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Revolutionising FX Trade Reconciliation - a journey into AI and machine learning


ANZ Bank provides banking and financial products and services to over 8.5 million retail and business customers, and operate across close to 30 markets.

In the fast-paced world of finance, ANZ Bank grappled with the intricacies of manual Foreign Exchange (FX) trade reconciliation.

Faced with a cumbersome and error-prone process, ANZ sought a transformative solution to streamline operations.



Armed with a cutting-edge approach integrating Artificial Intelligence (AI) and Machine Learning (ML), DCX agency Reason set about revolutionising the FX trade reconciliation process by automating workflows and making use of advanced technologies.

Phase Zero - Establishing the Workflow

In a swift four-week span, we validated the robotic process automation workflow through an interactive prototype.

This crucial phase laid the groundwork, defining the approach, target architecture, and project delivery timeline.

Phase One - Automating Workflows with Chatbots

The initial release marked a milestone with a 50% auto-match rate on FX trades.

The integration of AI-driven chatbots and a dedicated TradeButler web application seamlessly became part of daily workflows, flagging unmatched trades for swift resolution.

  • 50% auto-match rate achieved

  • TradeButler web application and chatbots seamlessly integrated into daily workflows

  • Efficient resolution of unmatched trades with links to supporting documentation

Phase Two - Ingesting and Interpreting Multiple Data Sources

Advancing into the second phase, we implemented regression analysis on new data-sets, pushing the automatic trade match rate to an impressive 70%.

The enhanced functionality of chatbots and the TradeButler Web App facilitated inter-departmental communication, significantly accelerating the resolution of non-matched trades.

  • 70% auto-matching of FX trades attained

  • 75% faster resolution of non-matched trades using advanced Web App and Chatbot features

Phase Three - Driving Match Rates with Machine Learning

The final phase saw the implementation of new machine learning models, specifically, employing entity recognition and predictive recommendations.

These models autonomously determined whether to auto-match a trade, steadily pushing match rates beyond 90%. Manual processing time witnessed a substantial 75% reduction.


rapid timeline


automated trade match rate thanks to AI & ML


reduction in time spent on manual reconciliations


Our architectural prowess delivered secure and effective solutions for ANZ Bank, blending adherence to internal policies with operational flexibility.

Docker containerisation streamlined concerns, while an event-driven micro-services architecture facilitated smooth deployments for new features.

  • Symphony’s Customer Innovation Award recognised the groundbreaking achievement
  • A stellar 90% automated trade match rate through the synergy of AI and machine learning
  • Rapid 4-week timeline to deliver a prototype and validate the automation process
  • A remarkable 75% reduction in time spent on manual reconciliations

In essence, ANZ Bank's journey into the realm of AI and ML not only overcame manual trade reconciliation challenges, but also set a new standard for efficiency and innovation in the financial sector.

Do Financial Services. Better.