In an era where technological prowess reflects market leadership, those in the financial services industry are embracing an innovative marvel — generative AI. This technology is rapidly demonstrating its transformative potential for both businesses and customers. It’s important to recognize that while novel and nascent, generative AI is technology that is cousin to some of the algorithmic and predictive technology already present within the financial services industry. Things like fraud detection, portfolio optimization, and personalized banking services are all present and utilized inside the tech stack of your selected bank, credit union, and/or wealth management provider. Other examples include search, chatbots, and your everyday commerce experience.
Dive in below to understand how generative AI is reshaping financial service marketing (many others too!) and why it’s crucial to harness its power responsibly. The conversation is changing RAPIDLY. Focusing on embracing these tools and building the skills to power productivity enhancement using generative AI in financial services is going to drive the next edge of competitive advantage – especially for small teams.
The Intricacies of Generative AI in Financial Services
At its core, generative AI systems are designed to construct content, visualize designs, and strategize autonomously, all by learning from a vast sea of data. Whether it’s creating compelling ad copy or devising customer-centric solutions, leveraging generative AI in financial services is a formidable ally for marketers. Options for utilizing these tools range from free usage that requires prompt engineering in a chat-like structure for tools such as ChatGPT and Bard to licensed products that provide some insulation and interface between you and the model or technology itself – tools like Yext, Copy.ai, and Kentico.
Amplifying Customer Engagement
The modern consumer craves personalization. Generative AI serves this on a silver platter, allowing financial institutions to tailor their interactions, ensuring each customer feels uniquely valued. Instead of generic notifications, imagine banking updates meticulously crafted to match your financial trajectory. Such acute personalization not only builds trust but also paves the way for sustained loyalty. One of the largest blockers to creating personalized content is the sheer volume of content required to speak to each of your organization’s personas in a meaningful way. With generative AI, we can remove these blockers by generating content en masse in a manner that controls tone, output, character count, and brand representation.
The power of this engagement can be measured both by the anticipated outcomes from users and also by the velocity by which your teams are generating content to support already established marketing goals.
The Power of Proactive Personalization
Generative AI's true prowess lies in its predictive capabilities. Financial service providers can now anticipate future customer needs, fostering a proactive rather than reactive approach. By offering tailored products before a customer even realizes they need them, businesses amplify their value proposition by speaking to their constituents in a manner they expect to be spoken to.
The best part is that this predictive technology is already utilized in so many marketing ecosystems. Take Google Analytics (GA), for example. With the recent shift to GA4, we see their measurements lean into what they have historically referred to as ‘Insights.’ This now includes insights, anomaly detection, predictive metrics, and predictive audiences.
Supercharging Marketing Productivity
Generative AI is a productivity catalyst. For marketers overwhelmed with content creation, AI comes as a redeeming feature. By automating content generation and design tasks, it allows marketing professionals to create content en masse, a previously difficult endeavor vital for hyper-personalized marketing campaigns. Moreover, it alleviates colloquial workplace issues like writers block, letting marketers channel their creativity towards strategy and innovation instead of tedious content tasks. Additionally, we’ll see this technology evolve to account for ‘multi-modalities’ that extend beyond just copywriting and into the creation of video, audio, social, and so many more styles of content. According to one study, productivity is expected to increase by up to 66% across various disciplines such as customer service agents and software engineers.
Generative AI and Risk Management in Financial Services
Risk management is another key area where AI can make a significant impact in the financial services industry. AI can be used to identify patterns and trends in data that may potentially suggest non-compliance. For instance, leveraging the technology to analyze mass amounts of transactional data to determine specific patterns or anomalies that may suggest possible violations of regulations and alert teams that an investigation may be necessary. If done correctly, this process would be a meaningful step in transforming the reactive nature of risk management in financial services.
Generative AI and Compliance: Navigating Regulatory Landscapes
It's probably safe to say that, historically, compliance professionals have been apprehensive to adapt to technological innovations. Their apprehension is totally fair, though, as in the financial services industry, there is often little room for error. However, with the advancements of generative AI, industry leaders are increasingly noticing the shortcomings of manual operation and have started to experiment with the technology to try to improve compliance processes. One opportunity is through the use of natural language processing (NLP) to analyze and understand complex regulations and compliance requirements. By leveraging NLP, generative AI is able to scan large volumes of regulatory texts, comprehend their meaning, and pinpoint any areas that need compliance attention. This process has the potential to save professionals an immense amount of time and resources by eliminating the need to read and interpret the lengthy documents themselves.
Navigating Challenges with Generative AI in Financial Services
Yet, as with all potent tools, generative AI presents its challenges. Ethical dilemmas around data privacy, potential misuse, and over-personalization abound. There is also the specter of AI misreading data, potentially leading to misguided recommendations. These so-called ‘hallucinations’ anchor the importance of human intervention when utilizing AI generated content. Likewise, these models are using trillions of data points to meet the requested requirements of a user or product when generating content. Conversely, humans use emotion and intellect to refine such content for tailor-made experiences that no amount of data can recreate.
This is never more apparent than when navigating industry standards. The realm of financial services is governed by ironclad compliance norms. Merging AI into this matrix necessitates robust security protocols, ensuring customer data remains inviolable. In practice, a marketer needs to be extremely cognizant of what is fed into these models to generate content using artificial intelligence. In the US alone, we see significant legal pressure that will determine precedents for things like an AI bill of rights, copyright owners, workers’ rights, and so much that we have not yet explored. Starting small with these tools allows for the flexibility to capitalize on the productivity enhancement without the constraints of a shifting sea of regulations.
A small anecdote as a side bar, but I used ChatGPT to build a princess and companions bedtime story for my young daughter. The focus was meant to be the intersection of adventure and friendship! Each night, I would add prompts into the tool to build the next chapter of the journey that was being built for these unique characters that were evolving each night as I put my daughter to bed. After about a week, the tool started to forget some of the companions that had joined the princess on her quest. It began to mis-remember details that my daughter was quick to point out as I was reading her this story. She made a bit of a game about it but if the tooling does not yet have the comprehension to track a princess and her companions, why would you want to risk your brand by using these tools without direct human intervention?
- Wes McChristian, Global Kentico Practice Director at Americaneagle.com.
Selecting the Right Generative AI Product
Venturing into generative AI demands diligence in selecting a reliable product. Not all AI solutions are crafted equal. Opt for products renowned for their safety, reliability, and accuracy to avoid pitfalls and ensure a seamless integration.
For instance, Kentico's AIRA (Artificial Intelligence Recommendations and Assistance) takes many of the points above and wraps it up in a neat little package inside your already established content/experience management solution. AIRA builds upon historic capabilities that allow for the automated generation of alternative text on images (for instance). Think of it as that multitasking friend who always has your back, ensuring your digital marketing strategies are both fun and effective. The tool helps with utilizing your content with a personalized touch, effortless campaign creation, with a trustworthy and reliable editing experience.
Real-World Applications of Generative AI in Finance
While generative AI in finance still has a long way to go, there are numerous examples of its capabilities in streamlining processes and producing positive outcomes.
Take Wells Fargo, for example. The financial services stalwart utilizes an AI-powered banking feature within its mobile app that provides personalized account insights and delivers tailored services based on the customers' data. It's as simple as users tapping a blue light bulb icon located within their account information screen to access more than 50 prompts based on previous and expected activity. This feature is a great example of how generative AI is being leveraged within the finance industry to provide customers with more personalized experiences.
Another great example of AI in finance is Aladdin, an electronic system built by BlackRock Solutions, the risk management division of the investment management corporation BlackRock, Inc. Aladdin utilizes machine learning and AI algorithms to continuously analyze massive amounts of financial data, identify risks and opportunities, and provide investment managers with real-time market insights. This type of technology is a significant stride as it empowers financial institutions to deliver better services and outcomes, while also improving the customer experience.
The Imperative to Innovate
Differentiation is paramount; early adopters of generative AI in financial services can expect dividends in customer satisfaction, loyalty, and revenue.
In total, generative AI is an exciting tool. While it must be wielded judiciously, it promises unprecedented competitive advantage. Marketers should explore this revolution while maintaining their brand standard and compliance requirements. The next chapter in financial service marketing is unfolding.
Curious how generative AI fits in with your overall marketing strategy? Americaneagle.com offers artificial intelligence and consulting services that can help you understand generative AI's potential. Contact us today to kickstart the conversation.
