Navigating the Pros and Cons of Generative AI in FinServ – Spiceworks News and Insights

How can businesses make the most of generative AI in finserv while tackling the risks?

While financial services can benefit from generative AI, executives must also recognize the technology’s teething troubles and potential impact on verification, outdated information, and ethical issues. To realize its full rewards and minimize risk, businesses must first put the necessary guardrails in place, explains Dr. Stefan Sigg of Software AG.
The recent developments in AI technology offer capabilities unlike anything seen before. Early adoption of AI tech can help businesses transform difficult or time-consuming tasks to achieve a significant efficiency advantage over their competitors. Particularly in the realm of financial services, generative AI provides businesses with a tool that can assist in internal and external processes such as fraud detection, risk management, and personalized financial services.
From Morgan Stanley’sOpens a new window use of ChatGPT-4 to analyze texts and reports to Goldman SachsOpens a new window ’ utilization of in-house generative AI tools to assist their code writing, this technology will be a determining factor in the longevity and success of the financial services industry.
However, before implementing existing generative AI tools or developing their own, financial executives and their teams must understand its level of maturity and challenges. Organizations eager to implement the latest technology and industry trends must be careful to avoid common pitfalls regarding verification, outdated information, and ethical concerns.
Generative AI operates similarly to an aggregator, compiling data and information from various sources to create a compelling message. The problem is that this information usually doesn’t come with citations, creating concerns about the accuracy of what’s presented to users. Verification measures are vital to avoiding common inaccuracies with AI models. 
Even with OpenAI’s recent and major updates of ChatGPT-4, which now processes various types of input and media based on human feedback training, generative AI doesn’t yet meet the requirements to produce well-verified content. Banks and insurance companies must make reliable, fair, and traceable decisions based on the latest news. At its current stage, generative AI tools like ChatGPT-4 haven’t reached the necessary maturity level to be trusted by financial institutions without significant verification measures in place.
Take the risk management priority from earlier: Generative AI can help predict future market trends and potential risks. Generative AI can help businesses make more informed decisions about investments and other financial activities by analyzing large amounts of data. However, with the promise of added efficiency and productivity comes the need for verification. 
Users can intentionally or unintentionally prompt language models like ChatGPT and Bard to make inaccurate statements. AI tools can operate differently than initially intended and provide misleading information, potentially harming businesses and their customers. When Google debuted its generative AI tool, Bard, earlier this year, the model notoriously responded to users with fake information. 
Another challenge with generative AI involves attackers using deepfake video and voice techniques with ChatGPT-like tools to recreate convincing conversations and mimic customers. Hackers could use this technology to access customer data, open new accounts, or drain or transfer money, posing huge risks for customers and businesses. 
Along with mastering the designated skillsets of their roles, employers should properly educate employees on AI platforms, their capabilities, and risks and put the proper guidelines in place to determine how employees incorporate AI into their workflows. Going further, employees should be wary of generative AI and create teams to review and fact-check content before sharing.
See More : Multimodality: A Must for Effective Human-AI Collaboration
Financial institutions leveraging generative AI for business operations must also be conscious of outdated information to avoid catastrophic repercussions for customers and the economy. 
ChatGPT (3.5) is only updated through 2021, which removes over a year of data from consideration when generating content. Imagine a bank is staying ahead of the curve, optimizing its efforts by working with an AI toolset like ChatGPT to improve business outcomes. In this instance, the tool used to get ahead of the competition doesn’t know about the war in Ukraine, the recent interest rate hikes, or the Silicon Valley Bank collapse.
This lack of timely knowledge about current events removes essentially any authority from the content produced by generative AI to make predictive decisions. As a result, teams should use AI tools to supplement their findings and content rather than driving it entirely. 
Businesses can combat these challenges by ensuring their data is accurate, accessible, and integrated. One strategy is investing in data governance and analytics capabilities that enable financial institutions to easily access and analyze accurate, up-to-date information.
Bias in AI is a significant concern. AI systems can generate unfair decisions since large language models rely on human-generated content containing human biases and prejudices. This bias raises tremendous ethical issues about using generative AI technologies in financial services, especially in the areas of customer data analysis and decision-making. 
ChatGPT searches for textual content relating to the question and recombines it for a distillation. Through this content generation, the majority opinion will dominate, pushing a narrative that may only reflect the loudest voices. 
For financial institutions, this bias can lead to unfair outcomes for customers. A biased AI should not be the sole source of information for customer needs, such as personalized investment advice or customized insurance policies, as it could insert its trained bias into decision-making. 
While generative AI can help financial businesses provide more tailored and effective services to their customers, the current ethical challenges create barriers to widespread use. With the rapid development of AI, companies can also expect an increase in policies to address potential bias in AI systems. Legal and auditory frameworks for artificial intelligence will continue evolving through private-sector work and legislative protections.
This bias should be positioned as a systematic error. Financial services must build a safety net to ensure their use of generative AI remains ethical. An AI bias audit can establish quantifiable metrics to create a framework for ethical and trustworthy AI deployments, ensuring data integrity, accuracy and a lack of bias in the insights that inform strategic decisions. 
See More: How ChatGPT Could Spread Disinformation Via Fake Reviews
Business leaders should use current generative AI models as a stepping stone to help reinvent the way work is done rather than replace the necessary and important work of humans. By focusing on which tasks can be automated or transformed through AI assistance, businesses can achieve a significant efficiency advantage compared to hesitant competitors.
Financial businesses interested in taking the next step toward AI adoption can look toward digital transformation initiatives to integrate multiple forms of AI as the next level of device management. Banks and insurance companies should begin imagining other use cases in IT development, marketing and sales, and HR while carefully examining their potential applications before full integration.
While generative AI technologies offer significant benefits to the financial industry, including increased efficiency and automation of routine processes, financial institutions must approach these tools cautiously. Verification measures, accurate and updated data, and ethical considerations must be considered to mitigate risks and maximize benefits.
What measures are you taking to avoid the common pitfalls and risks in leveraging generative AI? Share with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . We’d love to hear from you!
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Chief Product Officer, Software AG
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