EBA published a report that addresses the key considerations in the development, implementation, and adoption of big data and advanced analytics in the banking sector. The report also covers the recent trends in big data and advanced analytics. The report highlights that a data-driven approach is emerging across the banking sector, affecting business strategies, risks, technology, and operations of banks. The report focuses on the big data and advanced analytics techniques and tools, such as machine learning, which is a subset of artificial intelligence; these tools go beyond traditional business intelligence to gain deeper insights, make predictions, or generate recommendations using various types of data from various sources.
Through its ongoing interaction with the relevant stakeholders and by utilizing its innovation monitoring tools, EBA has observed a fast-growing interest in the use of big data and advanced analytics solutions, with two out of three EU credit institutions already having such solutions in production. All functions across institutions might benefit from the big data and advanced analytics applications, as they could improve existing services from an efficiency, productivity, and cost saving perspective or create new business opportunities. Most institutions are currently using simpler algorithms, leveraging on their core banking data. However, the current landscape can evolve at a rapid pace in the next few years.
The report identifies data management, technological infrastructure, analytics methodology, and organization and governance as the four key pillars for the development, implementation, and adoption of big data and advanced analytics. The report finds that the roll-out of big data and advanced analytics affects issues around trustworthiness and notes a number of fundamental trust elements that need to be properly and sufficiently addressed and these cut across the four key pillars. Efforts to ensure that artificial intelligence/machine solutions built by institutions respect these trust elements could have implications for all the key pillars. These trust elements are ethics, explainability and interpretability, fairness and avoidance of bias, traceability and auditability, data protection, data quality, security, and consumer protection. EBA is of the view that additional efforts are needed to ensure that big data and advanced analytics solutions respect and integrate these “elements of trust.” Toward meeting this objective, a risk-based approach could apply on certain “elements of trust,” depending on the impact of each big data and advanced analytics application.
Going forward, EBA will continue to observe (taking into account other work being done by ESAs and work being done in other international fora) and consider the pace of evolution of big data and advanced analytics in financial services (in line with its FinTech Roadmap). Where appropriate, EBA will accompany this work with opinions and/or proposals for guidelines to achieve a coordinated approach to the regulatory and supervisory treatment of artificial intelligence and big data and advanced analytics activities.
Keywords: Europe, EU, Banking, Big Data, Advanced Analytics, Fintech, Machine Learning, Artificial Intelligence, Fintech Roadmap, EBA
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