Case Study: FundApps improves market data change detection with Melon

FundApps - Customer Case Study

FundApps, a UK fintech company that helps automate regulatory compliance and monitors over USD 10 trillion in AUM, needed a better way to process large volumes of market data from multiple providers. Working with Melon, the company sought a solution to detect changes in company market data and identify the correct value when different issuers reported conflicting figures.

Melon built a big data and machine learning solution using tools such as TensorFlow, scikit-learn, SciPy, and Pandas. After preprocessing and analyzing the time-series data, Melon applied unsupervised learning with DBSCAN and a Pareto-based approach to identify clusters and predict the correct daily market data values, significantly improving detection of market data changes.

FundApps