The Rise of RegTech: Balancing Innovation With Accountability and Security
There is a revolution underway in compliance, but technical and ethical challenges abound
By Corinium Global Intelligence
Financial institutions are beginning to manage compliance in new ways, pursuing greater speed, accuracy and adaptability in a sector long slowed by manual processes and the need to keep pace with evolving global rules.
Regulatory technology – or “RegTech” – is powered by machine learning, natural language processing, and real-time data analytics and is ushering in a new era of compliance, says Pravin Kumar, Intelligent Automation Leader for RPA and AI at First Horizon Bank.
This novel use of AI is enabling firms to monitor, detect, and report anomalies with a heightened level of precision, helping financial institutions to reduce regulatory errors and avoid costly fines.
“What used to take weeks can now be flagged in seconds,” Kumar says.
But Kumar warns that challenges – including lagging regulatory frameworks, model transparency and explainability gaps, privacy compliance, and unethical algorithmic bias – still plague the RegTech revolution.
“We are driving a high-speed AI train, but the legal tracks underneath it are still being laid,” he says.
Real-Time monitoring and global compliance
At the core of the RegTech movement is the ability to continuously scan and adapt to regulatory changes across jurisdictions, Kumar says.
As financial firms – many of which operate across borders – work to comply with an increasingly complex and fragmented web of local and international laws, AI helps maintain a real-time view of changing rules across jurisdictions.
Kumar explains that AI tools can automatically update compliance protocols when regulatory changes occur – ensuring that localized rules are implemented instantly across systems and enabling fast-expanding institutions to easily keep their compliance systems up to date.
“With smart rules engines and localized models, firms can remain compliant without manual intervention,” he says. “This is especially valuable in high-frequency environments like trading and cross-border payments.”
Natural Language Processing transforms regulatory reporting
Natural language processing tools can now extract insights from a massive amount of unstructured data, summarize risk-related content, and map regulatory changes to internal policies, thus dramatically easing the burden on staff, says Kumar.
“Compliance teams used to have to read 200 or 300 pages of regulatory documents,” he says. “Now AI can highlight only the risk-relevant sections, reducing the manual workload and enhancing both accuracy and speed.”
Despite its promise, Kumar cautions that the RegTech revolution is not without its risks, including lagging regulatory frameworks, lack of model transparency and explainability, and algorithmic bias.
“Regulation often trails innovation,” he says. “Institutions struggle to align agile AI systems with rigid frameworks. The black-box nature of some models adds to the complexity, especially when auditors or regulators demand interpretability.”
He warns that biased or poorly trained AI models can lead to discriminatory outcomes.
“Model bias is often unintended but very real. If the input data is flawed or the model isn’t properly monitored, you’re going to end up with bad results,” he says. “There is always a concern – what if this AI model approves a wrong loan? What if it rejects a valid or genuine customer? So there's a lot of things and a fragmented global compliance standard.”
He also stresses that AI will only remain helpful for businesses if they maintain it properly. That includes feeding it the right data, keeping high compliance and governance standards and having human oversight that prevents over-reliance.
“In decision-making, if the data is wrong, then obviously the decision-making is going to be wrong,” he says. “We need to have a human in the loop and compliance in place to monitor things.”
Building trust through AI governance and strategy
The rise of RegTech is not just about automation, it’s also about redefining trust in AI systems with governance, responsible principles and robust frameworks, Kumar says.
He points out that financial institutions are now investing in AI governance frameworks that cover the entire model lifecycle, from development to deployment.
That includes leading banks like JPMorgan Chase, which are already implementing automated compliance engines within their model risk management platforms. These tools can flag AI or ML models that violate internal standards, enabling pre-emptive risk mitigation.
Other best practices should include centralized compliance dashboards, integration with enterprise governance, risk and compliance systems, and comprehensive documentation and traceability for every AI model in use, he says.
“Remember, RegTech is not just about automation,” Kumar says. “It’s about making AI accountable.”
He cautions that as the financial sector continues to embrace AI, the challenge will be not only to harness its speed and power but to do so with transparency, fairness and trust. Doing so will ultimately turn compliance from a tedious task into a tool to provide a competitive edge.
“With robust frameworks, continuous tracking and responsible AI principles, we can turn compliance from a checkbox into a strategic advantage.”
