The rise of the data artist

4 January 2017

The fintech online payday lender, 247Moneybox.com has always considered itself a technology firm first and a provider of payday loans second. This mindset was how the firm was set up from its outset in 2009 allowing customers to be at the heart of the business.

Along with the technology the firm is keen to stress that although it's the central theme of the business model, technology for the sake of technology is at best a distraction and at worst a negative contributor. This is not a novel argument and indeed many have debated the influence or contribution of technology versus human input.

Data artists making headway in the online loans arena

Phillip Mangold, a systems engineer for the lender, based in its operation straddling Mayfair and Soho, explains: "In every meeting we have the first and last question is always around the contribution of the thing we are discussing will make. There is zero point in pursuing something for the sake of it. Yes, it may be a very interesting academic exercise but what's the point?".

An example the firm puts forward is that of the obsession in the lending and indeed other industries with more data. Mangold continues "the term big data is often confused with quantity. We've never thought of the big in big data referring to volume or mass of data. We've always considered the data needs an additional adjective such as 'quality' or 'useful'. In lending short term, having masses of data of employment trends or other macro variables is nice but bears little to no relevance in making a lending decision."

So, with the new year comes the search for not only new technology but new data. The lender is constantly searching, testing and refining new systems approaches and processes. This can take the form of looking at totally novel types of data for correlations or can take the form of looking at the same data as previously but in a different way. This latter point is very important in the fast paced every changing landscape of high cost short term credit as data that may have previously not shown any degree of predictability ‎may turn out to be useful.

This volatility is to be embraced according to Mangold, "Any model has a finite life, what we love and to be honest sometimes loathe about the ultra-fast loan cycles we see in this model is that you have to constantly tinker."

Where will this ultimately end up is a question that Mangold and his team constantly ask but dismiss at the same time. ‎"It's only natural to aspire to fix or resolve something, my team and I are no different. What we've had to learn to love and to embrace is the constantly shifting goalposts or playing field. What worked yesterday might not today and relying on yesterday's tech can be detrimental to say the least".

Given this the lender has committed to allowing its teams to 'free think'. Mangold again: "Now we don't want to get all 1984, but us data geeks are not perhaps known for our creativity. We've all spent too long having that drummed out of us. What we're trying to do here is drum that creativity back in. It's a skill that needs to be relearnt and terms like data scientist are not helping. We prefer data artist!"

The lender hopes this mindset shift will set it up well for 2017 and beyond.