The regtech space is in for a major shake-up, with the FCA‘s new Consumer Duty regulations coming into effect in two months. This presents an opportunity for financial institutions to adopt a new approach to compliance and regulation.
We established how suptech is playing a role in helping regulators promote financial inclusion and keep up with rapid tech innovations. However, we would be remiss if we did not mention the development of artificial intelligence (AI) and machine learning (ML) and the consequential impact this is having on regtech – more specifically suptech. To better understand this, we reached out to the industry to find out why incorporating these technologies is so important.
Collecting and analysing data efficiently and effectively
In the modern day, organisations must find the most efficient way to spend resources. For Andrea Maria Cosentino, founder and host of Crypto Club at Rise by Barclays, regtechs can do so by incorporating AI and ML technologies:
“Suptech solutions can incorporate emerging technologies like artificial intelligence (AI) and machine learning (ML) to improve regulatory oversight in several ways, including:
– Risk assessment
“AI and ML algorithms can help regulators identify potential risks and patterns of behaviour that might otherwise go unnoticed. For example, these algorithms can analyse large amounts of data to identify trends or anomalies that may indicate fraud or other illegal activities.
– Compliance monitoring
“AI and ML can help regulators monitor compliance with regulations in real-time by automating the collection and analysis of data. This can help regulators detect and respond to violations more quickly, reducing the risk of harm to consumers and the financial system.
– Fraud detection
“AI and ML can help regulators identify potential instances of fraud or financial crime by analysing large amounts of data to identify patterns and anomalies. This can help regulators detect and respond to fraud more quickly and effectively.
– Predictive analytics
“AI and ML can help regulators predict future trends and potential risks by analysing historical data and identifying patterns and relationships. This can help regulators anticipate and respond to emerging risks more proactively.
– Natural language processing (NLP)
“NLP can help regulators analyse unstructured data, such as social media posts or customer reviews, to identify potential risks or issues. This can provide regulators with a more complete picture of the risks facing the financial sector and enable them to respond more effectively.
“Overall, incorporating emerging technologies like AI and ML into suptech solutions can help regulators improve regulatory oversight by enabling them to collect and analyse data more efficiently and effectively, detect emerging risks and trends more proactively, and respond to potential issues more quickly and effectively.”
Improving data analysis and pattern recognition
Ensuring compliance and identifying bad actors has become much harder in the last decade. As technological advancements are made, fraudsters are manipulating these technologies to benefit themselves. Improving data analysis and pattern recognition is one way AI and ML can boost suptechs and ensure bad actors are identified explains Andrew Latham, director of content of SuperMoney.com, the financial comparison site.
“Emerging technologies like artificial intelligence (AI) and machine learning (ML) can be integrated into suptech solutions to improve regulatory oversight by automating complex and time-consuming tasks, such as data analysis and pattern recognition.
“By harnessing the power of AI and ML, suptech tools can process vast amounts of structured and unstructured data, enabling regulators to gain a deeper understanding of the financial landscape and identify potential risks and compliance issues more effectively. Moreover, AI and ML can enhance predictive analytics capabilities, allowing regulators to anticipate market trends and shifts, detect fraudulent activities, and proactively address potential issues before they escalate.”
When compliance innovation meets ESG
Fraser Stewart is the co-founder and COO of Lyfeguard, a platform that simplifies life planning. For Stewart, AI and ML’s emergence in the regtech market allows for greater financial inclusion. He said: “The evolution of suptech has brought a new dynamic to the financial industry, enabling regulators to keep up with the rapid pace of tech innovation. Fintechs can play a crucial role in this context by developing innovative suptech solutions that enhance regulatory capabilities and ensure more effective supervision of financial institutions.
“Suptech can be a powerful tool for promoting financial inclusion. By leveraging technologies like AI and ML, suptech can facilitate the creation of risk models that incorporate previously excluded demographics, thus broadening the scope of financial services.
“Over the past year, we have seen the effectiveness of AI-driven analytics and real-time reporting in enhancing regulatory oversight. These tools have not only improved efficiency, but also enabled proactive risk management. Compliance and ESG can go hand-in-hand with using regtech solutions that streamline compliance processes while embedding ESG considerations into business operations.
“The journey towards national/international expansion can be fraught with regulatory hurdles. Leveraging regtech can simplify this process by providing insights into local regulations, automating compliance processes, and enabling seamless reporting.
“One of the most crucial regtech lessons from the past 12 months is the value of adaptability. With regulatory landscapes constantly evolving, quickly adapting to new regulations and implementing changes is thus essential for success.”
Enhancing entity verification
Lastly, we heard from Stephan Wolf, CEO, Global LEI Foundation (GLEIF), online source for open, standardised and legal entity reference data. Using GLEIF’s partnership with Sociovestix Labs as an example of how ML can help suptechs:
“Machine learning tools offer great promise for improving data quality and creating structured datasets, enhancing entity verification processes and supporting improved regulatory oversight.
“For example, GLEIF has collaborated with Sociovestix Labs to create a machine learning tool that recognises an entity’s specific legal form and automates the assignment of its corresponding Entity Legal Form (ELF) code.
“These legal forms are a crucial component when verifying and screening organisational identity. However, the wide variety of legal forms that exist within and between jurisdictions has made it difficult for organisations to capture legal forms as structured data.
“The new tool enables organisations to retrospectively analyse their master data, extract the legal form from the unstructured text of the legal name and uniformly apply an ELF code to each entity type. By creating richer data sets with improved categorisation of legal entities, the tool promotes greater insight and transparency into the global marketplace. Furthermore, it works in tandem with the Legal Entity Identifier to create a globally consistent data set.