Five generative AI use cases for the financial services industry Google Cloud Blog

Companies would need time to gather the requisite experience about the benefits and challenges of each method and find the right balance for AI implementation. To boost the chances of adoption, companies should consider incorporating behavioral science techniques while developing AI tools. Companies could also identify opportunities to integrate AI into varied user life cycle activities. While working on such initiatives, it is important to also assign AI integration targets and collect user feedback proactively. It is also no surprise, given the recognition of strategic importance, that frontrunners are investing in AI more heavily than other segments, while also accelerating their spending at a higher rate. Close to half of the frontrunners surveyed had invested more than US$5 million in AI projects compared to 27 percent of followers and only 15 percent of starters (figure 5).

Indeed, in addition to more qualitative goals, AI solutions are often meant to automate labor-intensive tasks and help improve productivity. Thus, cost saving is definitely a core opportunity for companies setting expectations and measuring results for AI initiatives. While financial institutions are working hard to ensure that these discriminatory practices do not take place, it doesn’t mean bias won’t happen from time to time. To combat this, financial institutions need to revisit their biases and take corrective measures to help mitigate these risks. In addition, the advent of robo-advisors further catalyzed this shift by employing algorithms to create tailored investment profiles based on risk assessments and financial objectives. This innovation significantly slashed costs compared to traditional financial advisory services, making investment avenues accessible to a broader spectrum of individuals.

While 55 percent of FS business leaders said they believe AI adoption is moving at the right speed, 37 percent said it is moving faster than it should. In addition, 85 percent of those surveyed said they wish their business would adopt AI technology more aggressively. Kagoo said organizations that approach AI from just a defensive strategy—to use AI and machine learning to drive cost savings, or to drive operational efficiency — are not yet realizing the returns. AI and blockchain are both used across nearly all industries — but they work especially well together. AI’s ability to rapidly and comprehensively read and correlate data combined with blockchain’s digital recording capabilities allows for more transparency and enhanced security in finance. AI models executed on a blockchain can be used to execute payments or stock trades, resolve disputes or organize large datasets.

Five generative AI use cases for the financial services industry

As a result,accounting professionals can be assigned other responsibilities like providing insights and advice to clients on the data accumulated or auditing or filing taxes, etc. Apart from that, AI tools are cloud-based, due to which computing hardware costs can be toned down to a certain extent. So integrating AI in accounting can effectively assist organizations in cost reduction. Procure-to-pay (P2P), for example, uses natural language processing (NLP) and machine learning (ML) and has shown immediate returns, while order-to-cash and audit analytics show near-term benefits from AI.

• Foundational models, such as Large Language Models (LLMs), are trained on text or language and have a contextual understanding of human language and conversations.
• Namely, TurboTax and QuickBooks are the gold standards in their respective categories, and it would be time consuming and costly for users to switch products.
• Online trading platforms have democratized investment opportunities, empowering individuals to buy and sell securities from the comfort of their homes.
• For the full year, management expects revenue growth ranging from 11% to 12%, and non-GAAP earnings-per-share growth ranging from 12% to 14%.

But investors can expect similar momentum in subsequent years, as Intuit believes it has tapped just 5% of its $300 billion addressable market. Intuit reported strong financial results in the first quarter of fiscal 2024 (ended Oct. 31), beating expectations on the top and bottom lines. Revenue increased 15% to $3 billion, driven by particularly strong growth in consumer group and the small business and self-employed group products.

Three common traits of AI frontrunners in financial services

TQ Tezos aims to ensure that organizations have the tools they need to bring ideas to life across industries like fintech, healthcare and more. Darktrace’s AI, machine learning platform analyzes network data and creates probability-based calculations, detecting suspicious activity before it can cause damage for some of the world’s largest financial firms. Here are a few examples of companies using AI to learn from customers and create a better banking experience.

Benefits of AI in Finance

Furthermore, predictive analytics can augment basic business intelligence (BI) reporting for financial planning. Cyber security breaches (50 percent) and privacy violations (44 percent) were noted as the top greatest potential risks financial services business leaders  face with implementing AI. But most FS business leaders surveyed (93 percent) are confident in AI’s ability to detect fraud, an increase of 8 percentage points compared to last year’s report.

AI in investment and financial services

The decision for financial institutions (FIs) to adopt AI will be accelerated by technological advancement, increased user acceptance, and shifting regulatory frameworks. Banks using AI can streamline tedious processes and vastly improve the customer experience by offering 24/7 access to their accounts and financial advice services. To effectively capitalize on the advantages offered by AI, companies may need to fundamentally reconsider how humans and machines interact within their organizations as well as externally with their value chain partners and customers.

Machine learning typically requires technical experts who can prepare data sets, select the right algorithms, and interpret the output. With the experience of several more AI implementations, frontrunners may have a more realistic grasp on the degree of risks and challenges posed by such technology adoptions. Starters and followers should probably brace accounting guidelines for contingent liabilities themselves and start preparing for encountering such risks and challenges as they scale their AI implementations. Indeed, starters would likely be better served if they are cognizant of the risks identified by frontrunners and followers alike (figure 11) and begin anticipating them at the onset, giving them more time to plan how to mitigate them.

Examples of AI in Finance

Foundational models, such as Large Language Models (LLMs), are trained on text or language and have a contextual understanding of human language and conversations. These capabilities can be particularly helpful in speeding up, automating, scaling, and improving the customer service, marketing, sales, and compliance domains. Eno launched in 2017 and was the first natural language SMS text-based assistant offered by a US bank.

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Platforms such as ContractPodAI and Icertis and specialist AI providers like Corticol.io are embedding AI functions in contract lifecycle management (CLM). “I’m not sure, but it’s not in billions and trillions [of parameters] — maybe millions? Our purpose right now is to have them read documents and summarize them with human in the loop. How much do you need? [is the question].” What do you see as the greatest future threat posed by AI as it approaches general AI status? “We’re just summarizing certain documents right now. It will evolve. It will do a lot more things. But when it comes to banking and banking regulations, we want to be simple, straightforward, and transparent.” “So, for me, nothing really changed. I still go through same rigor as any other banking model. We follow that. We talk to legal and they hand us some principles and compliance rules.”