Reimagining banking with personalization

In the last decade, consumer expectations have drastically changed. The advent of digitalization has put personalization and highly-tailored customer experiences at the core of business success. While banks have been utilizing some sort of personalization strategies for years, in the majority of cases, these efforts are insufficient to help them keep up with increasing customer expectations. Conventional customer segmentation practices based on demographic data, age, and other conventional metrics have grown obsolete. This is why many financial organizations now turn to banking software development. 

First, with the rapid invasion of innovative fintech companies, it became apparent that consumers are increasingly more inclined to switch to flexible service providers that shape their business models around customer experiences. As a result, some of the most profitable revenue sources for banks have been taken away by these nimble digitally native organizations. Second, this challenge has been compounded by the COVID-19 pandemic, forcing both banks and their customers to rely on digital channels more than ever before. 

It may appear surprising that financial institutions have been so reluctant to embark on a digital transformation journey and double down on personalization. However, it’s important to note that banks’ hands have been long tied by the utter inflexibility of their legacy systems, disparate data sources, and increasingly strict data privacy protection laws. 

At the same time, the pressing need for change has reached its peak level, leaving financial institutions with no other choice but to embrace service digitization and personalization. In this article, we are going to discuss the meaning of personalization in the banking industry, the most potent personalization strategies, common barriers on the path to their adoption, and the ways of overcoming them. 

Why banks need to bank on personalization

Personalization can be defined as using customer data, behavioral analytics, and data science practices to define customer expectations and satisfy them. In the banking context, personalization is aimed at tailoring financial service delivery to address specific customer needs in near real-time. 

For one thing, not harnessing the vast possibilities offered by big data, advanced analytics, innovative CX solutions, and other technologies to advance personalization initiatives is a missed business opportunity for banks. Nowadays, customers expect the same level of engagement and personalization that they experience with customer-centric services outside of the banking industry. 

What is more, for decades, price and product quality have been the major competitive differentiator across industries. Business development mostly revolved around introducing new products and adjusting prices to beat the competition. In this new digital age, the ability to provide customers with personalized experiences beyond physical branches and points of sale has become an equally important competitive advantage.  

This way, personalization proves the most effective way for banks to deliver on the ever-changing customer expectations, increase loyalty, and drive revenue growth.

Personalized banking can also play an important role in addressing financial exclusion. According to the recent report from FCA, more than 27 million adults in the UK alone have characteristics of low financial resilience. The government now considers the advent of fintech companies as one of the main instruments for tackling financial exclusion, having confirmed the investment of $5 million to establish the Centre for Finance, Innovation and Technology (CFIT). In this context, the personalization and digitization of banking services is poised to play a significant role in broadening access to financial services. 

Overcoming common personalized banking challenges

Ineffective data governance 

As with many other advanced analytics-based initiatives, personalized banking efforts will fail without a robust data governance structure. Unfortunately, many banks still utilize inflexible legacy systems that can’t provide the necessary data infrastructure. This is why deploying personalized banking systems should be a part of the banking digital transformation, which also implies the modernization of a legacy core system.

Given that CBS modernization is often an expensive, cumbersome, and time-consuming initiative, the best solution would be to build a customer data platform on top of the legacy systems. This way, banks can gather data both from the core system and third-party providers and analyze it in a separate, often cloud-based platform. For banks looking to deploy enterprise-wide personalization strategies, robust data infrastructure should be the top priority. 

In the context of personalized banking, it seems logical that the more data you collect the better. However, many banks struggle to make sense of lavish amounts of data that their customers generate every second. To understand what exact data organizations can use to advance their personalization efforts, it’s paramount to define customer pain points first. This calls for conducting demographic research and applying behavioral science to understand what exact hurdles customers face when trying to satisfy their banking needs.

Insufficient data privacy 

In many ways, personalized banking initiatives are hindered by strict data protection regulations that are growing more stringent. Obviously, no benefits of personalization can be justified if customers’ privacy is compromised, so transparency and consent should become the key secure data management components for banks. 

First, given that many advanced personalized banking solutions imply the usage of AI, it’s important to ensure that these systems are bias-free and explainable. Apart from the fact that AI explainability is also a matter of regulatory compliance, it is also important for banks to understand how the system makes certain decisions. Second, regardless of how sophisticated one’s AI system is, it’s imperative for banks to embrace the human-in-the-loop approach, meaning that any decision suggested by AI has to be approved by an employee. 

Third, it’s paramount for banks to be transparent about how they collect, store, and analyze customer data, and to allow customers to easily withdraw consent for data collection. Data privacy is crucial for retaining customer trust. In the past years, more consumers have become more thoughtful about the personal data they share and less trusting of their financial providers. 

In this case, personalization can become a double-edged sword. If banks don’t pay attention to data privacy, they risk diminishing their customers’ trust even more. This is why it’s imperative for companies not only to ensure that their personalized banking efforts are ethical but to also impress this upon the customers. 

As shown in the table below, the majority of customers are unwilling to share some of their more personal data. This means that banks have to find a balance between their needs for customer data and their customers’ willingness to share it.