Suptech Expert Explains Importance of Incorporating AI and ML to Improve Regulatory Oversight

The Regtech industry is undergoing a major transformation as the FCA’s new consumer tax regulations take effect in less than two months. This presents an opportunity for financial institutions to adopt a new approach to compliance and regulation.

We have established what role suptech plays in helping regulators promote financial inclusion and keep up with rapid technological innovation. But it would be remiss not to mention the developments in artificial intelligence (AI) and machine learning (ML) and the consequent impact they are having on regtech (and more specifically suptech). To understand this better, we reached out to the industry to find out why incorporating these technologies is so important.

Collecting and Analyzing Data Efficiently and Effectively Andrea Maria Cosentino, Founder and Host of Crypto Club at Rise by Barclays

In today’s world, organizations must find the most efficient way to spend their resources. According to Andrea Maria Cosentino, founder and host of Barclays’ Crypto Club at Rise, RegTech can do just that by incorporating AI and ML technologies.

“Suptech solutions can incorporate emerging technologies such as artificial intelligence (AI) and machine learning (ML) to enhance regulatory oversight in several ways:

– risk assessment

“AI and ML algorithms help regulators identify potential risks and behavioral patterns that might otherwise go unnoticed. For example, these algorithms analyze large amounts of data to Identify trends and anomalies that may indicate fraud or other illegal activity.

– Compliance monitoring

“AI and ML help regulators monitor regulatory compliance in real time by automating data collection and analysis. This helps regulators detect and respond to violations faster.” reducing the risk of harm to consumers and the financial system.

– Fraud detection

“AI and ML can help regulators identify potential instances of fraud and financial crime by analyzing large amounts of data to identify patterns and anomalies. can be detected and responded to more quickly and effectively.

– Predictive analytics

“By analyzing historical data and identifying patterns and relationships, AI and ML can help regulators predict future trends and potential risks. can be more proactively predicted and responded to.

– Natural Language Processing (NLP)

“NLP helps regulators analyze unstructured data such as social media posts and customer reviews to identify potential risks and issues. You will have a more complete picture of your risks and be able to respond more effectively.

“Overall, incorporating emerging technologies such as AI and ML into supertech solutions will enable regulators to more efficiently and effectively collect and analyze data, more proactively detect emerging risks and trends, We can improve regulatory oversight by being able to respond more quickly and effectively to potential issues.”

Improving Data Analysis and Pattern Recognition Andrew Latham, Supermoney.com Content Director

Enforcing compliance and identifying bad actors has become much more difficult over the past decade. As technology advances, scammers are manipulating these technologies for their own gain. Improving data analytics and pattern recognition is one of the ways AI and ML can enhance supporting technology and reliably identify fraudsters, says Andrew Latham, director of content at financial comparison site SuperMoney.com. explains Mr.

“Integrating emerging technologies such as artificial intelligence (AI) and machine learning (ML) into SupTech solutions can automate complex and time-consuming tasks such as data analysis and pattern recognition to improve regulatory oversight.

“By harnessing the power of AI and ML, SoupTech tools can process vast amounts of structured and unstructured data, allowing regulators to better understand the financial landscape and identify potential risks and compliance issues. In addition, AI and ML will power predictive analytics capabilities, helping regulators anticipate market trends and changes, detect fraud, and magnify potential problems. so that we can deal with it proactively before it happens.”

When Compliance Innovation Meets ESGFraser Stewart, Lyfeguard Co-Founder and COO

Fraser Stewart is co-founder and COO of Lyfeguard, a platform that simplifies life planning. For Stewart, the emergence of AI and ML in the regtech market will enable greater financial inclusion. “The evolution of saptech has brought new dynamics to the financial industry, allowing regulators to keep up with the rapid pace of technological innovation,” he said. Fintech can play a key role in this situation by developing innovative supertech solutions that strengthen regulatory capacity and ensure more effective supervision of financial institutions.

“Suptech can be a powerful tool for fostering financial inclusion. By leveraging technologies such as AI and ML, suptech can help shape risk models that incorporate previously excluded demographics. Facilitates creation and expands the reach of financial services.

“Over the past year, we have seen AI-powered analytics and real-time reporting effective in enhancing regulatory oversight. It also enables management: Compliance and ESG can go hand in hand with RegTech solutions that streamline the compliance process while incorporating ESG considerations into business operations.

“The road to national and international expansion can come with regulatory hurdles. Leveraging regtech can provide local regulatory insight, automate compliance processes, and enable seamless reporting. can simplify this process.

“One of the most important lessons we have learned from RegTech over the last 12 months is the value of adaptability. is essential to.”

Enhancing Entity Validation Stephen Wolf, Global LEI Foundation CEO

Finally, we spoke with Stephen Wolf, CEO of the Global LEI Foundation (GLEIF), an online source for open and standardized legal entity reference data. The partnership between GLEIF and Sociovestix Labs is an example of how ML can help support technicians.

“Machine learning tools hold great promise for improving data quality and creating structured datasets, enhancing entity validation processes, and supporting enhanced regulatory oversight.

“For example, GLEIF worked with Sociovestix Labs to create a machine learning tool that recognizes company-specific legal forms and automates the assignment of the corresponding Enterprise Legal Form (ELF) codes.

“These legal forms are an important factor in ascertaining and validating the identity of an organization. It is becoming difficult to obtain as cipher data.

“This new tool enables organizations to retrospectively analyze master data, extract legal forms from the unstructured text of legal entity names, and uniformly apply ELF codes to each entity type. , to improve the classification of legal entities to create a richer data set, thereby improving insight and transparency into global markets, and in conjunction with the legal entity identifier to create a globally consistent data set. increase.

Francis Binel

Francis is a journalist and chief correspondent in Latin America, holds a BA in Classical Civilizations, and has a professional interest in North and South America.

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