Regional analysis
Geo-specific analysis of AI adoption, regulatory developments, and fraud economics. Each article is grounded in primary regulatory sources, enforcement data, and market statistics specific to that jurisdiction.
Global
APP scam regulations are evolving around the world, but the real story is not reimbursement. As liability shifts toward financial institutions, investment priorities are changing. Fraud prevention is becoming an economic imperative, driving new investment in real-time decisioning, mule account detection, behavioural analytics, and Transactional AI.
APAC · 6 min read
AP regulators have moved from issuing guidance to imposing penalties. Banks that built AI governance as an afterthought are finding that the cost of remediation far exceeds what it would have cost to build it correctly from the start. The institutions that will compound advantage are those that treat governance as the mechanism by which model quality improves — not as the overhead that slows it down.
South Africa · 7 min read
Most South African banks are targeting AI at the wrong decisions. The ones that identify the right decisions are failing for three consistent reasons. The SARB/FSCA joint report found governance frameworks uneven across the sector — which means the failure patterns documented globally are also the failure patterns active in South Africa right now.
APAC · 7 min read
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.
Strategic approach
Most AI programmes underdeliver because they start with capability and work backward to value. These articles argue for a different starting point: identifying where AI investment produces the highest measurable return, building governance that functions at production speed, and sustaining model performance over time.
Article
Enterprise AI is often presented as requiring organisations to redesign business processes and operating models. But is large-scale transformation always the best place to start? This article explores an alternative in which organisations improve economically significant business decisions within existing processes before embarking on broader transformation.
Explore frameworkArticle
Agentic AI is transforming how organisations automate and orchestrate business processes. But does better orchestration automatically produce better business outcomes? This article explores the relationship between Process Intelligence and Decision Intelligence, arguing that autonomous workflows ultimately depend on the quality of the decisions they execute.
Explore frameworkArticle
Most discussions about AI focus on models and inference. Yet many of the highest-value Transactional AI solutions depend on behavioural context that exists outside the transaction itself. Fraud detection, financial crime prevention, and payment risk all require a mechanism for remembering what happened before. This article explores whether IBM Z Digital Integration Hub could provide that behavioural memory layer.
Explore frameworkCore principle
AI strategy isn't about technology adoption—it's about embedding intelligence into the decision-making fabric of your organization. Every transaction, every risk assessment, every customer interaction becomes an opportunity to learn, adapt, and improve. The question isn't whether to use AI, but how to architect your organization so intelligence flows to where decisions are made.
Learn about our approachIndustry focus
AI strategy applied in specific operational contexts. These articles examine how Transactional AI, governance, and decision economics work in practice across payments networks, financial services, insurance, and the transaction systems at the core of global commerce.
Government Border Control
Border agencies face a structural and permanent challenge — more decisions, fewer people, higher stakes. The data to make better decisions already exists in the transaction systems agencies trust. The constraint has always been architectural: analytics ran somewhere else, on a different timeline, against a copy of the data. IBM Z's Telum processor changes that. For the first time, AI inference runs inside the transaction itself — against the authoritative record, at the moment the decision is made, before the window closes.
Payments Networks
Acceptance gaps represent permanent lost volume. Spending that flows to cash, bank transfer, or a competing network because card acceptance is unavailable, unreliable, or uneconomic in that merchant segment does not automatically return when the acceptance gap is closed. Closing gaps earlier is more valuable than closing them later. Knowing which gaps to close first requires analysis that aggregate acceptance data cannot provide.
Payments Networks
A single large acquirer failure can expose a payment network to hundreds of millions in unrecovered settlement losses. The network sees every transaction flowing through every acquirer's portfolio — far more granular signal than any credit agency can provide. Early detection while mitigation options are still available is the investment that makes the most consequential network risk manageable.