about 1 month ago by Sandip Kotecha

Regtech can be defined as the use of new technologies to satisfy regulatory and compliance requirements more effectively and efficiently.  Financial institutions are believed to have the responsibility to support regtech development, mainly by creating IT and risk infrastructures capable of integrating new solutions.  This blog will identify the main issues in compliance and regulatory reporting, discuss how they could benefit from regtech and which solutions in particular, and will also explain barriers to regtech implementation and development of the market.

Risk data aggregation is often required for capital and liquidity reporting, RRP and stress testing.  The effectiveness of this is dependent on the gathering of high quality structured data from across a business.  This is made difficult by the use of outdated IT systems.  Machine learning based data mining algorithms can organise and analyse large data sets, even if the data is of low quality, such as sets of emails.  Improvements in cryptography and distributed ledgers can also result in more secure, fast, efficient and effective data sharing within institutions.

Modelling, scenario analysis and forecasting, required for stress testing and risk management is becoming more and more complex and demanding in terms of computing power, labour and intellectual capacity.  This is because of the vast array of scenarios and variables that mustn’t be overlooked.  Machine learning can create self-improving methods for modelling and forecasting.

It is hard to monitor payments transactions due to the low quality and lack of compatibility of transaction metadata produced by payments systems.  This makes it hard to interpret transactions and identify money laundering and terrorism financing.  This is yet another area where machine learning can be applied to effectively sift through this data.  Blockchain and distributed ledgers could also be used to potentially increase transparency for financial institutions, as they can monitor transaction flows to identify potential criminals.

Identification of clients (KYC regulations) can be made more efficient through the used of automated identification solutions.  These could include fingerprint and iris scanning and blockchain identification techniques.

Monitoring the internal culture and behaviour of an institution normally requires the analysis of qualitative information provided by employees.  Automating interpretation from these sources through AI would enable progress in efficiency, capacity and speed of compliance.

Financial markets trading requires participants to carry out regulatory tasks such as margins calculation and choice of trading venue.  Automating these tasks using blockchain technology and smart contracts could vastly increase the speed and efficiency of trading.

Interpreting new regulations and implementing into day-to-day operations can be very labour intensive and complex.  However, these processes could be enhanced by automation, again through machine learning and AI algorithms.

In conclusion, it is clear that there is a lot of space where regtech can provide serious efficiency gains for firms, and should be given due attention by recruiters in financial services.