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How Banks Are Capitalizing on a New Wave of Big Data and Analytics - SPONSOR CONTENT FROM COGNIZANT

Digital is reconfiguring the world. Smart, always-connected devices and anytime/anywhere interactions are now givens, particularly among millennials, who expect such conveniences in banking and financial services.

Data underpins digital’s disruptive promise. Combined with predictive Analytics, hardware, and connectivity, data opens the door to breakthroughs such as Code Halo™ thinking. Code Halos are the information that surrounds people, organizations, and devices and are today’s digital fuel. Every digital click, swipe, “like,” buy, comment, and search produces a unique virtual identity—something we call a Code Halo. While Code Halos are important to each of us, they are becoming increasingly vital to the success of every business, as they help create unprecedented business performance.

When JPMorgan Chase & Co. analyzed 12.4 billion debit and credit card transactions, its research revealed a dramatic slowdown in the growth of everyday consumer spending from 2014 to 2015. That data shaped the company’s future strategies and offerings.

Companies can use analytics to find new patterns and insights in the same data their competitors are seeing. Consumers can even sell their own data anonymously through technologies like Datacoup, which analyzes it and provides the results to companies.

Read more from Cognizant:
Data also provides a deeper look into a company’s workforce. Credit Suisse Group and Walmart Stores, Inc., are among the companies that analyze factors such as job tenure, performance reviews, and communication patterns to identify employees who are highly likely to leave.

Organizations have always collected data on customers, suppliers, products, and services. Now that traditional information can be combined with big data (i.e., interactional data) and third-party data that, for example, add demographic and geographic details. Some companies are also pushing for “fast” data—information in real time at the point of engagement.

Uncovering data patterns sets the stage for conducting predictive analytics. Like Facebook and Google do, financial institutions can tap the imagination of millions of people, creating contextualized and personalized experiences.

When a large global bank built a model to predict customer interest in savings-related offerings and cross-selling, the model pilot produced a tenfold increase in branch sales and 200 percent growth in conversion rates over a two-month period.

A pioneering global financial services company created personas for its customers based on anonymous transactional data that showed their buying habits and interests. The company feeds the information into prediction models, then pairs the results with geolocation and mobile data to create merchant-funded personalized offers to customers.

Within data privacy limitations, an institution can comb the Web, social media, apps, and other online and offline 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 who could be in the home or auto market in the next 30 to 90 days. Taking a “next best action”—presenting a compelling offering even if the customer hasn’t asked for it—can lead to not just customer loyalty but also selling opportunities.

Data and analytics can also help determine whether a financial services provider wants an individual or organization as a customer. Lending Club, for example, decides whether customers are risk-worthy in part by noting how fast they fill out an online application, what time of day they fill it out, and the makeup of their social media friends list.

Predicting and preventing fraud—rather than acting after it happens—can potentially mean big savings for a bank. HSBC has improved fraud detection, false-positive rates, and fraud case handling by using analytics to monitor the use of millions of cards in the United States. A bank can also protect against internal threats by using data and algorithms to monitor employees’ on-the-job activities.

In short, banks have several ways to capitalize on the wealth of data available to them:

  • Support high-impact initiatives that drive change, break down silos, create more information sharing, and build efficiency.
  • Invest in smart algorithms, predictive analytics, and advanced tools to enhance data management and make better decisions.
  • Explore organizational redesign and new operating models, including creating a chief data officer function or strengthening an existing position.
  • Commit to creating a data scientist function and strengthening the organization’s data science proficiency. Such a function could be filled by a team of behavioral scientists who can help convert raw data to information, insights, and foresights.

Banks are uniquely positioned to apply Code Halo thinking because they own data on enormous numbers of transactions and can track their customers’ money movements. By leveraging data’s power, a bank can transition from just “doing digital” to being a digital organization.

Adapted from Cognizant’s “How Data Fuels Banking’s Digital Transformation.” To learn more about how Cognizant is helping banks lead the way with digital, click here.

This post first appeared on 5 Basic Needs Of Virtual Workforces, please read the originial post: here

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How Banks Are Capitalizing on a New Wave of Big Data and Analytics - SPONSOR CONTENT FROM COGNIZANT


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