The choice of AI in financial crime compliance is no longer just about which model performs best. According to Napier AI, financial institutions are now weighing where a model was trained, who built it, what data it learned from and where it runs. Those factors don’t necessarily work against performance, but they are separate measures, and they are increasingly shaping AI strategy.
The theme was debated at a recent panel at the Napier AI / AML Assembly, which brought together practitioners from technology, financial services and payments. The session was held under the Chatham House Rule, so views were shared without attribution.
Panellists noted that running software in local or dedicated environments is nothing new. The harder part has been accessing certain AI models in a fully disconnected way, because some providers still don’t allow it. That restricts institutions with strict data residency or control requirements. The panel expected that as sovereign cloud offerings for AI mature, data control will become a baseline expectation rather than a premium extra.
The case for control goes beyond compliance. Napier AI pointed to a multinational corporate seeking to expand its credit facility, which typically approaches several banks with broadly the same information. Each bank could feed an identical prompt into the same general-purpose model. What separates them is the institutional and tacit knowledge they apply on the way to a decision. Training AI on that knowledge is a real differentiator. The same is true in financial crime compliance, where typologies, risk appetite and investigator judgement are hard-won assets.
On regulation, the panel argued that rules exist to protect and set boundaries, not to make AI better. A lighter regulatory touch in some markets may speed up deployment, but it carries risk. Firms could end up with AI running compliance checks across the business, only to find they have to reverse engineer those systems to understand past decisions. That challenge grows as autonomous agents adapt their behaviour with less human direction. Napier AI said it treats traceability and auditability as a core discipline, so that every alert, disposition and escalation can be explained to a regulator.
Then there is the data sharing paradox. Sovereignty pushes institutions to keep data close, yet research suggests the industry would catch more financial crime by sharing more. Appetite is strong in payments and growing in banking, with consortium analytics proving valuable against authorised push payment (APP) fraud, where signals about receiving accounts help identify mule accounts faster. Card networks are also keen to coordinate intelligence.
Synthetic data offers another path. Napier AI took part in the Anti-Money Laundering and Synthetic Data Project alongside the Financial Conduct Authority (FCA), the Alan Turing Institute and Plenitude Consulting. The project built a fully synthetic dataset from anonymised banking data, which the FCA is making available through its Digital Sandbox. Napier AI has since applied frequency-based AI algorithms to large-scale synthetic datasets in the FCA Supercharged Sandbox.
According to Napier AI, the tension eases once raw data is distinguished from the intelligence derived from it. Sovereignty governs where data lives and who controls models, while collaboration can happen through signals, patterns and typologies. The firm’s cross-customer learning applies this principle, improving defaults and typology coverage without any customer transaction or alert data leaving its own tenant.
The panel suggested institutions ask five questions of any AML vendor: where the model was trained and on what data, where it runs and whether it can operate disconnected, whether every output can be explained to a regulator, what leaves the environment, and how the approach fits data-sharing rules in each jurisdiction.
Napier AI concluded that the institutions best placed to stay ahead of financial crime will be those that combine control over their data with a willingness to share intelligence responsibly.
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