By Monica Hovsepian, Head of Financial Services Industry at OpenText
The banking industry is in the midst of a major shakeup, thanks to Generative AI (GenAI). From streamlining operations to enhancing customer experiences, AI’s potential here is huge. However, its adoption hinges on one key factor: trust. As financial institutions continue to integrate AI-powered solutions, ensuring reliability, security, and ethical application is crucial.
But, why is trust to important in banking?
Trust is fundamental in banking, as customer relationships are built on security, transparency, and reliability. As GenAI becomes more integrated into financial services, institutions must ensure that AI systems are both explainable and ethical.
A key concern is AI’s decision-making process. Customers and regulators need assurance that AI-driven recommendations, whether for lending decisions or fraud detection, are based on unbiased, well-audited models. To achieve this, financial institutions should prioritise AI governance frameworks that provide visibility into how AI systems function and make decisions.
Cybersecurity also remains a key factor in trust. AI systems process vast amounts of sensitive customer data, making them prime targets for cyber threats. Banks must implement robust cybersecurity measures, including real-time AI monitoring, to safeguard against data breaches and fraud.
How GenAI helps bankers, not replaces them
One of the biggest misconceptions about AI is that it will replace human expertise. In reality, AI is most powerful when it amplifies human potential—enhancing innovation, intelligence, and problem-solving to drive better outcomes. Through processing vast amounts of data quickly, AI can identify patterns and insights that would be difficult or time consuming for humans and employees alone to discern. For example, a mortgage application can sometimes be 900 pages and utilizing GenAI, an underwriter can pull relevant information from the vast content to make intelligent decision regarding the application. Conversely, human oversight is essential to interpret AI-generated insights, apply critical thinking, and ensure ethical considerations.
For example, in customer service, AI-powered chatbots can handle routine inquiries, freeing up human agents to focus on complex, high-value interactions. Similarly, in risk management, AI can analyse real-time market data to detect anomalies, while human analysts are needed to contextualise and act on these insights.
The Role of Data
AI’s quality is only as good as the data on which it is trained. Financial institutions must ensure that their AI systems are fed high-quality, unbiased, and comprehensive datasets to avoid flawed outputs. Data transparency and integrity are vital for AI-driven financial services. This is highlighted by OpenText’s State of AI in Banking Report, which underscored that AI’s ability to interpret and communicate insights accurately is directly tied to the quality of its data sources. Banks must invest in robust data governance policies to ensure AI models remain fair, accurate, and compliant with regulatory requirements.
Looking ahead
For banks to fully realize the benefits of GenAI, they must build AI systems that both customers and regulators trust. This requires strong AI governance, secure and ethical applications, and a commitment to augmenting rather than replacing human expertise.
Trust, transparency, and human oversight will be the defining factors in AI’s success in banking, ensuring that financial institutions remain both innovative and reliable in the digital age.
