Five AI trends transforming financial services

By Sayon Deb

As AI solutions continue to generate buzz within the banking industry, how can banks cut through the clutter of instant AI experts on social media and salespeople with an angle to identify actionable developments? I recently attended the Re:Work AI in Finance Summit in New York City, where five key trends came to the fore.

Bankers interested in AI can focus on the challenge of creating user-centric AI solutions, the ethical and social implications of AI, the development of scalable and sustainable AI capabilities, advancements in processing and analyzing data and finally the value of generative AI applications like ChatGPT and Bard.

User-centric AI solutions

One of the most pressing issues discussed at the summit was the challenge of creating user-centric AI solutions. Translating complex technical concepts into simple, understandable language for all project participants is a practice AI experts refer to as “cognitive courtesy.” This is important because it promotes alignment and collaboration among team members who may come from different technical backgrounds or have different areas of expertise.

This concept also extends to how AI solutions are designed by keeping the end user’s perspective as the guiding principle. Specific applications of cognitive courtesy in product design may include using clear and concise language in user interfaces or providing explanations for AI decision-making processes.

Ethical and social implications of AI

As the use of AI in finance continues to grow, there is a pressing need to consider the effects of these technologies on society as a whole. The use of AI in banking and finance has raised concerns about privacy, algorithmic bias, transparency, potential labor market disruption, customer trust, financial inclusion, and systemic risk. Addressing these ethical and social implications requires a careful balance between innovation and fairness, emphasizing transparency, accountability, and inclusive practices. Experts across multiple sessions highlighted the importance of developing specialized hardware and software platforms that can ensure ethical and transparent use of AI in finance. They also emphasized the need for ongoing dialogue between industry stakeholders, regulators and the public to ensure that AI is developed and used in a responsible and beneficial way.

Scalable and sustainable AI capabilities

Developing scalable and sustainable AI capabilities was also a key focus of the summit. Experts emphasized that it is critical for financial institutions to develop a robust and coherent AI strategy and roadmap that aligns with business objectives as they look to build successful AI implementation. In addition to developing strategy and roadmap, the sessions also pointed to identifying use cases that are best suited for AI solutions and building the infrastructure and team to support implementation and scaling (that is, from innovation centers to enterprise-wide solutions) as key building blocks.

Meanwhile, bankers should beware common pitfalls, such as unrealistic expectations, resistance from end users, chasing after the latest tech, inconsistent strategy or conflicting architecture, or not considering risk and ethics early in the design process.

Advancements in data processing and the rise of generative AI

Finally, the summit tackled two interrelated themes: the latest advancements in data processing and analysis as well as the potential impact of generative AI applications that are based on data-intensive large language models such as ChatGPT and Bard in driving innovation and value in finance.

The next frontier in data and AI is expected to bring significant advancements in processing and analyzing data, along with more sophisticated algorithms. These advancements can be expected to drive innovation in customer experience, risk management and operational efficiency by enabling personalized banking experiences, enhancing fraud detection, and leading to more tailored and secure financial products and services.

Predictive analytics are likely to play an important role in helping identify potential risks and in recommending proactive risk management strategies, while automation tools will help streamline entire libraries of manual processes resulting in reduced operational costs. Additionally, blockchain-based solutions could also play a role in improving transparency and security in areas such as payments and trade finance.

Experts also discussed the opportunities and challenges companies face in using AI solutions to improve their processes, forecasting abilities, and regulatory compliance. Many of the most immediate use cases for generative AI may not be in “core” financial services but rather operational and supportive functions in bank customer engagement, marketing, branding and media outreach.

There are also several cautionary notes around adoption of generative AI, including gaps in recency and accuracy, proclivity of these models toward “hallucinations” (in which the AI confidently asserts as fact a false statement made up from the material it was trained on) as well as a lack of common sense (and domain-specific) knowledge. There are some potential legal and compliance issues to keep in mind as well, including data retention and memorization by the models, potential bias and privacy.

From user-centric solutions to ethical considerations and scalable implementation strategies, AI is certain to continue shaping the future of finance. AI technology is integrated into a range of software for many banking business applications, and banks can consider the key themes from this conference as they evaluate and implement this evolving technology.


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