Regulatory technology or RegTech, along with wealthtech and proptech (property technology), is on the rise. Regulators are using it more and more and venture capitalists are investing in it, even though its take-up among firms seems to have plateaued temporarily.

Awards

ComplyAdvantage, which uses data science and machine learning to tackle the risk of global financial crimes, has won the coveted prize for Best RegTech Solution at Finovate’s Award ceremony in New York City. NICE Actimize, meanwhile, has been awarded “Best Compliance RegTech Global” by Capital Finance International in the US for the second consecutive year. It claims that its newest and most glamorous product, SURVEIL-X, detects virtually all forms of risky behaviour.

A regulatory report

British financial regulators have long been encouraging financial firms to take up machine learning, the development of models for prediction and pattern recognition from data with limited human intervention, especially as a way of countering fraud and money laundering. The Financial Services Authority and the Prudential Regulation Authority have now published a report on progress.

The different stages of the so-called "machine-learning pipeline" are:

• the acquisition and ingestion of data;
• the selection and engineering of features;
• model engineering and performance metrics;
• model validation (i.e. tests to see if the model works as expected); and
• deployment and safeguards.

The regulators sent a questionnaire out to almost 300 firms, including banks, other lenders, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers and principal trading firms, and received 106 responses. The survey has revealed several things.

Machine learning is being used increasingly in the financial service sector, with two-thirds of respondents already using it in some form. The median firm uses live machine learning software in two business areas and this is expected to double in less than three years. In many cases, firms' machine learning has passed the initial phase of development and is being deployed in a mature way. One-third of machine-learning applications are used for a considerable share of activities in a specific business area. Deployment is most advanced in the banking and insurance sectors.