The CAMELOT project expects to revolutionize the detection of financial fraud through Machine Learning techniques. The project aims to develop an innovative machine learning platform, which will tackle three key issues that hinder the efficiency and accuracy of modern AI applications such as machine learning models, cloud resources, anonymized data, privacy issues, and integrating information from different, independent, and heterogeneous data platforms.
Keywords: Fraud detection · Machine Learning · Anonymization
Feedzai uses AI to detect and prevent financial fraud such as card fraud, money laundering, or account opening fraud for all types of financial institutions and large merchants. To support its clients, Feedzai manages about 2000 machines and spends about 500,000 eur/month in cloud computing resources.
The goals of the CAMELOT project are to improve the efficiency, accuracy, or quality of the machine learning platform and associated stack using efficient cloud resources.
Feedzai expects to be able to sustain its aggressive company growth goals. Among the desired potential impacts of the project are to reduce by 50% the cost for tuning the platform’s configuration; 50% reduction in of the operational costs at steady state and 50% faster deployment times for new models when reusing models.
The project will tackle three key issues that hinder the efficiency and accuracy of modern AI applications: