Regtech promises to disrupt the regulatory landscape by providing technologically advanced solutions to the ever-increasing demands of regulators across the world. Regtech is considered the business model of the fintech landscape.
These processes need to be executed by abiding by rules and regulations in order to get the desired output with minimum malfunctions. Such processes are known as regulatory processes. Regtech is simply the management of these regulatory processes using technology. Regtech makes use of modern technology like Artificial intelligence, Big data, and Data mining to provide easy, cost-effective, and reliable regulatory solutions.
Regtech has become an important aspect in the finance domain as the extent of regulations is rising with more focus on data and reporting. Using technology like artificial intelligence, NLP, and machine learning, Regtech is automating the processes which were first executed manually
Today regtech is considered a subset of Fintech. There is a rapid rise in the Regtech family on a timely basis. Each company offers or comes up with a new solution to the problems related to compliance. Regtech universe is a universe of many such companies broadly classified categories according to their niche. Following are the different categories in the Regtech universe.
- Regulatory reporting
- Risk management
- Identity management and control
- Compliance
- Transaction monitoring
Regulatory Landscape of Reporting
Companies under this category usually do the reporting work of raw materials or operations carried out in financial institutions. Slowly these companies are moving towards automation of processes. By automating the operations and reporting in an organization using data analytics, the outputs are more accurate. Also, risks are reduced as there is no manual intervention.
Risk Management
Companies under this category use technologies like artificial intelligence and machine learning to manage risks. Automation is being implemented in order to reduce errors. Technology like predictive analysis is used to predict the risks in the future and to take required measures to combat the risks.
Identity Management and Control
Companies use artificial intelligence and machine learning for verifying the identity of customers. Machine learning learns from the existing database and with the help of Artificial intelligence based on factors like photos, signatures, etc it automatically verifies the identity of the customers.
Regulatory Landscape of Compliance
As mentioned before in any financial industry around the globe, there is a number of different processes. These processes need to be executed by abiding by rules and regulations in order to get the desired output with minimum malfunctions. Using automation, companies are making all efforts to comply and stay updated with regulatory standards.
Transaction Monitoring
Companies use AI to keep a track of all consumer transactions. This maintains integrity and mitigates risks. The transaction is safe and frauds like money laundering are curbed. With the help of machine learning, the systems can predict the upcoming frauds, and accordingly, measures are taken to curb them.
