The ongoing uncertainty in the economy has put particular strain on financial institutions such as banks and investment firms. Times are challenging especially for those operating in the traditional, brick-and-mortar side of the banking and insurance businesses. But there are digitally-enabled strategies that equip financial institutions to optimize customer relationships and gain traction, even during times of capital tightening. One of them is Generative AI in financial services.
What is Generative AI in financial services?
Generative AI leverages natural language processing models to produce text, images, or certain analyses based on simple prompts. Financial institutions can use GenAI applications for a wide variety of use cases. They include facilitating customer-facing interactions, enabling stronger fraud detection, and automating back-office processes such as reporting or regulatory fulfillment.
None of these applications are theoretical. On the contrary, banks are already applying GenAI for these purposes. JPMorgan Chase, for example, is on record for applying large language models to fraud detection. That is despite early announcements that banks and other financial institutions would be proceeding cautiously with GenAI in financial services.
There is a big difference, however, in the results financial institutions can hope to achieve with different GenAI solutions. Many of the options available on the market today make customer interactions more efficient by automating certain workflow steps. For example, by generating first-draft content for marketing campaigns, or answering customer service queries. Efficiency is not the only—or even the primary—source of value for Generative AI, however. Generative AI can also make financial services engagement more effective through more engaging and personalized interactions.
In the following sections, we explore some opportunities to leverage Generative AI in financial services specifically for better customer-facing activities.
3 marketing and customer service use cases for Generative AI in financial services
The launch of OpenAI’s ChatGPT in November 2022 brought awareness of GenAI to the mainstream. Pioneering financial institutions were already adopting Generative AI point solutions long before that, however. For instance, Persado has worked for years with financial service brands like Ally Bank, JPMorgan Chase, LendingClub, SoFi, Vanguard, and others.
Three concrete customer-facing use cases for Generative AI in financial services are gaining traction. They include self-service enabled by virtual assistants or chatbots; optimized service messages; and personalized marketing messages. Let’s look more closely at each of them.
Use Case #1: GenAI for financial services chatbots
The near-instant popularity of ChatGPT launched a wave of custom virtual assistants and chatbots using foundational large language models. [Note: Virtual assistants often handle a wide range of tasks, including executing transactions, whereas chatbots are often designed to answer service questions—but the distinctions are blurry.] Yet just as banks had been using GenAI for years before 2022, they had also been leveraging virtual assistants and chatbots to supplement service delivery.
Bank of America, for example, offers Erica, a virtual assistant that can execute transactions and flag anomalies. Wells Fargo works with AI firm Kasisto and its proprietary large language model to deliver relevant content to banking customers.
Large language models have accelerated that progress by making it faster and cheaper for financial institutions to train chatbots for specific contexts. The result is improved bots that can handle a wider range of questions. This is a vast improvement over chatbots of old, which could only answer the most basic questions. Financial institutions that go a step further to couple more sophisticated AI-enabled virtual assistants and chatbots with human respondents deliver a better experience.
Use Case #2: Optimized marketing and service messaging
One of the key metrics marketers care about is conversions—did the customer take the action we wanted them to? For customer service, the goal is a bit different, in that service teams want to see first-time resolution. That means the customer gets an answer to their query or a solution to their problem with a single call.
Generative AI can help financial institutions be more efficient with both those goals by generating clear messages that anticipate customer questions or address them when they come up. Websites, contact center IVR systems, and digital channel chatbots can further help when they are trained to discern customer issues clearly and either provide the right answer the first time or route them to a human agent who can.
Providing correct and timely information is not the only factor in optimizing messaging, however. Using the right language to engage and motivate customer action also matters. Among all the Generative AI solutions available for financial institutions, only Persado Motivation AI generates language that taps into motivation. Persado is an enterprise Generative AI solution trained to drive customer action. Our knowledgebase contains insights from over 10+ years of marketing campaigns, fed through our machine learning algorithm to identify what works to drive customer action. It then applies that knowledge when generating new campaign language.
Generative AI adoption case studies by financial institutions
Financial institutions work with Persado to identify the language that will motivate customers to take action. For example, an investment management firm aiming to increase retirement savings account acquisition worked with Persado on a Facebook campaign. Persado generated and tested multiple campaign variants, ultimately identifying one that produced 118% more applications than the human-generated control. In the category of service delivery, Persado-generated campaigns to increase paperless enrollment, to access documentation, or to sign up for automatic payments have also produced significant savings for financial institutions using Generative AI.
Even on-time payment rates can improve with messages generated to optimize customer motivation. For example, an email campaign Persado generated for a credit card customer included a headline and message encouraging on-time credit card payments using optimized language and a more prominent CTA. It produced more than 90% higher engagement than a human-generated version.
Use Case #3: Personalized marketing messages
Marketers across industries have been talking about personalizing customer experiences for decades. Yet there are many limiting factors. One of them is having the ability to scale messaging output enough that the brand can deliver distinct messages based on a customer’s differentiated needs yet also reflect the brand’s values and tone.
Generative AI puts personalized messaging into reach. Informed by customer data, brands can scale their message production to deliver distinct versions to different customer groups based on their needs and preferences.
Persado applies its specialized GenAI, Motivation AI, to bring personalization to another level, tapping into the distinct motivators that drive different customers to take action. For example, a Fortune 50 bank boosted loyalty engagement by as much as 60% using personalized emails encouraging different customer groups—segmented according to their historical use of loyalty points—motivated a larger share to trade in their credits.
What’s next?
Generative AI in financial services is taking hold and delivering results. From more efficient and capable chatbots, to better, more personalized customer experiences across the marketing and service organizations, GenAI solutions are already rapidly transforming how financial institutions engage and motivate customers—and delivering higher conversions and revenue. Let Persado help you tap into this massive opportunity with a no-risk trial.
Share
