ANZ Bank (Australia and New Zealand Banking Group) is a multinational financial services institution and one of Australia’s largest banks.They needed help to improve the manual, error-prone process to reconcile Foreign Exchange (FX) trades each day.Reason created a secure automated workflow, applying Artificial Intelligence (AI) to evaluate the emailed trade requests and Machine Learning (ML) to review and match requests to executions. In addition, chat bots flagged any unmatched trades for the relevant person to review and resolve.
Reason validated the workflow concept for the robotic process automation with an interactive prototype in just 4 weeks. During this time, we designed the approach, target architecture and project delivery timeframe.
The early focus created the environments, internal tooling and processes to allow the team to rapidly iterate over future releases.The initial release delivered basic matching functionality using Artificial Intelligence (AI), and a newly built Web App displayed the status of each trade.
Applying regression analysis to new data sets helped increase the automatic trade match rate to 70%, reducing the need for manual reconciliation.The reconciliation process for non-matched trades was aided by new chatbot and WebApp functionality, enabling inter-departmental communication and feedback.
To truly deliver on the promise of automation, new ML models (prodi.gy) used entity recognition and prediction based recommendations (regression analysis), to automatically and reliably ‘decide’ whether or not to auto-match a trade, improving match rates above 90%.Over time, the match rate increased as the models continued to ‘learn’ how to match trades using the available data.
Our architectural approach balanced a need to adhere to internal policies (using an internal hybrid cloud platform) whilst allowing stakeholders the flexibility to experiment and adapt.
Docker containerisation solutions split operational from developmental concerns, while an event-driven micro-services architecture decoupled new feature developments (e.g: adding new data sources and model training) for smoother deployments.
automated trade match rate through AI & ML
to deliver prototype and prove automation process
time saved on manual reconciliations