Emerging Challenges of Generative AI in Finance – PYMNTS.com

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The financial services sector has long served as the proving ground for the application of emerging technologies. The current era of disruption is no exception to this history. Generative artificial intelligence (AI) represents the latest in this line of transformative technologies reshaping finance and banking, with applications for everything from enhancing consumer interactions to refining risk assessment models. Its influence is already pivotal in financial decision-making, yet generative AI introduces significant challenges. These encompass the risks of propagating incorrect financial information, exposing sensitive banking data to security breaches, and expanding the digital gulf between modern and developing economies.
Banks and financial institutions (FIs) are actively developing strategies to navigate these complexities, employing innovative approaches to mitigate the risks associated with generative AI integration. Moreover, the institution and expansion of regulatory guardrails are crucial for managing these challenges, ensuring that the deployment of generative AI in the financial sector is both safe and secure. The focus lies not only in recognizing — and harnessing — the potential of generative AI but also in emphasizing the importance of strategic and regulatory frameworks to fully capitalize on its capabilities.
With the aid of generative AI, the financial industry has accelerated the adoption of banking as a service (BaaS) and embedded finance, marking a shift from planning to implementation. A recent report reveals a substantial increase in BaaS adoption across global financial institutions, rising to 48% from 35% in 2022. Similarly, embedded finance is witnessing significant growth, jumping by 8% in the past 12 months.
Generative AI is rapidly gaining traction in the financial sector, primarily as a tool to meet the rising demand for personalized customer services. However, its applications extend far beyond this usage to encompass critical areas like environmental, social and governance (ESG) and anti-money laundering (AML) initiatives. The global rise in implementation this year has rendered generative AI an instrumental technology in advancing key focus areas within financial services.
Generative AI’s emergent role in financial services is significant, as approximately 90% of FIs in the United Kingdom were already employing predictive AI in back-office functions. Predictive AI in finance is largely used to forecast future events based on historical data, while generative AI creates new, synthetic data and insights with implications for financial modeling and analysis beyond existing patterns. More than 60% recognize the potential of generative AI to drive substantial cost reductions and operational improvements. Supporting this level of optimism will require a thorough reassessment of business models, workforce capabilities and the considerable resource demands of AI technologies, particularly in the context of supply chain sustainability.
In the highly regulated financial sector, caution prevails, with more than 70% of generative AI applications still in experimental stages. Achieving a return on investment depends on the quality of data and the technology’s seamless integration into existing frameworks, a process anticipated to take the average solution three to five years. At the confluence of predictive and generative AI is where transformative potential lies, yet it introduces new challenges like the now-infamous hallucinations and complexities that plague external model sourcing. Despite these hurdles, 60% of U.K. institutions feel equipped within their current risk management strategies to accommodate generative AI.
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