The Financial Conduct Authority (FCA) published its business plan for 2019/20, which outlines the key priorities for the coming year. While Brexit transitioning remains a major focus, there are also areas where advanced analytics and data are being prioritized as cross-sector priorities: combating financial crime and improving anti-money laundering practices; and ensuring innovation and the use of data work in consumers’ interests.
Innovation, data and data ethics
Data and technology are increasingly driving changes in financial markets, including business models, products and consumer engagement. As a regulator of these firms, the FCA needs to understand the change, potential harm to consumers, and its future role in shaping markets. The aim is to ensure that innovation, coupled with advances in technology and data use, works in consumers’ interests. Priorities for 2019/20 build on the FCA’s work through initiatives such as the Regulatory Sandbox and regulatory technology Techsprints and strategic interventions like Open Banking.
The FCA’s key priorities are: build on a deep understanding of innovation in the UK financial services markets, influencing the global response to financial innovation (FinTech) and developing our strategic approach to regulatory technology (regtech); explore whether we should put in place policy frameworks for how firms collect and use data, to protect consumers and enhance market integrity; consider where a global response is necessary to protect the users of our markets, while ensuring that it does not unnecessarily hinder the potential benefits that could be realized from innovation.
Financial crime (fraud and scams) and anti-money laundering (AML)
The FCA wrote in its business plan that it’s using the data received from our its financial crime return for firms, together with a range of data and intelligence, to monitor trends in financial crime. The FCA is also strengthening its ability to use technology to target criminals. The FCA said it will use more advanced data analytics and machine learning techniques against money launderers and other financial criminals.