Mars 20, 2025

Context

Migrating to ISO 20022 unlocks operational efficiency and deeper transaction insights, while paving the way for high-quality AI-powered services.

However, achieving a migration that is accurate, swift, and cost-effective — “right, fast, and cheap” — remains a significant challenge. Financial institutions must navigate legacy system integration, increased data complexity, evolving regulatory requirements, pressing technology adoption, all that by coping with a shortage of specialized expertise at the crossroads of payments operations, technology, and data [3].

AI-powered automation can accelerate ISO 20022 migration by dealing with those unstructured portions of the message that are out of reach for rule-based transformations provided by industry specifications, and ensure compliance-ready message formats. While concerns, justified or not, around vendor reliability, security, and integration challenges may exist, working with a proven provider can promote best of breed components, reduce uncertainty and offer a risk-controlled adoption enhancing operational efficiency while keeping costs in check.

This paper introduces a joint solution that blends Transformer from Trace Financial [1] with Swiftflow from Alpina Analytics [2], combining proven prebuilt mapping library transformations from legacy formats like MT into ISO 20022, with AI-driven data structuring, in the present case solving the postal address structuring headache.

The data and AI revolution starts in the factory of the bank !

Solution

The key to a seamless ISO 20022 migration is to acknowledge the potential of its benefits while minimizing disruption on existing systems and workflows. Instead of breaking into current platforms, this solution blends a proven message transformation solution with an AI-powered parser and enricher via a simple API, ensuring compatibility with legacy infrastructures while unlocking the benefits of structured data (see Figure 1).

By integrating an AI solution “behind” the core-banking transactional flow and IT systems, banks retain operational familiarity — legacy systems are protected as teams continue working within their existing environment, avoiding new integration patterns associated with new tools or methods, and related uncertainty. This means limited changes to established workflows, and reduced scope for additional service contracts. Instead of replacing current processes, AI unnoticeably enhances them by automating data structuring, reducing manual intervention, and improving overall data integrity. This ensures a smooth transition to ISO 20022 while keeping operations efficient and risk-mitigated.

Technically, Transformer is in charge of the data I/Os with the messaging flow, taking care of the translations from the legacy MT messages to/from the desired ISO 20022 outputs and in the desired MX flavour — CBPR+, CHAPS, MEPS+, etc (see Figure 1). In those messages, the unstructured postal addresses elements are extracted from their envelope and passed to Swiftflow via a simple API call for structuring and enrichment if required (see demo video).

The resulting fully compliant ISO 20022 message encapsulates a fully structured address (or hybrid only if required) that can be handled by your existing downstream system.

The entire packaged application can be deployed modularly and run indifferently on premises or on cloud. Also it does not require any external API call on a third-party system like a LLM (e.g. GPT) or geolocation service (e.g. Google Maps). The entire data flow and application stays runs on client’s environment and in their ownership.

Outcomes

Outcome 1: Operational Continuity

This solution ensures broad compatibility by supporting a wide range of legacy message formats, and guarantees that both incoming and outgoing messages meet various ISO 20022 specifications — maintaining business continuity with the different market infrastructures and actors — while minimizing manual data adjustments and operational overhead.

Outcome 2: Enhanced Address Structuring & Use Cases

Prepare for the evolving landscape of address requirements and anticipate compliance and regulatory risks. This solution delivers future-proof messaging, enabling financial institutions to transition smoothly to hybrid or fully structured ISO 20022 messages. Also it not only ensures compliance with evolving regulatory and industry standards, but also empowers institutions to unlock a first wave of improvements on use cases by themselves — such as enhanced sanctions screening — and by leveraging robust, structured address data.

Beyond Migration: From Transactions to Insights

ISO 20022 migration is just the first step. Beyond regulatory compliance, structured financial messages become a strategic asset, unlocking deeper insights from transaction data. By embedding AI-enhanced structuring directly within the payment ecosystem, institutions transform compliance into an opportunity for intelligence-driven innovation. The figure below shows how transactional flows can coexist with analytics flows for deriving data insights.

This solution provides a “hook into the core infra”, laying down gradually the foundation for downstream LLM-supported AI and BI applications and unlocking advanced capabilities such as geolocation-based analytics, anomaly detection, and business intelligence.

Transactional Flow — Reduces costs and risks associated with non-compliance, manual repairs, and message reconciliation errors by ensuring structured, high-quality data from the start.

Analytical Flow — Enables AI- and BI-driven insights by repurposing clean, enriched and standardized transaction data for advanced use cases such as:

• Sanctions screening — Enhancing real-time transaction monitoring for regulatory compliance.
• AML & fraud detection — Identifying suspicious patterns with AI-driven anomaly detection.
• Customer profiling & segmentation — Improving financial product personalization with richer behavioral insights.

By embedding AI-enhanced structuring within the payment ecosystem, financial institutions future-proof their data strategy, making ISO 20022 an enabler of intelligence rather than just a regulatory obligation.

Conclusion

Migrating to ISO 20022 goes beyond compliance — it presents an opportunity to boost operational efficiency, enhance data quality, and unlock strategic AI-driven insights. By integrating Transformer from Trace Financial for industry proven, prebuilt mapping transformations, and Swiftflow from Alpina Analytics for advanced AI-powered data structuring, financial institutions can significantly reduce migration risks, minimize costs, and ensure a smooth transition.

The joint solution allows banks to preserve existing workflows, ensuring operational continuity while proactively future-proofing their payment infrastructures. Clean, structured transaction data not only satisfies regulatory demands but also provides the foundation for powerful downstream AI and BI applications — supporting essential use cases such as sanctions screening, AML detection, and richer customer profiling.

With scalable AI integration, easy deployment, and minimal disruption, Transformer and Swiftflow seamlessly bridge legacy systems with tomorrow’s intelligent, data-driven financial ecosystem.

Let’s Talk

To explore how the combo Transformer and Swiftflow can accelerate your ISO 20022 journey, reach out to us:

– Paul Ruskin (Trace Financial) — paul.ruskin@tracefinancial.com
– Pierre Oberholzer (Alpina Analytics) — pierre.oberholzer@alpina-analytics.com

About the authors

​Paul Ruskin is the Director of Business Development at Trace Financial, bringing over 25 years of experience in the financial industry. Throughout his tenure, he has addressed the challenges organizations face when implementing ISO 20022 solutions, ensuring the development of future-proof, maintainable systems. Paul has contributed thought leadership on topics such as CBPR+, FAIM to ISO 20022 transition and SWIFT’s in-flow translation service. His extensive background encompasses project management and architectural roles at global financial institutions, underscoring his comprehensive understanding of the sector.

Pierre Oberholzer, is the founder of Alpina Analytics, specializing in making inter-banking data, such as MT and ISO 20022, ready for AI and BI. With more that 15 years experience in data science, AI, BI in various technology contexts including banking, he has developed solutions that enhance data engineering, information retrieval, transaction analysis, and compliance workflows. His expertise extends to ISO working groups, where he has infused new data modeling techniques to foster interoperability. Through Alpina Analytics, Pierre continues to drive innovation, helping financial institutions leverage AI-driven analytics for modern banking challenges.

References

[1] https://www.tracefinancial.com/transformer/
[2] http://alpina-analytics.com/products/swiftflow-2/
[3] https://www.bny.com/assets/corporate/documents/pdf/BAFT%20ISO%2020022%20Migration%20Lessons%20Learned-%202024.pdf
[4] https://www.swift.com/standards/iso-20022/iso-20022-financial-institutions-focus-payments-instructions
[5] https://medium.com/@pierre.oberholzer/iso-20022-front-to-back-to-front-5291c585214d