Tony Virdi discussing customer-centric banking services - Global Banking & Finance Review
Tony Virdi, VP at Cognizant, emphasizes the importance of data in enhancing customer-centric banking services, focusing on personalized experiences for better customer retention.
Banking

USING DATA TO HELP BANKS IMPROVE CUSTOMER CENTRIC SERVICES

Published by Gbaf News

Posted on October 22, 2014

4 min read

· Last updated: November 1, 2023

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Tony Virdi, VP and Head of Banking and Financial Services in the UK & Ireland, Cognizant

Challenges and Strategic Shifts in Banking

Banking and financial services firms are in a period of transition as many face challenges to drive profits and retain customers. As a result, many are increasing their focus on retail and customer-centric banking services to optimise customer experience as well as maximise alternate revenue streams and prevent further customer losses.

Banks can convert the inefficient use of capital tied up in fixed assets and operating expenses into manageable, consumer-centric experiences that contribute to revenue uplift and cost reduction. However, this requires a transition from an account-based view of banking customers to one that knows them as individuals and enhances the customer experience with relevant, convenient and personalised products and services.

Tony Virdi

Harnessing Customer Data and Code Halos

Those companies that are successfully making the shift toward tailored customer services are analysing what we call “Code Halos” – the clouds of data and information that surround every customer, employee, company and product – to differentiate how they service each customer.

In order to remain competitive, banks should identify and use the clouds of data available to them by focusing on the following areas:

Context

Turning Big Data Into Actionable Insights

When using data to improve customer-centric services, it is important to filter noise and create algorithms to pull actionable information and insight. For example, a bank may find that it no longer makes sense to present a customer’s financial data in chronological order, when there is context that enables information to be ranked in order of importance to each customer. If a customer has outlined savings goals, it may be more valuable to present their discretionary spending first and fixed expenses second. With the right filters, banks can use the information they have – about the customer, demographics, and their own services – to build meaningful correlations between a customer’s problems and the products and services they offer. This will help banks understand their customers better and allow them to optimize their customer experience.

Security

Ensuring Data Security and Compliance

Banks need to ensure that the code they store is watertight and seek compliance to strict data regulations that have emerged including the EU’s Data Protection Directive. Organisations that ultimately win will be those that generate, maintain, and compete on trust, allowing customers to opt in or out from sharing code. For example, some insurance companies already demonstrate a clear connection between value and information – the Give-to-Get ratio – by offering a better insurance deal based on actual driving data.

Individualisation

It is useless to have access to a large amount of data if banks do not use it to create an individual experience for each customer. For example, why does a bank, who knows so much about each customer, have to ask a customer’s language preference at every ATM visit? The bank that applies the information they already have about their customers to create more thoughtful engagements will establish a competitive advantage in the marketplace.

Anticipation

Personalizing Banking for Customer Needs

The clouds of data and information that surround every customer can be used by a bank to generate insights that will anticipate the needs of customers. If Amazon can anticipate the kind of products that its customers will want to buy next, imagine the impact a bank can have if it applies that level of insight to its offers to customers. Banks could create algorithms that will allow for analysis of a customer and predict their next need, with a service or offer ready to meet it. This could be a game changing experience.

The banks most likely to succeed in the future and run better are those that recognise the importance of offering customer-centric services and take the steps needed to run differently, meet customer expectations and anticipate their future needs – to create true value improvements to the services offered.

Key Takeaways

  • Banks must shift from an account‑centric to an individualized view of customers using data “Code Halos”.
  • Contextualizing and filtering customer data enables more meaningful, relevant experiences.
  • Strong data security and trust frameworks are essential when leveraging personal data.
  • Personalization reduces friction—for example, remembering customer preferences eliminates redundant interactions.
  • Predictive analytics lets banks anticipate customer needs and deliver timely, tailored offerings.

References

Frequently Asked Questions

What are “Code Halos”?
“Code Halos” are the digital data clouds generated by customer interactions (clicks, searches, social activity) that reveal preferences and behaviors, enabling banks to personalize services.
Why is data context important in customer‑centric banking?
Context helps filter noise and prioritize insights—for example, highlighting discretionary spending based on a customer’s savings goals rather than chronological transaction listings.
How do banks build trust when using customer data?
By ensuring robust security, complying with data regulations, and allowing customers to opt in or out—thus making privacy a competitive advantage.
What does individualisation mean in this context?
It means using known customer information to tailor each experience—such as remembering language preferences so users aren’t repeatedly asked.
How can banks anticipate customer needs?
By analyzing data halos to detect patterns, then using algorithms to predict and proactively offer relevant products or services before the customer asks.

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