banks to detect suspicious activity, assess risk, investigate cases, and intervene when action can still change the outcome. AI creates an opportunity to identify emerging threats earlier, improve real-time fraud decisions, and focus investigative capacity where it matters most.

1 Nov 2024 fraud financial crime

Business Domain

Fraud, Waste & Abuse

Fraud, waste and abuse span claims, providers, members, pharmacy and payment activity. AI creates an opportunity to identify suspicious patterns earlier, prevent inappropriate payments, and direct investigative capacity toward cases with the greatest potential impact.

1 Nov 2024 fraud waste

ai strategy · 8 min read

The Fraud Solution Opportunity Waiting on IBM Z

IBM has spent years building the ability to run AI inside one of the world's most important transaction-processing environments. Telum and Telum II put model inference close to the applications and data on which consequential financial decisions depend. That is a substantial engineering achievement, but it is not yet the full commercial opportunity. The runtime makes it possible to execute models in the transaction path. The greater prize is the fraud solution built around it, the models, behavioral features, decision logic and workflows that let institutions prevent more fraud rather than merely run more inference.

5 Sep 2026 AI strategy

Article · 8 min read

Why I Built a Highly Targeted Fraud Vector Detection Capability on IBM Z

Most institutions do not have a fraud platform problem. They have specific fraud vectors that continue to generate losses despite years of investment in fraud technology. This article explains why I chose to focus on a single fraud vector rather than build another broad fraud platform.

9 Jun 2026 Fraud Fraud Vector

insights · 7 min read

APAC Banks Pay $4.59 Per Dollar Lost Without AI Detection

APAC financial institutions spend $4.59 for every $1 lost to fraud. Regional AML penalties surged 266% in H1 2024. The 2023 Singapore money laundering case has shifted every major AP regulator from consultation to enforcement. Three pressures are converging on APAC banks simultaneously — and all three are backed by primary evidence.

12 May 2026 banking AI

insights · 9 min read

SIC5 Created the Fraud Problem Swiss AI Must Now Solve

Switzerland launched SIC5 instant payments in August 2024. Cyber fraud cases rose 40% the same year. CEO fraud nearly doubled. FINMA's April 2026 survey found 42% of Swiss banks have no dedicated digital fraud policy. The infrastructure is live. The governance is not.

12 May 2026 banking AI

insights · 8 min read

Why Pix Made AI Fraud Prevention an Infrastructure Requirement

Brazil built the world's most advanced instant payments infrastructure. In doing so, it created the world's most advanced bank fraud problem. Pix processed 63.4 billion transactions in 2024 on an irrevocable settlement rail — a fraud surface that no rule-based system can adequately cover, and a financial inclusion opportunity that no traditional credit model can serve.

12 May 2026 banking AI

Insight · 7 min read

A Fraud Use Case That Fails to Identify a Single Fraud Vector Is Not a Use Case

Fraud detection is not a use case. It is an umbrella term. The vector is closer, but still not sufficient. The use case emerges only when a vector is mapped to a specific business decision, and that is where most fraud AI initiatives stop short.

28 Jul 2025 Fraud AI