Unlocking deeper customer insights with AI

AI goes beyond basic customer service by using advanced analytics to truly understand a customer’s unique financial situation. Here’s how: 

• Data analysis: AI can analyze each customer’s transaction history, financial goals, and banking habits to understand their unique financial profile. This allows banks to move beyond a generic approach and tailor their offerings to each customer’s specific needs. 
• Predictive analytics: AI can predict a customer’s future financial needs based on their past behavior and market trends. Imagine receiving proactive alerts when you’re approaching a low balance, or being pre-approved for a loan you might actually need. 
• Real-time advice: AI-powered virtual assistants can answer your questions and provide real-time guidance on managing your finances. Imagine a virtual assistant suggesting ways to save money on upcoming bills, or recommending budgeting tools based on your spending habits. 

Now that we’ve explored how AI sheds light on customer needs, let’s see how you can leverage this knowledge to craft some competition-busting hyper-personalized experiences. 

Using AI to craft hyper-personalized services

Keep in mind that hyper-personalization isn’t just offering personalized product recommendations; it’s about understanding each customer’s needs, preferences, and behaviors in great (really great) detail. This level of personalization is done via advanced AI algorithms that sift through massive amounts of data in real time to identify patterns and trends that are unique to each customer. 

Let’s say a customer travels internationally a lot for work. AI algorithms can spot this pattern from transaction data and suggest travel-friendly banking services, such as fee-free international transactions or travel insurance recommendations. It can also give retirement planning advice based on a customer’s financial history and goals if they’re nearing retirement age. 

AI-powered chatbots are also key to delivering hyper-personalized 24/7 customer service. These virtual assistants can understand natural language queries and provide relevant answers in real time, including providing: 

• Personalized product recommendations: AI can recommend personalized financial products tailored to a customer’s specific goals. Saving for a home? AI can suggest high-yield savings accounts. Want to invest? AI can recommend investment options based on an individual’s risk tolerance. 
• Proactive support: AI can anticipate potential problems and proactively offer solutions. Is a customer running low on cash before payday? AI can suggest a temporary line of credit or connect them with budgeting resources. 
• Frictionless banking: AI chatbots can handle routine tasks like bill payments and transfers, freeing customers up for more complex interactions with human advisors if and when needed. 

Chatbots can also use machine learning to get better and better at what they do, based on what customers say. Basically, the help they provide gets more personalized and accurate over time. This process of learning from experience helps chatbots to understand what customers need and to give them help before they even know they need it, improving the customer experience. 

That’s not to say it’s all smooth sailing. 

While chatbots are great for efficiency and accessibility, it’s important to strike the right balance between automated assistance and human interaction. There will always be times when customers need the expertise and empathy of a human advisor, especially when making complex financial decisions or resolving sensitive issues. 

Hyper-personalization in action

Moving beyond chatbots, AI also delivers hyper-personalized experiences via (quite a lot of things): 

• Targeted marketing: AI can analyze customer data to deliver targeted marketing campaigns with relevant financial products and services. 
• Personalized content: Banking apps and websites can use AI to display personalized content and features based on a customer’s financial profile and needs. 
• Risk-based authentication: AI can personalize security measures based on individual risk profiles, streamlining the login process for trusted users while adding extra verification for high-risk transactions. 
• Dynamic pricing and offers: AI can enable dynamic pricing strategies and personalized offers based on individual customer behavior and market conditions. For example, banks can use AI to adjust interest rates, fees, or promotional offers in real time to better match each customer’s needs and circumstances. 
• Behavioral biometrics: AI-powered systems can analyze behavioral biometrics, such as typing patterns, mouse movements, and voice characteristics, to enhance security and personalize user experiences. By continuously monitoring and analyzing these behavioral patterns, banks can more accurately detect suspicious activity while providing frictionless authentication for legitimate users. 
• Predictive financial planning: AI can help customers with long-term financial planning by using predictive analytics to forecast future expenses, income, and investment returns. This enables banks to offer personalized financial advice and recommendations to help customers achieve their financial goals more effectively. 
• Contextual insights and recommendations: AI can provide contextual insights and recommendations based on factors such as location, time, and current events. For example, if that traveling customer is on the road again, AI could provide tailored suggestions for managing their finances overseas, such as currency exchange options or local banking services. 

AI-driven hyper-personalization also brings a number of new solutions. One is the use of AI algorithms to create personalized test banks for customers. These test banks simulate real-world financial scenarios that are tailored to individual preferences and goals. This allows customers to explore different banking products and services in a risk-free virtual environment. By interacting with their personalized test bank, customers can get valuable insights into their financial options and make more informed decisions about their banking needs. 

Cool idea. 

Now that you know how AI can help create hyper-personalized experiences, here are some steps you can take to use AI to build a data-driven foundation, understand customer needs, and design user-centric solutions that deliver a truly outstanding experience. 

Data is King (Queen, and everything else in-between)

• Data collection and security: 
  • Implement robust data collection methods to gain a comprehensive understanding of customer demographics, financial goals, preferences, and behaviors. This can include collecting data from transactions, account activity, surveys, and customer service interactions. 
  • Ensure the highest standards of data security and privacy to build customer trust. Be transparent about how data is collected, used, and protected. 
• Data infrastructure and analytics: 
  • Invest in a strong data infrastructure that can efficiently handle large volumes of customer data. 

• • Build robust data analytics capabilities to extract meaningful insights from the data you collect. Use tools such as machine learning and AI to identify patterns and trends that can be used for personalization. 

Focus on customer-centric design:

• Understand customer needs: Conduct user research, such as crowd surveys, to fully know customers’ pain points, financial goals, and preferred communication channels. This will help tailor AI solutions to address their specific needs. 
• Create a seamless user experience: 
  • Design AI-powered features that integrate seamlessly with the existing banking experience. 
  • Ensure that these features are easy-to-use, intuitive, and provide clear value to the customer. 

And don’t skip the testing:

• Usability Testing 
  • Conduct regular usability testing with real users to ensure AI-powered features are user-friendly, intuitive, and meet customer needs effectively. This will help identify any potential friction points or areas for improvement before a wider rollout. 
• Payment Testing 
  • Integrate robust end-to-end payment testing into the development process to ensure secure and frictionless transactions, even when personalized features are involved. This testing should cover various payment scenarios and potential vulnerabilities.

And for added measure, use the power of crowdtesting to test for AI bias. Because AI models are trained on data, it’s crucial to test for potential biases that could lead to unfair or discriminatory outcomes for certain customer groups. By using real people to provide real insights, you can regularly assess and mitigate any identified biases to ensure fair and ethical AI implementation. 

The future of banking is here, so make sure you’re ready!

Everything is quickly becoming mobile, online, contactless, and hyper-personalized. For anyone developing BFSI solutions, integrating all of this into a single frictionless solution isn’t just essential, it’s imperative. AI is the key to making this happen. 

Just look at the agendas of many of today’s fintech summits and conferences, from Money20/20 and dozens more. 

It’s clear that AI-powered solutions are creating a vital competitive advantage. Especially when it comes to that all-important element – the customer experience. 

If you’d like to read more about how AI is transforming the BFSI and fintech industries (beyond hyper-personalization), stay tuned for the next article in our Fintech Frontiers series!