RegTech: Passive to Predictive

RegTech: Passive to Predictive

FinTech has pervaded across the entire spectrum of commerce, banking, insurance, asset management, robo-advisory and other financial services. This has brought to the fore the challenge of regulatory compliance. Through the use of new technologies, regulatory technology, (RegTech) is transforming this area of the financial industry. Here we chart how regulation and compliance is evolving from being passive to being more reactive to its current state of being proactive. We believe it is yet to arrive in the final two stages: Predictive, using advances in Machine  Learning, Big Data and Analytics; and International RegTech as all these previous stages incorporate international regulatory compliance as nations adopt comprehensive cooperative measures.

 In the aftermath of the credit crisis of 2008, new regulations were slapped on financial institutions with penalties for non-compliance. It gave rise to a regulatory fatigue within the FinTech landscape, creating a demand for innovation.

An agile technology using SaaS in the cloud was devised to address regulatory challenges and streamline the compliance process. RegTech, automates regulatory risk compliance in a real time connected financial ecosystem, facilitating the delivery of regulatory requirements across geographies and financial services.

The innovation trajectory – from passive to predictive

With savvy start-ups filling-in the various industry requirements, the implementation of RegTech has witnessed a revolutionary transformation. Innovation, niche deliveries, seamless operations and an increased use of cutting-edge tools mark RegTech today.

Passive

At the very outset, RegTech served as a leveller of regulations with passive applications. The compliance automation functioned in regulatory sandboxes to enforce banking and financial service regulations. Focus was on compliance. The natural fallout was reduction of operational risks, which minimised penalties. The cloud-based underpinning also ensured responsiveness and flexibility in a given environment.

RegTech applications automate risk management processes, facilitate regulatory reporting, enable companies to stay abreast of regulatory changes around the world and support strategic planning.

Reactive

An increase in the need for risk management called for a more reactive engagement of the RegTech format. RegTech became more agile, addressing challenges of legacy based systems by seamlessly integrating, for updates of compliance manuals and regulatory reporting, in keeping with the strategic policies of the institution as and when they occurred. We still see this now as many large firms still appear to have inadequate risk management systems.

Proactive

With FinTech becoming more pervasive, and the amount of data voluminous, RegTech is reinventing itself to a proactive mode to meet the needs of today. In-house violations and third-party frauds have necessitated a more intuitive and real-time approach to RegTech. Big data aggregation using data mining, risk modelling for bank stress-testing, monitoring of capital-requirement compliance, are other areas of RegTech diversification.

With financial data becoming the cornerstone of opportunities, the RegTech trajectory has also begun leveraging regulatory focused data to better understand and manage compliance risks.

Predictive

We are now entering the next phase of development. The true potential of Big Data is still being unlocked with cloud-based solutions, and technology-driven products. Predictive analytics, Machine Learning (ML), use of APIs, biometrics, computational statistics and deep learning algorithms, are increasingly leveraged for a regulatory reporting and analytics infrastructure. These tools are used cohesively or a-la-carte, to analyse fraudulent or suspicious activities for effective risk management.

As more and more data is generated, Machine Learning capabilities become more effective. The ML model is fed with historical datasets to build algorithms for complex fraud detection and risk controls. Machine learning and computational statistical tools support identification of red flags and trends at early stages and increasing before they cause damage.

Looking ahead: National to International

Though we have not yet seen the full move to predictive RegTech, partly because of cost and the difficulty of analysing Big Data – these barriers are slowly being eroded. The next focus will be a move from national to international regulatory frameworks. RegTech can be expected to transition from geographic silos to a comprehensive worldwide seamless integration in real time. This will enable more granular checks in AML and internal frauds and reduce severity of financial cycles and financial criminality.

About the Author
admin