The Evolution of Customer Personalization in Finance Through AI

The finance industry is undergoing a profound transformation in its approach to customer personalization. At the forefront of this revolution is the integration of artificial intelligence (AI) technologies, enabling finance companies to deliver tailored experiences that resonate with individual needs and preferences.

Finance companies are increasingly harnessing the power of AI to personalize the customer experience across various touchpoints. By analyzing vast amounts of data, including transaction history, browsing behavior, and demographic information, AI algorithms can generate insights into individual preferences, financial goals, and risk tolerance levels. This allows companies to deliver targeted recommendations, personalized product offerings, and customized communication channels, thereby enhancing customer engagement and satisfaction.

Nonetheless, AI is not new to the sector, affirms Ximena Salgado, Head of Product, Nu. Salgado explains how Nu has leveraged this technology to gain competitiveness in a sector that used to be very traditional and hard to access. She explains that Nu has used AI to accelerate the process of customer learning to help them access the market. “AI has been used for decades, it helped greatly at the very beginning when we were not known to cater to a wide variety of clients through AI models that allowed for credit granting.”

Experts agree on AI uses to customize their product and services offering to clients. To fully embrace AI capabilities, Louis Zaltzman, Chief Growth Officer, RappiCard, recommends seeing AI as a whole, integral system and deeply understand what are the problems it seeks to solve. Zaltzman explains that AI can help to adapt to each client. “Before deploying these systems we must truly understand the whole journey you need it to solve. It is not solely about having the best data set or having the most advanced process, but rather employing AI to solve problems while adding value.”

However, leveraging AI for personalization comes with its own set of challenges. Ensuring data privacy and security, maintaining algorithm transparency and fairness, and addressing ethical concerns are paramount considerations for finance companies. Moreover, integrating AI into existing systems and processes requires significant investment in technology infrastructure, talent acquisition, and ongoing training, posing implementation challenges for many organizations.

Joaquín Domínguez, Chief Credit Officer, Ualá, explains how AI can be used to push for the right products for the right clients, while Zaltzman concurs adding that this personalization also leads to greater product use and lower costs, as strategies have the best outcome possible while reducing efforts. Domínguez explains that Ualá uses AI models to determine product attractiveness for both active and inactive clients. Based on past transactional behavior, it can also understand whether a client is inclined toward investment products or purely transactional ones, allowing the company to focus marketing strategies accordingly.

In the credit domain, companies can deploy different strategies like with scoring, which involves analyzing transactional, credit bureau, and onboarding information to create a model that predicts the likelihood of a client taking up a credit product and understanding their delinquency level. This allows us to efficiently contact clients with a high likelihood of default and avoid bothering those who are only a day or two late, says Domínguez.

AI plays a central role in fintech strategies to personalize the customer experience. Through the deployment of advanced machine learning algorithms and predictive analytics, companies can analyze customer data to gain deep insights into their behavior, preferences, and needs. These insights inform the development of personalized product recommendations, targeted marketing campaigns, and proactive customer support initiatives, aimed at enhancing satisfaction and loyalty. From a unique perspective, Santiago Benvenuto, Senior Director of Digital Wallet, Walmart, emphasizes the significant advantage Walmart's Cash has due to its affiliation with Walmart as a retailer.

However, AI cannot be discussed without first understanding the needs for financial inclusion and finding strategies that make sense with the company’s operations. “Five million people enter our stores daily, with a significant portion paying in cash. Financial exclusion often stems from a lack of information within the system, hindering access to AI-driven scoring models and other tools. Beyond enhancing the shopping experience, in Mexico, we have a great opportunity to create records for individuals without a digital footprint, enabling risk scoring and improving retail offerings for better financial inclusion. Given the large number of Mexicans we serve, we play a vital role in supplying transactional information to these models, impacting not only those who are already banked,” says Benvenuto.

Ensuring data quality and accuracy is crucial to the effectiveness of AI algorithms. Finance companies must invest in robust data governance frameworks, data cleansing processes, and data validation techniques to maintain the integrity of their datasets. Moreover, navigating regulatory compliance requirements, particularly concerning data privacy and security, requires careful attention and adherence to evolving regulations. Additionally, addressing the scalability and interoperability of AI systems across different platforms and channels is essential for ensuring seamless integration and consistent user experiences.

Domínguez explains that three main points are critical for processing vast amounts of data: the quantity of data points to strengthen the models does not necessarily mean they are all important, centralizing the data source is crucial, and having a technologically scalable architecture is essential. “Constantly changing the technological architecture to accommodate a growing number of clients is necessary.” Salgado adds that data quality is as much if not more important than quantity, especially when thinking of data privacy and cybersecurity. Another focal point is recognizing AI’s shortcomings regarding biases. “Talking about the gender gap, for instance, we need to actively intervene and drive inclusion. AI has helped democratize the process, but close monitoring remains important,” she says.

As finance companies continue to embrace AI-driven personalization, the ability to deliver tailored experiences at scale will become a critical differentiator in an increasingly competitive market landscape. By leveraging AI technologies effectively, addressing key challenges, and measuring the impact on business outcomes, finance companies can unlock new opportunities for growth, innovation, and customer satisfaction in the evolving digital economy.