RegTech (Regulatory Technology) is the application of emerging technology to improve the way businesses manage regulatory compliance.
RegTech is evolving quickly, despite its youth. Machine learning, natural language processing, blockchain, artificial intelligence, and other technologies are now being used by RegTech businesses to bring the power of digital transformation to the world of regulatory compliance and risk management. Since 2015, the global Regtech market has grown every year. The greatest reason for this is the rise in financial institutions’ duties. The growing quantity of financial system laws and audits indicates that the Regtech sector will expand as well.
Supervisors can use RegTech to increase the speed and accuracy of analytics, as well as fill oversight gaps caused by the influx of new data and information required by the changing financial and regulatory environment. Ironically, central banks across the globe are fast adopting technologies to oversee financial institutions using big data and AI tools grouped under “SupTech”.
RegTech can help FIs with the following core issues :
Regulatory Reporting – The increasing volume and granularity of data that licensees anticipate, as well as the transit and storage of huge files, have become a problem. Problems arise when data is requested and collected in a format that resembles paper reports, despite the fact that it is exchanged electronically. Manually filling out and submitting regulatory returns takes longer and is more prone to human error. Regulatory reporting solutions emphasize data sharing automation using APIs and cloud computing, allowing regulators to access data and information in an organised, simplified format on a single server. This could help with the audit process by allowing real-time transaction data to be reviewed for better monitoring and auditing.
Risk Management – To comprehend and enhance estimates of future threats, stress testing and scenario analysis, as well as other types of risk modelling, rely on huge amounts of aggregated risk data (both historical and forward-looking). Risk management software automates and simplifies risk data in a well-organized database to calculate current exposure levels, capital, asset quality, and liquidity ratios, among other things. AI and machine learning can be used to detect and assess the risks of non-compliance, allowing for early detection of dangers and prompting corrective action. Modeling components, in particular, can rely on big data analytics by applying massive computational power to the data provided.
Transaction Monitoring – Know your customer (KYC) regulations are a set of rules that compel financial organizations to gather and analyse customer or counter-party data in order to help detect and prevent fraud, money laundering, and terrorism funding, among other things. KYC processes are made more efficient by the decentralised database developed by Distributed Ledger Technology (DLT), which stores immutable transaction data that may be changed by institutional members of the blockchain, resulting in a permanent financial record. AI and machine learning can be used to interpret trends in client behaviour and flag, prevent, or report suspected criminal activities in real time using transactional data.
Identity Management and Control – These technologies can rely on DLT by keeping all KYC data in a secure database, resulting in a digital identity for a client or counter-party that can be checked in a timely and cost-effective manner. Financial institutions that have consented to participate in the blockchain can promptly access and transparently update KYC data about shared customers and counter-parties by storing due diligence data in a decentralized location. Biometric technologies, such as fingerprint scanning and facial recognition, can help enable blockchain identity control by enabling digital identification. AI, machine learning, and big data analytics can also be used to analyse the existing database and provide ad hoc reports to help with risk management.
Compliance – Regulatory watches keep track of relevant upcoming regulations; compliance project management defines the tasks and resources required to comply with new regulations within a specified timeline; regular compliance health checks; and cyber security and due diligence are all part of the compliance function. These jobs can become quite complicated as they necessitate the cooperation and collaboration of multiple departments within an organization. Compliance solutions provide better, real-time regulatory monitoring based on artificial intelligence (AI), which automates the interpretation of regulations. This facilitates a constant compliance health check and allows institutions to keep up with the pace of regulatory changes.
The study of RegTech’s development and benefits, as well as the barriers to its adoption, would certainly benefit from a more precise definition of its numerous elements as its role in financial services grows. A common taxonomy can assist stakeholders communicate in the same language, refer to the same topics, and expand the RegTech ecosystem. It can serve as a foundation for future academic study in the RegTech sector, as well as a starting point for the development of critical standards to help RegTech progress.