Data and banking's digital transformation

Prasad Chintamaneni and Carlo Lacota
— 1 minute read

Financial services companies can harness their customers' data by using predictive analytics and modelling techniques, write Cognizant's Prasad Chintamaneni and Carlo Lacota.

Prasad Chintamaneni

Data is the crude oil of the digital economy. When refined with predictive analytics and modelling techniques, data can power personalised offers that boost customer loyalty, create upsell and cross-sell opportunities, and generate greater share of wallet.

Digital is reconfiguring the world as smart, always-connected devices and anytime/anywhere communications and interactions are now a given, particularly among millennials. They expect such conveniences in their banking and financial services relationships.

Data underpins digital’s disruptive promise. Combined with predictive analytics, hardware and connectivity, data opens the door to breakthrough insights through code halo thinking, which uses customers’ digital persona to develop new offerings, guide them to relevant and enabling information, improve service and anticipate future needs.

JP Morgan Chase & Co. analysed 12.4 billion debit and credit card transactions to explore factors shaping the growth of local consumer commerce. The research revealed a dramatic slowdown in the growth of consumer every-day spending from 2014 to 2015, a valuable insight for shaping financial services strategies and offerings.

Companies can gain distinct advantage by using analytics to look at the same data competitors do, to uncover new patterns and insights. They can then develop differentiating business propositions that lead to disruptive products and services.

The power of data 

Data also provides customer intelligence and insights into the workforce. Across industries, organisations have always collected and stored data on customers, suppliers, products and services.

Today, such data is being combined with big data (ie, interactional data) and third party-supplied data that, for example, adds demographic and geospatial inputs. An emerging trend is the push for ‘fast’ data ― making pertinent information available in real time at the point of engagement or interaction.

Using smart algorithms, analytical tools and frameworks, businesses can uncover insights from these disparate data sources, providing the basis for action.

When a large global bank built a ‘propensity to save’ model to predict customer interest in savings products and increase cross-selling, the model pilot produced a tenfold increase in branch sales and 200 per cent growth in conversion rate over a two-month period.

Uncovering the meaning in data patterns sets the stage for conducting predictive analytics. The capability to influence behaviour in a way that it becomes predictable or anticipates something before it happens can inform the development and configuration of products and services.

Financial institutions can create constructs that tap the imagination of millions of people, leveraging the power of information, algorithms and technologies to create contextualised and personalised experiences.

Data can help to point the way to decisions and actions, such as pitching a certain product to a customer at a certain time in a certain contextual situation.

A global financial services company harnessed the anonymous transactional data of its charge card customers and developed personas based on their buying habits and interests.

In partnership with merchants, the company feeds the real-time analytics into dynamic predictive models and pairs it with geolocation and mobile data to create merchant-funded personalised offers to customers.

In-market signals can help drive bank strategies too. Within data privacy limitations, an institution can comb the Web, apps and other data sources for information such as who is browsing for a home or car.

Mapping this group to its customer base, the bank can make targeted offers to customers. When fraud occurs, a bank can leverage the power of data and use tools, analytics and algorithms, to tackle fraud. 

Banks can take several actions to capitalise on the wealth of data available to them:

  • Use data to support high-impact, top-down initiatives that drive change, break down silos, create more information-sharing and ultimately evolve to ‘capture once and done.’
  • Enhance data management and decision-making by investing in smart algorithms, predictive analytics and advanced tools, such as speech analytics.
  • Explore organisational redesign and new operating models, including establishing or strengthening the role of the chief data officer.
  • Strengthen the organisation’s data science proficiencies and commit to creating a data scientist function.

Banks are uniquely positioned to apply code halo thinking because they own data on enormous numbers of transactions and track their customers’ money movements.

This powerful advantage can enable a bank to transition quickly from just ‘doing digital’ to being a digital organisation. 

Prasad Chintamaneni is the president of banking and financial services at Cognizant, and Carlo Lacota is the assistant vice president, banking and financial services at Cognizant.


Data and banking's digital transformation
Prasad Chintamaneni
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