NICE Actimize Hetherington on AI Governance in Finance
With the rate of deception rising and oversight becoming stricter, NICE Actimize’s Chad Hetherington argues that artificial intelligence must be governed, transparent, and integrated into essential operations. Hetherington’s viewpoint on the application of AI within finance is grounded in a fundamental belief: technology should support specific business results rather than existing merely for its own sake.
Serving as the Global Head of Product for NICE Actimize, his remit involves working through the technological shifts occurring across financial services firms, focusing on difficult demands regarding anti-money laundering, enterprise fraud and payments, trade monitoring, case management, and more. “My work is specifically dedicated to the delivery and implementation of enterprise-grade solutions, and I oversee the development and execution of NICE Actimize’s corporate strategy, a focus that has driven substantial growth for the company in recent years,” he states.
This strategy establishes NICE Actimize as a worldwide leader in AI-driven tools for preventing financial crime, managing compliance, and handling risk. Operating as a division of NICE, the firm assists banks, payment processors, insurers, and other entities to identify deception, stop money laundering, meet regulatory duties, and safeguard clients using intelligent automation and sophisticated analytics. Their cloud-native infrastructure allows organizations to make quicker, better-informed choices, speed up investigations, ensure compliance with regulations, and provide reliable digital financial services.
Why certain AI programs expand – and others stall
When asked what differentiates businesses that successfully operationalize AI at scale from those stuck in endless pilot phases, Chad identifies three starting points: reliable data, well-defined business goals, and robust oversight. “In financial services, AI creates the greatest value when institutions understand where operational friction exists and apply intelligence to measurable business challenges,” he says, citing examples such as reducing fraud losses, accelerating investigations, improving customer onboarding, or strengthening regulatory compliance.
Conversely, institutions that remain in pilot mode, by contrast, often lack a clear data strategy or operational roadmap, he adds. “AI initiatives deliver lasting value only when they are embedded into enterprise workflows and measured against business and regulatory outcomes,” Chad explains. “Rather than deploying AI for its own sake, leading organisations identify where intelligence can remove friction, automate routine decisions, and enable employees to focus on higher-value work.” This disciplined approach, he argues, is what transforms AI from experimentation into enterprise capability.
Three foundations for enterprise-wide production
Transitioning AI from isolated pilots to secure, enterprise-wide production at scale requires three core capabilities, Chad outlines. First, institutions require trusted, governed data that can be securely accessed across fraud prevention, AML, payments, compliance, and customer engagement, he emphasizes. “AI is only as effective as the quality, accessibility and governance of the data that supports it.” Second, AI must be integrated directly into core workflows rather than tacked on as an afterthought.
Explainability, auditability, model governance, and regulatory controls need to be built into every operational process to safely expand AI while following changing compliance rules. Finally, it is vital that organizations invest in people as much as they invest in technology. Chad adds: “Successful transformation requires executive sponsorship, workforce training and operational discipline to ensure AI becomes a trusted part of day-to-day decision-making rather than another standalone technology initiative.”
Fragmented customer experience and compliance systems are among the biggest hurdles to AI transformation in banking and fintech. Financial entities function in one of the world’s most regulated environments – they must keep detailed records, protect customer data, and prove compliance across all processes from start to finish – meaning many organizations have collected disjointed systems designed to solve individual regulatory or operational problems rather than function together.
Those disjointed systems restrict AI’s capacity to drive tangible business value. “Customer information, fraud signals, transaction history and compliance data often reside in separate systems, preventing institutions from building a complete picture of customer behavior or emerging risk,” Chad says. “Modern AI platforms address this challenge by unifying intelligence across channels while preserving the governance, privacy and security controls required by financial institutions. The result is a more complete customer and risk view that improves operational efficiency, strengthens decision-making and enhances regulatory confidence.”
Strategic AI Adoption in Financial Services
Chad stresses that organizations succeeding in modernizing customer engagement treat AI as a strategic business initiative, not just a technical rollout. He explains that leadership teams must set clear goals, match tech investments with measurable business outcomes, and create accountability throughout the firm. Equally important is investing in staff through training, governance, and operational processes to ensure responsible and consistent AI adoption. The most effective institutions constantly measure results, refine models, and scale what works to show measurable business value while keeping trust with regulators, customers, and shareholders.
Based on his experience scaling operations, Chad notes that enterprise transformation starts with leadership commitment. He argues that aligning AI projects with strategic business goals ensures long-term success. Organizations must also prioritize workforce development to integrate AI effectively into daily work. By combining strategic vision with practical execution, financial institutions can use AI to improve customer experiences, boost operational efficiency, and strengthen regulatory compliance.
Trustworthy AI by Design
As AI reshapes financial services, Chad argues that the most successful fintechs blend AI with trusted governance to provide seamless, real-time customer experiences. He stresses that success depends not just on speed of deployment but on careful implementation to cut fraud, speed up investigations, and ensure compliance. This means embedding explainability, governance, auditability, and human oversight from the start to meet regulatory requirements like KYC, AML, and data privacy.
Chad continues: “AI can identify anomalies, prioritise investigations, authenticate customers, recommend and execute next-best actions, allowing employees to focus on higher-value work while improving both customer trust and operational resilience.” From his experience scaling operations, Chad sees clear lessons for fintech leaders modernising customer engagement. For him, enterprise transformation begins with leadership: “Organisations that successfully modernise customer engagement treat AI as a strategic business initiative rather than simply a technology deployment. “Leadership teams must establish clear objectives, align technology investments with measurable business outcomes and create accountability across the organisation.
.
