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Every technology depends on the quality of data available within the business process, and AI and generative AI are no exception. This is especially important when it comes to accurately assessing customer risk during know-your-customer onboarding. While these tools aim to tackle data-related challenges, banks still need to ensure their databases are accurate by using technology that goes beyond what AI alone can offer.
Looking beyond the hype, AI solutions must be solutions-focused, demonstrating clear value, scalability and security. As financial firms rethink their approach, this year will call for a balance between experimentation and execution to help ensure that AI investments deliver measurable value.
From an information security standpoint, mentioning AI in a product triggers rigorous compliance checks to ensure the technology aligns with regulatory and privacy standards. Questions like "how is this built?" and "what was it trained on?" are becoming commonplace in decision-making. The initial rush to adopt AI often prioritized the "wow" factor over practical applications. Now, the era of experimenting with AI is giving way to a more deliberate strategy.
The digital transformation journey, however, is complex and according to
A key challenge for organizations will be staying ahead of rapid advancements in AI, machine learning and gen AI while equipping their teams with the skills needed to capitalize on opportunities ahead of competitors.
There is a technology "arms race" when it comes to financial crime because AI is a double-edged sword. On one hand, bad actors are leveraging AI to perpetrate sophisticated fraud, generating fake identities and fraudulent documentation that are increasingly difficult to detect. On the other hand, financial institutions are deploying advanced AI tools to counter these threats. AI-driven fraud now contributes to
Using traditional methods to uncover fraudsters requires labor-intensive manual investigations. AI can rapidly analyze vast datasets and uncover patterns and other "hidden in plain sight" information.
Rumors that generative AI would kill banking jobs so far seem greatly exaggerated, but the technology is spawning new job titles.
When it comes to rethinking product development, AI serves as a "copilot" — assisting in coding and accelerating the translation of vision into actionable requirements. Product managers can increasingly rely on AI to draft project plans, breaking down high-level strategies into executable steps. These tools free up human creativity and focus, enabling financial firms to build innovative products faster and with greater precision.
This efficiency in development is matched by AI's ability to create highly personalized solutions, helping firms tailor their offerings. Business customers are behaving more like regular consumers, demanding a more customized experience. Financial institutions can utilize predictive analytics to better understand and anticipate customer needs, while simplifying routine tasks.
For instance, chatbots and virtual assistants are being designed to provide more natural and engaging interactions, combining the efficiency of automation with elements of human understanding. By analyzing transaction histories and other data points, firms can proactively offer solutions, turning the customer experience into a competitive advantage.
It's still a bit of a "wild west" when it comes to AI governance but as regulation begins to catch up, early leaders in the field may need to pause, reconfigure and revalidate their systems to ensure compliance. Regulators are looking for transparency about how these systems are built and what data underpins their functionality.
AI performance is only as good as the data it is fed, and financial institutions process massive volumes of data, making efficient data management essential. Corporate Digital Identity, or CDI, addresses this challenge by leveraging advanced technologies, including AI; security frameworks; and robotic process automation. CDI integrates data from both public and private sources while maintaining compliance with regulatory requirements, such as documenting data sources.
By automating how they collect and integrate data from trusted public sources, banks can work more efficiently, improve first-time success rates and free up resources for more meaningful activities — like growing the business and building stronger relationships with clients.
As AI adoption becomes increasingly strategic, financial institutions should return to fundamental principles to guide their investments. Key questions to consider include whether the technology provides a sustainable advantage, whether it is scalable and whether it addresses a real problem. Firms should also evaluate if the technology enhances operational efficiency or improves the customer experience. It is important to determine if the investment is a sound, long-term decision or merely a temporary gimmick.
The era of unchecked AI enthusiasm is giving way to a more measured and deliberate approach. Organizations are beginning to see AI not as a panacea but as a powerful, albeit complex, tool that requires thoughtful implementation. Firms must now channel their energy into refining and scaling AI solutions that align with their strategic goals.
This shift calls for financial institutions to return to the basics — prioritizing data integrity, embedding a robust governance framework and fostering human oversight alongside technological advancements. In this new chapter, organizations that balance innovation with pragmatism will be best positioned to thrive in 2025.
