This research explores the potential of machine learning to address these challenges by automating the categorisation and processing of these fees, with the aim of reducing manual input, minimising errors and improving overall efficiency.

The research is divided into preparation, design, evaluation and integration. In the preparation phase, fee structures and data formats from major financial markets, including the United States, United Kingdom, Hong Kong and Canada, were analysed. For testing purposes, synthetic data sets were created to replicate real-world scenarios, including errors, inconsistencies and non-standardised formats, providing a robust basis for testing. The design phase focused on developing ML solutions tailored to the unique challenges of post-trade fee processing. Various algorithms, including Random Forests and Conditional Random Fields, were compared and evaluated for their ability to accurately identify and categorise fee types, extract key data elements and handle inconsistencies between datasets. The evaluation phase assessed the performance of the ML models against manually processed datasets, with key metrics including accuracy, error rates, processing time and scalability.

The results provide insights into the strengths and limitations of ML in this domain, highlighting opportunities for optimisation and identifying barriers to achieving zero-error results. Finally, the integration phase addresses the practical challenges of deploying ML solutions in complex, matrixed business environments. This includes considerations for aligning organisational structures, ensuring compliance with regulatory standards, and implementing effective quality control measures to monitor and improve system performance over time.