August 03, 2023
Trust matters in financial services. Trust builds strong customer relationships, unlocking increased lifetime value, brand loyalty and product adoption. Ultimately, trust enables greater access to customer data—the most valuable currency of all.
Will AI enhance or destroy the value of this currency? A recent Salesforce survey found that 88% of customers believe trust becomes more important during periods of significant change. We’re currently experiencing one of those transformative periods. AI is poised to revolutionize every stage of the customer experience — and the very processes we use to create those experiences.
A solid foundation of trusted and accessible data is critical to harnessing the full potential of AI. When financial services companies lack this foundation, they risk developing features or experiences that aren't data-driven and fail to meet customer expectations. These products can hurt brand trust and customer loyalty while potentially driving up customer acquisition costs.
Developing meaningful products with AI requires a strong knowledge of the technology and what you’re hoping to achieve with it, as well as a clear process for execution.
Focus on customer outcomes
Generative AI has opened up huge opportunities to build intuitive, frictionless customer experiences, and financial services is one of the industries that’s leading the way.
Leading banks are all experimenting with generative AI. Goldman Sachs, JPMorgan Chase and Morgan Stanley have each announced GenAI projects.
By prioritizing customer needs and improving digital experiences, financial institutions can enhance customer satisfaction and loyalty, ultimately impacting the bottom line — as long as those institutions start with a solid understanding of the customer's objectives and aligning AI initiatives with those goals.
GenAI can help identify patterns and uncover hidden correlations from customer feedback, behavior and market trends. While GenAI alone is not creative or empathetic, AI and human collaboration merge the ability to analyze vast datasets and together create new ideas with contextual understanding, creativity and emotional intelligence.
This knowledge and capability create unparalleled opportunities to build better products and services. Combining the insights of artificial and human intelligence enable more in-depth understanding, better design and testing, and more effective products.
Build on insights from your data
Consider what it means for your business to have AI-enabled smart digital experiences.
In financial services, for example, customers are looking for financial guidance, but the information involved is sensitive. Most customers expect the highest levels of security from financial products. Before they entrust their financial data and decisions to AI, personal trust must be established — especially if they feel contact with a human is complex or difficult.
For an AI-powered tool to truly shine, it should provide simple, meaningful results or services that are valuable enough to replace manual processes. American Express, for instance, introduced Amex Trip Planner last year, which can create an “all-encompassing digital itinerary” for travel based on customer preferences and interests. Cardmembers can filter results and see automatic recommendations.
For banks, GenAI can help deliver better service and new solutions to customers, but security and trust are again primary issues. Reaching human support easily and immediately is still important for consumers. GenAI shouldn’t replace human contact but instead act as a tool to optimize customer support experiences.
Humans must also define the strategy for what products to build. “AI isn’t a one-size-fits-all solution,” says Valtech Data Strategist Heather Ryan.
“Meaningful business transformation will come from knowing when to leverage AI, and when not to. Embedding experimentation into how you release products will ensure that you don’t spend time on things that won’t work and foster the environment to innovate, change direction, and deliver something with real value.”
Businesses should also use data intelligence to inform product development. The main challenge, Ryan notes, is that many products are dictated only by business needs rather than a blended view of business and customer needs.
Synthetic data can help solve this. Synthetic data is the creation of datasets that mimic real-world data and can be used for testing new products while fully protecting user privacy. This can overcome product testing challenges in financial services, where creating test environments with access to complex production data is often difficult and increases the potential for malicious attacks.
Synthetic data allows companies to generate realistic copies of real-time data for extensive testing. However, traditional methods of creating synthetic data as a mirror of real-world data have security vulnerabilities that can lead back to the original data source. Without explicit remediation, there is also a real risk of creating unintended bias or replicating bias present in data captured.
By introducing controls, companies can start to address potential bias in generated data.
Conduct secure experiments
Customer data isn’t the only challenge when using AI to develop products. The recent surge in fraud attacks related to GenAI has added another layer of complexity: How do financial services organizations uncover insights without compromising their duty to customer experience and customer data security
While GenAI is in its early days, the eagerness of banks to begin experimentation is encouraging. It shows that traditional banking institutions can successfully navigate the ever-changing fintech landscape and deliver cutting-edge experiences to customers.
Adapting to change begins with understanding it, and the most effective way to leverage any new technology is to figure out how it can improve your processes and results. Once that’s clear, you can start experimenting in a sandbox environment, gathering data on what works and what doesn’t.
Experimenting with AI is all about taking an iterative approach, gaining insights and getting ready for the future.
Companies are weighing the potential risks associated with using third-party AI solutions versus building in-house AI systems to ensure data security. In February 2023, JPMorgan restricted employees from using ChatGPT over compliance concerns and restrictions the bank has on using third-party vendors.
Further, the rise of third-party vendors from popular web platforms creates the risk that users will be interacting with AI without their knowing so — and without their employers knowing so, either. That’s when organizations lose the ability to know where their data is, and where it’s going.
Improve your data hygiene
Before embarking on their AI journey, companies should establish a solid foundation for data management by implementing better data hygiene practices.
A useful starting point: using the same terminology across the organization. This approach requires companies to make sure all departments are using the same terms to describe their technology, processes and innovations. This can be incredibly difficult because many large organizations have been built through acquisitions and have gone through transformations that empower local teams, resulting in a wide range of tools and methodologies.
More complex data hygiene tasks could involve standardizing the application programming interface (API) standards. For example, in some global companies, divisions in different countries may use different API standards for the same data, which prevents data collection and analysis across boundaries and limits the insights that can be gathered.
Manage the hype cycle
Uncertainty surrounds the future of GenAI in financial services.
While the technology certainly has the potential to transform customer experiences in banking and financial services, we mustn’t yet set overly high expectations — as happened with cryptocurrencies and buy now, pay later services.
Generative AI is at a pivotal moment. So is the financial services industry. GenAI could very well change the way banking products are delivered to customers in the future. We could see some truly intelligent and personalized digital experiences emerge.
If you have any questions about AI’s role in the customer experience your financial organization wants to create, please get in touch with our Financial Services team today.
