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  • Writer's pictureSanni Salokangas

How is machine learning used in compliance tech?

What can machine learning do to help companies to adhere to regulations in a data-driven world in which companies pay billion-dollar fines for GDPR violations?

Who oversees compliance?

The zoo that is regulatory compliance is getting more and more difficult for companies to navigate. The speed that technology is advancing today can leave critical gaps within a company's compliance framework and lead to severe financial penalties. Especially for startups, failing to adhere to regulations like GDPR can become even life threatening for operations. It is important for any company to understand their regulatory framework, whether the adherence is managed in-house or outsourced to a regulatory technology (regtech) company like a consultancy firm, a third-party service provider or a law firm.

The use of machine learning (ML) to automate and streamline processes is now revolutionalizing ways that companies ensure the collection of customer, financial, employee and operational data amongst many others to stay compliant within industry standards. Systems that used to take hours of manual labor and often arrived with a heavy bill can now be done by algorithms that analyze massive amounts of data and manipulate it in ways that makes the job easier and less labor-indusing for humans. Compliance technology uses machine learning namely in risk assessment and prediction, customer due diligence, KYC and fraud detection.

Risk assessment and

fraud detection

To proactively mitigate risks that are widely involved in regulatory compliance, machine learning provides algorithms that work with data that have access to past anomalies, patters or issues in compliance. These systems can detect potential reruns in the future or even eliminate the risks fully. In addition, training a ML model to assign risk scores to transactions and that way assess each transaction's likelihood for noncompliance offers banks, for example, an unforeseen method of identifying suspicious activity. It is also used to detect fraud by analyzing transactions in real-time to flag potential anomalies. Before the maturing of AI and ML, banks relied fully on rule-based and manual analysis systems, increasing the risk of low-quality compliance and higher costs.

Machine learning is a subfield of artificial intelligence that requires little to no human intervention to train itself from sets of data. ChatGPT, for example, uses machine learning.

Do you know-your-customer?

User data collection is a slippery road for compliance adherence. Know Your Customer (KYC) is a process that companies use to identify their customers. To verify user for who they claim to be, a company needs to verify them to eliminate money laundering, fraud and other illegal activity. Machine learning provides solutions, such as automated verification of an authentic document, facial recognition from a photo, risk profiling to assess previous activity and behaviour of an user or even biometric analysis, like an iris scan, for additional verification. KYC is an essential part of various industries. Therefore, machine learning will play a significant role in enhancing onboarding processes and customer experience, detecting fraudulent activity, and minimizing faults in user verifications.

To summarize, there are different ways to ensure the compliance to ever-tightening regulations that are crucial for a working data-driven economy. Whether compliance tech is outsourced to a third-party or managed by a CCO in-house, machine learning is changing the traditional labor-dependent and expensive processes that companies use to avoid getting fined $1.3 billion for violating GDPR like Meta did in May 2023. Expensive!

Sanni S



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