@ Metamorworks/stock.adobe.com
Topic Fintechs Big data, artificial intelligence and machine learning
Many people would agree that big data (BD) and automated data processing using artificial intelligence (AI) and/or machine learning (ML) are the key to digital transformation in almost every industry. In the financial sector, too, there are application scenarios that would be barely conceivable without BDAI/ML.
In simple terms, BaFin defines AI as the combination of big data, computing resources and machine learning. However, this definition does not make a universally applicable distinction between BDAI/ML methods and other methods. In fact, when it comes to a specific method to be examined, supervisory practice, inspection techniques and inspection intensity need to take into account which BDAI/ML characteristics are present and the extent to which these characteristics are pronounced – there are thus no clear-cut lines between the methods and there might be some overlap.
The characteristics can be grouped into three dimensions of the BDAI/ML scenario:
(2) The use of the output relates to the significance of the process within a supervised entity’s risk management.
(3) The third dimension relates to the general distinction between in-house development and outsourcing and the underlying IT infrastructure.
(See BaFin/Bundesbank, Consultation 11/2021 – Consultation paper: Machine learning in risk models – Characteristics and supervisory priorities https://www.bundesbank.de/en/homepage/machine-learning-in-risk-models-characteristics-and-supervisory-priorities-793670)
In the banking and insurance sectors, BDAI/ML is typically used for anti-money laundering and fraud detection and for back office processes (such as black box processing in the insurance sector). BDAI/ML is also used for scoring, pricing (incl. telematics rates) and for sales support. In the area of risk management, BDAI/ML is increasingly being used to prepare data and validate risk models. In the securities industry, which is already characterised by a high level of automation, BDAI/ML is primarily used to enhance existing processes in trading, advice, risk management and compliance.
In 2021, BaFin defined general principles for the use of algorithms, with a focus on algorithms in the area of BDAI/ML (https://www.bafin.de/dok/16178870). In doing so, BaFin set out key general principles (e.g. clear management responsibility) and specific principles for the development phase and application phase of BDAI/ML applications. For example, data strategy, data governance and the implementation of appropriate validation processes must be taken into account in the development phase, while in-depth approval and feedback processes must be carried out during the application phase.