By Boyke Baboelal
Financial Data Management has focused mostly on day-to-day operations to deliver quality data to critical downstream applications. Little has been done to take advantage of AI and Machine Learning to further improve service levels and reduce TCO. However, Financial Data Management contains many use cases where new algorithms can improve operations and quality of data, ranging from new instrument set-ups using NLP, allowing to process product sheets, emails and pdf files, to data enrichment activities such as proxying incomplete data with information from related risk factors, found using recommender systems built using explainable techniques. Using Data Management Automation operational efficiency is improved and risks are reduced with the elimination of manual activities, data quality is improved using algorithms that process more data in a better way, and turn-around time is reduced, reducing risks of not meeting SLAs.
Exception handling and anomaly detection
One of the key controls in Financial Data Management is the detection of suspect data and validation thereof. Validation can be a very time-consuming activity depending on the universe of instruments, but also on the performance of rules, i.e. the number of false positives that are generated. The use of Machine Learning allows for advanced rules that compare more related information with each other and therefore reducing the number of false positives, for example using similar instruments to confirm price movements or verify descriptive values, but also to check frequency of updates, combinations of values, and/or completeness of data. Data Management Automation contains building blocks for organizations to build efficient data checks using Machine Learning.
Advanced controls and monitoring
In the data flow, from acquiring source data to distribution of instruments, there are many data risks that can impact the quality of delivered data. As stated before, Exception Handling is an important control, but there are many more. For example, controls should monitor if all instruments have rules applied to them and if these rules are consistently applied? Data Management Automation and Data Quality Intelligence provide frameworks and building blocks for organizations to methodologically identify data risks, create controls, and monitor risks through KRIs.
Data management processes require many manual activities and these activities should be performed according to documented procedures, for example to allow for transparency, lineage, and further analysis. Do resources follow procedures and show sufficient diligence? Are changes clearly documented? Do all instruments have the proper ownership and security settings? Anomaly detection, NLP, and automated process discovery can help organization check and improve compliance to internal guidelines.
In short, data management capabilities can be significantly expanded using Machine Learning. ML techniques can reduce workload and cost by automating manual processes, improve data quality with better exception handling, comply with internal and external guidelines with advanced analytics, and reduce risks with advanced controls.