Automating Trade Reconciliation Using Artificial Intelligence

FX Trade Processing Transformed with Machine Learning

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.

90%

automated trade match rate through AI & ML

75%

time saved on manual reconciliations

4 wks

to deliver prototype and prove automation process
Symphony’s Customer Innovation Award 2018

Phase Zero

Establish Workflow

Phase One

Automating workflows

Phase Two

Interpreting data

Phase Three

Driving Match Rates

0%

Automated Reconciliation

90%

Automated Reconciliation


10%

Manual Reconciliation

A Phased Approach

Phase Zero

Establishing the workflow

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.

Phase One

Automating Workflows with Chat Bots:

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.

  • 50% auto-match rate on FX trades, with links to supporting documentation
  • TradeButler web application and chatbot fully integrated into daily workflows to flag the relevant people and resolve unmatched trades

Phase Two

Ingesting and Interpreting multiple data sources

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.

  • 70% auto-matching of FX trades
  • 75% faster resolution of non-matched trades using Web App and Chatbot to flag, review and resolve

Phase Three

Driving Match Rates with Machine Learning

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.

  • 90% of FX trades automatically matched
  • Manual processing time reduced by 75%

Delivering
 results

90%

automated trade match rate through AI & ML

75%

time saved on manual reconciliations

4 wks

to deliver prototype and prove automation process
Symphony’s Customer Innovation Award 2018