As digital banking and peer-to-peer lenders sprout up, so innovation is adapted to meet the demand for speedy lending decisions. Loan origination has taken an interesting turn towards the plethora of free online data so is it time for consumers to social-engineer their private lives? Anna Milne writes
Provenir is a loan origination firm with a strong focus on tech to provide lenders with fast loan decision strategy. Despite having two major banks on its books, HSBC and Wells Fargo, it has been operating under the radar as a "well-kept secret" since starting in 1996. This is due to not having a PR or marketing strategy until 2013. In the US it is slightly better known and it is busy doing the rounds in Europe before hitting Africa and Asia.
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Provenir is enlisted to embed a scoring system into banks’ and lenders’ legacy systems, reducing the delivery time-frame from months to minutes, in some cases. What may come as a surprise is the range of tactics employed to facilitate such fast decisions.
Klarna is a Swedish ecommerce firm that processes some 30% of all online transactions in Sweden. Its system allows shoppers to receive goods before paying for them, taking on the credit risk for retailers. Offering credit at the point of transaction requires a fast decision process, which of course is where Provenir comes into play.
"No money changes hands and the result for one merchant has been an increase in fulfilment at checkout from 35% to 55%," said Paul Thomas, Provenir’s managing director, EMEA.
"Provenir pushes through several hundred transactions per second with less than a second’s latency." The decision model is based on core data and is surprisingly basic, checking the address; whether it’s a repeat customer; how many transactions the customer has made that day. And more.
The most interesting aspect of Provenir loan origination is the use of social media as an ‘add-on’ to the approval process. If a loan application is ‘referred’, aka undecided, social media analytics are employed to try and bump through more approvals. One of Provenir’s clients had a 98% decline ratio, which is apparently not unheard of.
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By GlobalDataIf there is nothing in the person’s activity to suggest an irresponsible or impulsive nature or dubious lifestyle, then the referral is likely to be approved. If something is flagged up, "such as regular posts about gambling", then it will likely be declined. Common sense, but that the person’s friends are pulled under the radar as well.
In cases where very little credit history is available it can be a very useful tool, even if it does make us all squirm just a little. And of course, for the unbanked, it opens up the credit world where previously they were pooled into one large group of high-risk individuals. It is just a matter of whether the consumer fully understands the implications of granting permission for access to his or her social media activity. Hence something of a grey area: consumer privacy issues aside, it is difficult to ascertain how a lender scores the data, if the algorithms are undisclosed.
According to Anand Raman, partner of a Washington DC law firm, specialising in consumer finance,"It can become a fair lending issue if the use of that data results in disproportionate negative outcomes for members of a protected class." Raman was speaking to the FT in February.
During the application process, the consumer is invited to allow the lender access to his or her social media activity, to "increase the likelihood of doing business".
"As lending goes, the more declines you make in the referral space, the more responsible you are as a lender, but if you can find a way of informing that referral space to push more approvals, you reap the benefits."
Provenir works with a Big Data partner to incorporate this. "It’s going to be the norm but at the moment it’s novelty."
When Provenir pitched it to a group of bankers at a roundtable event it hosted, Thomas said the reaction was at once horrified and delighted- as consumers, terrified; as risk managers, delighted.
German digital bank, Kreditech, calls it Algorithmic Banking. Its website boldly states: "Banking as we know it today is dead. Your banking branch won’t exist ten years from now, and neither will cost intensive, manual banking processes. We believe algorithms and automated processes are the way to customer-friendly banking." It further states that it uses 20,000 dynamic data points to credit score anyone, "including the four billion individuals without a credit score".
The only stipulation is that a user has and operates a smartphone. The kinds of data that are taken into consideration are mind-boggling, from the browser being used to the time of day a loan application is made. Kreditech asks consumers to allow its app to connect with their Facebook account, stating proudly it is interested in their news feed, activities, home town and education. Plus that of all their friends.
UK-based Wonga uses similar scoring methods to provide fast decisions, based on myriad data points. It states on its website: "in addition to the personal and financial information you submit (or we collect), we may collect information about your computer including, where available, your IP address, operating system and browser type – for the purposes of system and loan administration and product improvement."
A recent report by the US Proceedings of the National Academy of Sciences on digital data profiling highlighted the efficacy of predicting all manner of attributes from the person’s Facebook Likes. For example, "Users who liked the Hello Kitty brand tended to be ‘open’ but low on ‘conscientiousness’, ‘agreeableness’, and emotional stability’.
The report concluded that a wide variety of people’s personal attributes, ranging from sexual orientation to intelligence, can be automatically and accurately inferred using their Facebook Likes. "Similarity between Facebook Likes and other widespread kinds of digital records, such as browsing histories, search queries, or purchase histories suggests that the potential to reveal users’ attributes is unlikely to be limited to Likes. Moreover, the wide variety of attributes predicted in this study indicates that, given appropriate training data, it may be possible to reveal other attributes as well."
Provenir is by no means the first to employ this method but as Thomas says, it will soon no longer be a novelty but the norm. Exactly how regulation will catch up with it remains to be seen, particularly given the generation coming of age now whose lives are played out on social media without a second thought.
