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Processes and decision automation for financial markets trade surveillance: Challenges and recommendations — next-generation solutions
Journal of AI, Robotics & Workplace Automation,
1
(2),
119-130
(2021)
Abstract
This paper presents the challenges institutions encounter when implementing or deploying market surveillance solutions. A guidance framework is provided to design and deploy solutions that can overcome the current challenges. The support technologies that should be used include artificial intelligence (AI) techniques, advanced data science and statistics. The next-generation surveillance solutions should make use of these technologies that provide a robust framework for the detection of any market abuse, both known and new emerging patterns, while reducing the operational cost.
Keywords: trade surveillance; market abuse; financial instruments; automation; artificial intelligence (AI); explainable; proactive
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Author's Biography
Cristina Soviany has an MSc degree in Computer Science from Polytechnics University of Bucharest, Romania, and a PhD in Applied Sciences from Delft University of Technology, the Netherlands. She is a technologist with a strong academic and research and development (R&D) background and more than 15 years of entrepreneurial experience. She is currently the co-founder and chief executive officer (CEO) of Features Analytics, a young technology company based in Belgium. She was awarded the prize for leading the most innovative technology company in Europe in December 2011. Prior to starting Features Analytics, she worked as a senior scientist for Philips Applied Technologies in the Netherlands and later joined Advanced Medical Diagnostics, where she has been leading the development of an innovative technology for cancer tissue characterisation using three-dimensional ultrasound data. She is a frequent speaker at international finance conferences, educating the audience on the effective use of artificial intelligence (AI) technology and solutions. Features Analytics specialises in AI/machine learning fraud detection solutions for large banks and financial institutions, acquirers, payment processors and card issuers. The Features Analytics team has developed eyeDES, a unique AI/streamlined machine learning technology and platform for real-time payment fraud detection and surveillance solutions for financial market abuse detection, applied to foreign exchange, capital markets and anti-money laundering (AML). The company works with Tier 1 financial organisations and global investment banks.
Sorin Soviany is a senior researcher at the National Institute for Research and Development in Informatics (ICI-Bucharest, Romania). He received his MSc degree in computer science from Politechnica University of Bucharest (Romania), Faculty of Control Engineering and Computer Science in 2000. Sorin obtained his PhD in electronic engineering and telecommunications from University of Pitesti, Faculty of Electronics, Computers and Communications with the thesis ‘Decision Optimization for Biometric Identification Systems’ (2013). Sorin has published and presented over 40 scientific works in international conferences and scientific journals. His main research topics include biometric systems design and evaluation, intrusion detection systems design with machine learning-based techniques, security applications design for smart environments (smart home, smart city), advanced solutions for data analysis and modelling in earth sciences. Currently he works at ICI-Bucharest, Romania where he is involved in research and development projects related to smart applications based on artificial intelligence, biometric security, data and networks security with specific use cases in smart environments.
