The Future of Generative AI Finance & GenAI Banking
We’re in the early days of the next big leap in technology. It’s what some are already calling “the Fourth Industrial Revolution.” What many of us still aren’t really grasping, though, is how rapidly things are going to change, given AI’s exponential learning potential.
“Generative AI” (or “GenAI”) offers some exciting prospects. It can enhance art, elevate translation, refine data scripting, and enrich educational materials. However, the system is not without its issues. We can't overlook the potential for cybersecurity threats like deepfakes, by which an AI could convincingly imitate a high-ranking employee to make malicious requests. There are other general data integrity and privacy concerns, too, that need to be addressed.
So, what benefits and challenges does GenAI present for financial institutions and retail sectors? Let’s take a look.
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What is Generative AI?
Let’s start at the top, by explaining what GenAI actually is.
Generative AI is a branch of artificial intelligence. Specifically, it has the capability to produce diverse content such as text, images, audio, and synthetic data. The surge in interest surrounding genAI can be at least partially attributed to the user-friendly interfaces. The tool allows for the effortless generation of premium text, visuals, and videos, all in mere moments.
Advancements in transformer models and large language models (LLMs), notably those with billions or even trillions of parameters, mark a transformative point. Now, genAI can seamlessly produce compelling text, craft lifelike images, and even fabricate engaging video content. Further, the rise of multimodal AI facilitates the amalgamation of diverse media forms, from text to video. A prime example is Dall-E, a tool capable of translating text descriptions into images or vice versa.
It's best to approach this tech with caution, though. Generative AI, especially in its nascent stages, has seen issues like accuracy discrepancies, inherent biases, and unexpected and off-tangent outputs. Despite these hurdles, the trajectory suggests that this tech holds significant potential. It has particularly promising applications in areas like coding, system architecture, product innovation, business process enhancement, and supply chain evolution.
Early Applications of Generative AI Finance
Some banks are already integrating GenAI into their back-end operations as a proactive measure to foster innovation while sidestepping potential pitfalls.
So far, GenAI has shown potential in sifting through massive data sets to extract relevant information and insights. This is great for streamlining tasks like summarizing and research, translating to significant savings in time and effort. GenAI can efficiently distill both public and proprietary data, offering valuable insights to customers and clients.
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Furthermore, institutions can use generative AI to create concise overviews of rules, regulations, HR induction procedures, and training materials. IBM and Truist, for example, partnered to roll out a productivity tool for the Truist Wealth team back in July. This group generates summaries for over 350 requests for information (or “RFIs”) annually, supplied by fund managers. Leveraging GenAI’s knack for handling unstructured data, such as RFI feedback or online articles, could significantly reduce workloads and improve data accuracy.
Gen AI also has the capacity to tailor financial products and services to individual needs. Bud Financial, for instance, leverages transactional data to gain customer insights for financial institutions. They’ve been training their language models for this purpose over the years, improving with each successive rollout.
The buzz in the banking sector surrounding GenAI is palpable. Finance professionals are excitedly exploring how this technology can enhance current methods, be it through content generation or in-depth research.
The Push for GenAI in Finance
The current adoption of generative AI in finance is primarily for enhancing established procedures via automated content creation and occasional analysis of smaller data clusters. To get a better idea of how useful it could be in business, here's a breakdown of how it's being applied across various finance-related sectors:
Clearly, there’s something to this new trend. That said, while the progress made by tech giants like Open AI, Google, IBM, and Amazon is exciting, it’s worth noting that these advancements aren't quite foolproof enough yet to warrant an unreserved endorsement.
Gen AI Implementation Challenges
Dissimilar to earlier technologies like robotic process automation or process mining, delving into generative AI in financial areas seems more approachable and accessible. However, certain core challenges need addressing to truly harness its complete potential in business and finance. Take data accuracy, for instance.
Earlier-gen versions of these AI tools occasionally miss the mark in exact calculations. Precision is paramount, and careful crafting of these tools is essential. Alternatively, teams could lean on calculations done externally and then use AI for content creation.
The silver lining is that, with tech evolution, as seen in the transition from GPT-3 to GPT-4 (now featuring a code interpreter), these issues are on a downward trend. However, there are other concerns to weigh as well:
- Data Breaches: Proprietary data is put at risk while training generative AI models via public clouds. It could be compromised if a security breach occurs.
- Governance Modeling: Contextual understanding and real-time data feedback is limited. There's no definitive governance method to verify outcomes.
- Hallucinations: There are instances in which genAI might yield incorrect yet alarmingly convincing responses, referred to as hallucinations.
Given these hurdles, the deployment of GenAI is being approached in stages. The preliminary phase has an exploratory nature, zooming in on internal operational enhancements. The next step will be externally focused, targeting products that intersect with consumer interfaces.
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Steering Ahead of the Gen AI Curve
Generative AI's transformative potential in the finance landscape, plus in adjacent fields like HR and marketing, means CFOs can't simply watch from the sidelines.
Embracing this next evolution in tech is paramount to staying at the forefront of the shift. But how? Here are some steps that CFOs can consider to navigate this change:
What's Next for Gen AI in Finance?
Given the potential for a relatively frictionless adoption curve, waiting on the sidelines isn't a strategy. CFOs should wholeheartedly welcome this tech wave, clear any internal roadblocks, and rally their squads to leverage generative AI across the finance continuum.
Ultimately, collaboration is shaping the introduction of GenAI-driven financial products. Brands are molding their product offerings and deployment strategies. As a result, competition in GenAI is beginning to extend beyond the tech giants that typically lead these spaces. The focus has shifted from solely crafting superior language models to strategically using existing resources and collaborations.
This raises the question: will banks already equipped with AI-powered chatbots seamlessly integrate GenAI to enrich their offerings? Might banks without such digital offerings lag behind? Or, could up-and-coming tech frontrunners step in to equalize the landscape? How successful these deployments will be in the long run remains to be seen.