Emerging technologies, and more specifically artificial intelligence (AI), have pervasive and global implications that are transforming entire economic sectors. Algorithms are becoming ubiquitous in our lives, as part of decision-making with far-reaching impacts in education, health, employment, but also access to credit and financial services, more generally.
Financial technology (FinTech) is an umbrella term to describe a new technology that aims to improve and automate the provision and use of financial services. Some recent studies indicate that 70% of FinTechs are already using AI today, and AI is predicted to dominate the market by 2025. FinTech providers, relying on big data analytics , are able to harness the power of AI systems. to create innovative products and business intelligence solutions that help businesses and individuals while reducing operating costs.
Some of the most prominent examples of uses of AI in finance include:
loan approval – possibly the most popular way FinTech companies benefit from AI is through money lending apps that analyze individuals’ financial habits and credit exposure to calculate their credit rating, making the underwriting process more efficient, often without the need for human intervention; the thesis being that such a loan approval process is also more precise and less biased, thanks to a better analysis of the client’s risk profile.
Insurance policy issuance – AI, in combination with the Internet of Things (IoT) and external data sets can interact with customers in real time and determine levels of risk, for example, by calculating levels of a person’s risk based on the assessment of their driving skills through a mobile application or assess the health risks based on information not included in their personal medical record.
forecasting – AI solutions are able to help users perform reliable calculations on their spending habits at a very low cost and in a personalized way, using consumer insights through key data points, in order to track individuals’ expenses and calculate whether they would meet their financial goals, which can predict credit scores and prevent bad debts.
These are all exciting and important benefits that FinTech AI systems can deliver, enabling substantial economic and social gains, the ability to radically improve services and increase efficiency. But they also present potentially far-reaching risks, which are still not fully understood, with possible effects varying across users and social groups. These risks may arise from unrepresentative data, bias inherent in representative data, choice of algorithms and human decisions, based on their interpretations of AI, incl. whether humans are involved in the decision making process supported by AI.
The panel will discuss:
key technologies and their respective applications in FinTech solutions, focusing primarily on AI, but also its interactions with IoT, blockchain and edge computing, to provide the technological basis for the discussion.
the main risks arising from the applications of these technologies in FinTech, and how these risks manifest themselves in different social contexts, in particular. in developing markets. The discussion will also examine possible risk mitigation approaches, both in terms of data quality, but also testing algorithms and models, as well as the results and their explainability.
relevant standards and emerging reference regulatory frameworks, with an emphasis on the intersections between non-binding best practice approaches and the approaches envisaged by regulators, again mainly in emerging markets.
developments in FinTech companies, including in anticipation of upcoming regulations and market expectations, i.e. what FinTech companies are currently doing to address key risks and how they prepare for upcoming regulations.