Greater personalization driven by Artificial Intelligence (AI) is one of the many new technologies and innovations transforming the banking and financial sector, says Jay Venkateswaran, BFS Head at WNS.
A lack of personalization in their messaging means that banks and financial services companies are still struggling to engage with customers meaningfully. Too often they are, for example, contacting customers with offers of personal loans or mortgages that aren’t even relevant to them while the ones who get calls about student loans have never had one. Simply put, this is annoying and intrusive. According to a survey by Deloitte, less than a third of respondents agree that their bank personalizes their products/services.
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Customer Hyper-Personalization
However, increasingly, AI is driving opportunities for banks to hyper-personalize customer communications. For instance, banks can notify their customers of a decline in mortgage rates, proactively offering them a reduction in interest payments. This is the kind of hyper-personalized interaction that builds loyalty. Customers also like to be updated about their credit scores or advised on managing credit card debt. Similarly, messages can be tailored to meet customers’ savings goals as well as offering advice on managing their spending patterns.
The more tailored the bank’s messages, the more effective its cross-selling and upselling strategies will be and the greater the share of wallet. Used efficiently, AI can be particularly powerful in enabling banks and financial services companies to analyze the increasing amounts of data that they have access to in order to define what is relevant to each customer.
Speaking of emerging technologies, we at WNS are overly excited about the rapid emergence of hyperautomation and its ability to cut costs and deliver personalized customer experience. The most interesting aspect of hyperautomation is not only the individual capabilities but its potential to create a consolidated, correlated approach to using a number of technologies, including AI, Machine Learning (ML), Robotic Process Automation (RPA) and blockchain that we have been experimenting with over the last five to 10 years. A low-code/no code approach makes hyper-automation a seamless process. It helps create apps and solutions that form an intelligent layer over innovative technologies, easily interfacing with all to drive automation. By bringing all of these components together and leveraging them across multiple channels, banks can deliver hyper-personalization and improve customer experience.
The Promise of Blockchain
Looking further ahead, we’re equally excited about the growing potential of blockchain. The financial services sector is best positioned to take advantage of this technology, especially in areas that tend to be more complex and expensive to operate. For example, at the moment when moving money across borders – perhaps to make a purchase, pay a bill or to transfer money to a family member – customers might pay anywhere between 4 to 11 percent commission.
With blockchain, this type of transaction could cost just 1 percent and what currently takes three days to complete can be done in just three seconds. Not only does it require less administration for banks and their customers but it’s more transparent. Remittance tokens can also be incorporated into the transaction flow. This removes cash from the transaction and gives the sender extra control and visibility.
Blockchain also has the capacity to simplify regulatory requirements around Know Your Customer (KYC) processes by storing the client’s details once and eliminating duplication. This information can be stored and then retrieved whenever necessary.
This is just another element of the kind of automation that will truly transform financial services and improve customer experience immeasurably for those banks and financial services firms that are ready to embrace it.
Predictive Analytics and Data-Driven Insights
Among the most promising new technologies reshaping financial services is predictive analytics — the use of AI and machine learning to forecast customer behavior and market trends. By leveraging massive datasets, financial institutions can anticipate customer needs even before they arise. For example, predictive models can flag when a customer is likely to close an account, miss a loan payment, or require new investment advice. This enables proactive engagement, helping banks retain customers and offer timely, relevant financial solutions.
Predictive analytics also extends to risk management. Traditional credit scoring methods often fail to account for nuanced patterns in customer data. With AI-driven predictive tools, banks can assess creditworthiness more accurately by analyzing spending habits, transaction history, and even social or digital behavior. These new technologies not only enhance personalization but also contribute to safer, more stable financial systems.
Conclusion
The convergence of new technologies such as AI, hyperautomation, predictive analytics, and blockchain is creating a new era of intelligent, customer-centered banking. As these innovations mature, they empower financial institutions to transform customer interactions from transactional to truly personalized experiences. Those that embrace these advancements now will not only streamline operations and cut costs but also build deeper, more trusted relationships with their customers. The future of finance belongs to organizations that harness these new technologies to deliver personalization, transparency, and value at every touchpoint.
