Dollars In The Detail; Banks Pan For Gold In ‘data Lakes’

LONDON, June 21 (Reuters) – From sending special deals on restaurants to burger-loving current account holders to offering anonymised credit credit card records, banking institutions are race to monetize the huge troves of data they hold. Mining mountains of trading data to predict stock goes; partnering with merchants on marketing promotions and using artificial intelligence (AI) tools to speed up credit decisions are a few of the areas banks are concentrating on. Craig Macdonald, head of data monetisation at Accenture.

The surge in data mining is happening against a changed regulatory backdrop. New European Union (EU) rules introduced last year allow technology companies to gain access to banking institutions’ customer data if they have customers’ authorization. The European union has toughened its privacy laws also. Companies will have to get permission before they can collect and use private information gleaned online from people living in the bloc. But even with the excess protections, sensitive data is still at risk of being exploited because many people are not aware of how they may protect themselves.

Less than a third of Europeans were alert to all their data rights in support of 13 percent said they read privacy statements fully, this year of 27 according to a poll,000 EU residents. Banks do not disclose how much they earn from analysing and marketing customer data or other ways where they monetize the information they hold.

But, compared to the billions gained from lending and trading, the amounts generated are likely to be small. Benjamin Ensor, an analyst at Forrester. Tie-ups with retail companies is one way banking institutions are monetising their data. Customers of Britain’s Lloyds and Spain’s Santander can get special offers from a range of retailers following the banks joined a digital loyalty plan run by US-based data advertising firm Cardlytics. The plan uses spending data to give customers special discounts at shops they already frequent or that are in their neighbourhood. So, burger-aficionados get deals at local burger restaurants and fashion fans get ads about discounts at clothing stores.

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The banking institutions get a share of the fee billed by Cardlytics for running the campaign. Cardlytics gets insights on consumer behavior which help the suppliers fund and tailor the offers and discounts. Cardlytics, Lloyds and Santander declined to comment on what percentage of the charge banks get. Campbell Shaw, London-based head of bank partnerships at Cardlytics. Bank clients have to enrol in the rewards program.

A spokesman for Santander said their customer spending data was only shared with Cardlytics if customers choose to receive retail offers. The lender said the info was shared on an anonymised basis meaning the customer’s name is replaced by a distinctive identifying quantity. Lloyds declined to touch upon the specifics of the offer. Its online privacy policy said the system would use customers’ mobile location data only using their permission. With the tougher rules around big data Even, privacy experts alert there is still scope for mistreatment, for example, if highly-indebted people are targeted with unsuitable offers for high interest loans or bank cards.

Paul Bernal, an expert in data privacy at University of East Anglia. Ashok Vaswani, global head of consumer and obligations at Barclays, told participants at AI conference CogX in London this month that the lender would crunch data within an moral way. Like many banking institutions, Barclays marketplaces anonymised spending data to a range of businesses including mall operators who can easily see from the info which retail stores get the most customers and are therefore worth concentrating on as tenants. Barclays said it generally does not share individually identifiable information and it transmits privacy notices to customers through a combination of email, text, post and via mobile apps. In addition, it has a page on its website explaining its data privacy policy.

Using data to improve risk analysis, make faster credit decisions and anticipate customer needs is particularly appealing for banks looking to cut costs. HSBC plans to use AI tools to rake through its 10 petabytes of data – roughly equivalent to the storage capacity of 2 million DVDs – from investment banking clients in 66 countries. Europe’s largest loan provider has struck a offer with Element AI, a Canadian company, to make it tap this so-called `data lake’.

JPMorgan, meanwhile, is developing a raft of AI applications to raised predict stock goes and to map and mine 3 billion transactions it deals with annually. The lender hired Manuela Veloso, the relative mind of the machine learning section at Carnegie Mellon University or college, calendar year to be its mind of AI research last.