How data fuels banking’s digital transformation

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. From the advent of the commercial internet on, technological advances have made our day-to-day lives more virtual and global.

Smart, always-connected devices and anytime-anywhere interactions are now a given, particularly among Millennials, who 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 the digital persona of customers to develop new offerings, guide them to relevant and enabling information, improve service and anticipate future customer needs.

An example is JPMorgan Chase & Co, which 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 everyday spending from 2014 to 2015, a valuable insight for shaping financial services strategies and offerings.

Companies large and small can gain distinct advantage by using analytics to look at the same data as their competitors do, in order to uncover new patterns and insights.

They can then develop differentiating business propositions that lead to disruptive products and services. Consumers can also get into the insights game by selling their anonymous data through Datacoup, which analyses it and provides the insights to companies.

The power of data

Data provides not only customer intelligence but also insights into the workforce. Credit Suisse Group is among the companies that are analysing factors such as job tenure, performance reviews and communication patterns to identify employees with a high likelihood of leaving for another job.

Across industries, organisations have always collected and stored data on customers, suppliers, products and services.

Today, traditional enterprise and market data is being combined with big data (i.e. interactional data) and third-party supplied data that, for example, adds demographic and geospatial inputs.

In addition, an emerging trend is the push for ‘fast’ data – making the most 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.

Like Facebook and Google, 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 point the way to decisions and actions in various situations, such as pitching a certain product to a customer at a certain time in a certain situation.

Financial services: a case study

A pioneering global financial services company harnesses 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, the social web, 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.

Armed with this information, the institution can work proactively to offer the right financial product at the right time in the right place, and also reinforce customer loyalty by delivering a meaningful experience.

Taking a ‘next best action’ – presenting a compelling offering even if the customer hasn’t asked for it – can lead to additional upsell and cross-sell opportunities that fuel greater wallet and market share.

In addition to uncovering what customers want, data and analytics can help determine whether a financial services provider wants an individual or organisation as a customer.

Lending Club, for example, looks at diverse factors when it assesses whether a customer is risk-worthy, including how fast and at what time of day loan seekers fill out an online application and the makeup of their social media friends list.

Data’s ‘dark side’

When fraud occurs, a bank can expect to put considerable effort and resources into bringing the perpetrators to justice and recovering the funds, sometimes to no avail.

Predicting and preventing fraud, rather than acting after it happens, can potentially mean big savings for a bank. Leveraging the power of data and using the right tools, analytics and algorithms, banks can do just that in some situations.

HSBC has improved fraud detection, false positive rates and fraud case handling by monitoring the use of millions of cards in the US.

Fraud can be attempted from both inside and outside the institution. To address the internal threats, a bank can establish data and algorithmic mechanisms to monitor employees.

Capture the power of data

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. Such a function could be filled by a team of behavioural scientists who can help convert raw data to information, insights and foresights that inform next best action activities and strategies.

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

This powerful advantage can help create an institution for the future by leveraging data’s power through the application of smart algorithms and predictive analytics. This will enable a bank to quickly and effectively transition from just ‘doing digital’ to being a digital organisation.

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