Structuring unstructured address data for ISO 20022 compliance: Design, assurance and governance of an open-source AI utility for cross border payments
Abstract
The migration to ISO 20022 requires financial institutions to move from fully unstructured postal address fields towards structured or hybrid address formats, including explicit town and country elements. This creates a practical challenge for banks and corporates that hold large volumes of legacy free-text address data across jurisdictions, languages and local postal conventions. This paper examines that challenge through a case study of a narrowly scoped artificial intelligence (AI) utility designed to infer town and country from unstructured payment address data. The approach combines a compact machine learning model with reference-data matching, postal-code logic, synthetic training data and confidence-based outputs, enabling local deployment without reliance on external large language models or remote inference services. Validation across internal, external and independently reviewed datasets showed strong performance on representative payment-style data, while also identifying predictable failure modes in ambiguous, incomplete, non-standard and transliterated addresses. The case demonstrates that targeted AI can support ISO 20022 data remediation and cross-border payment efficiency where its operating boundary is explicit, its limitations are documented, and its outputs are subject to appropriate governance, validation and human review. This article is also included in the Business & Management Collection which can be accessed at http://hstalks.com/business.
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Author's Biography
Tom Jacobs heads the AI and Analytics Strategy and Exploration team at SWIFT, and has worked in topics ranging from business intelligence consulting to sales and product development. Having previously designed analytical utilities to support central banks in macroprudential regulation and monetary policy, he is now driving the next generation of data science and artificial intelligence use cases for the financial ecosystem.
Gabriel Ortega is a computer scientist specialising in data science and AI solutions at SWIFT, where he serves as the technical lead for the AI Hybrid Address Structuring model. Leveraging a strong engineering background, he focuses on delivering pragmatic, data-driven solutions, with a particular emphasis on software architecture, algorithm design and the development of workflows that enable seamless integration into existing systems.
Brigitte De Wilde has held various leadership roles at SWIFT, including Head of Market Development for Fraud and AML and Head of AML and Sanctions initiatives. With extensive experience in market development and software engineering, she has been instrumental in launching the Sanctions Screening service, which helps financial institutions comply with sanctions regulations. Prior to SWIFT, she worked as a CAD-CAM Software Development Engineer at various companies, starting her career in 1986.
Martin Koder is the Responsible AI Lead at SWIFT, where he oversees the definition and implementation of standards for responsible artificial intelligence and AI governance. Prior to joining SWIFT, Martin was Senior Manager for Regulatory Strategy, Capital Markets at the London Stock Exchange Group; he has also held other policy and technology consulting positions.
