The banking sector in the age of Big Data

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The banking sector in the age of Big Data: what if we made consumers want to love their bank again?

There were statistics, then Business Intelligence and now we are in the time of Big Data! Beyond the marketing rhetoric, it appears that French banks are lagging behind in implementing real initiatives to use their data.
However, if there is one sector that has the opportunity to interact very regularly with its customers and produce data, it is the banking sector.

Until a few years ago, the rare exchanges between a banking institution and its clients were limited to visits by the latter to its branch.
But with the democratisation of call centres, online banking and then mobile banking, customers are more than ever connected to their personal banking information and all these interactions represent a mountain of data that has so far been unexploited by banks.

These are still virtually untapped because banks do not necessarily know what to do with them, or are not necessarily equipped to handle such large amounts of data.

However, there are many potential uses: this data can enable them to significantly improve their customer knowledge, to transform or adapt the proposed experience to their customers and, above all, to build customer loyalty by anticipating their needs and offering the right services at the right time and on the right channel.

While customers say they've never looked away so muchThey complain that they have never been so badly advised and that they are offered products that are poorly or not at all adapted to their needs. Perhaps it is finally time for the banking sector to renew its customer relationship with Big Data.

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Big Data, commercial leverage of the multi-channel bank

In concrete terms, the potential applications of Big Data in the banking sector are very numerous. In France, the first thing that generally comes to the mind of banks is to use this data for their credit institutions to obtain segmentations, appetence scorings, acceptance scorings and regulatory indicators, but also to optimise some of their branch processes, particularly the management of their ATM fleet.

Fortunately, this is only the beginning, because we are still taking a very old-fashioned view of the banking profession. They must and can show more ambition!
Big Data should be used by banks to initiate, or at least accelerate their business transformation through :
- the development of customer proximity,
- of improving advice,
- and the reduction of costs, therefore, of the rates charged to customers.

Other growth levers are to be exploited with Big Data, such as developing tailored and differentiating offers for their merchant customers through payment transactions.

In Australia, CommBank has understood this and has had a secure terminal and innovative applications developed for small retailers (florists, bakers, hairdressers, restaurants, etc.): this innovation, which has been deployed since March 2014 in more than 100,000 Australian shops, opens the doors to consumer knowledge. Typical profiles by age, gender, place of residence are revealed as well as the average spending level by category. This is a veritable mine of information that banks can offer to their merchants to enable them to exploit later, by better targeting their actions.

In its report on retail banking in the United States in 2020, Accenture calls for a more personalized bank than ever beforewith "tailor-made" services.

Why not also analyse the frequency of visits to the pages dedicated to products (loans, life insurance, etc.) and offer them spontaneously to the customer at the right time? In the United States, for example, Wells Fargo started his own Big Data Lab to be able to build from scratch a customer sentiment analysis model and try to guess their next needs.

What about analyzing a customer's past behaviour over the last 12 or 24 months to try to predict future behaviour? Still in the United States, Capital One 360 (ex-ING DIRECT) now offers promotions and advantageous conditions to its customers on mobile phones. and via its application based on his purchases over the last few months.

How long will this pile of gold remain unexploited?

By making good use of this "heap of gold" at their disposal, banks have at their fingertips revolutionary dynamic commercial offer systems and the possibility to maximise cross and up-selling more easily.

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These considerations are all the more important when reading the banking sector's market shares, which hardly change from one year to the next: banks must therefore diversify and optimise the number of products per customer. In this battle of wills, Big Data can be a valuable ally, particularly in renewing the revenue model of retail banks.

However, a first obstacle often stands in the way of the democratisation of Big Data in the bank: security issues. But the banking sector is facing the issue of Big Data at a time when the technologies to store and analyze these large volumes are mature, secure and efficient.

Much more sensitive, the issue of data protection and privacy remains a major challenge: the experience of ING in the Netherlands [1][6], accused of wanting to sell her clients' personal data for profit also shows the dangers of getting into this business if you don't control all the details.

There is therefore an opportunity for the most audacious banks, who see this data as a potential source of potential rather than additional risk.

The Big Data to build the bank you've been dreaming of?

Big Data is completely different from traditional data processing analysis solutions. While Business Intelligence analyzes trends using information-rich data, Big Data will cross-reference masses of data for a broader and richer analysis .

Logically, an efficient implementation of Big Data operations in a bank involves four essential steps:
- An efficient data acquisition system, capable of recording and prioritizing all the interactions of a customer with his bank (web, mobile, agency...).
- A coherent infrastructure for managing its data (storage, integration into IS applications, etc.)
- A relevant analytical layer, allowing aggregated and real-time analyses
- A phase of adaptation of the organization and subsequently of the commercial offers and the training of the teams, in order to be more agile and in phase with the current and future needs of the customers.

However, these attractive prospects require in-depth studies, not confined to IT departments.

For the adaptation of Big Data for banks will reveal their complexities, from the transparency of calculation methods to legal or operational constraints. In addition to these innovative perspectives, the new uses will have to be brought into line with current regulations and their application in all departments.

On the end customer side, this can lead to the feeling of having a bank that listens, an advisor who knows customers better, services that are better adapted to their needs and, above all, a much more rewarding and satisfying experience with the bank overall.

This bank, you dreamed of it as a consumer, Big Data can still do it!

Steve Bousabata, General Manager Banking France at Wincor Nixdorf



– "Banking 2020 Thought Leadership Series - A Critical Balancing Act: US Retail Banking in the Digital Era", Accenture, 2013

- There was an outcry about ING's "big data" announcement, blog " It's not my idea "March 12, 2014

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