How fintech is turning analytics into dollars

Published on the 04/08/2016 | Written by Donovan Jackson

Looking for growth? It’s in the analytics as Juniper tips fintech for a revenue boost of 960 percent…

To get a good idea of the potential impact of analytics on business, take a look at the triple figures for revenue growth that Juniper Research is anticipating in the fintech environment. It reckons revenues for unsecured consumer loans issued using artificial intelligence and machine learning technology are set to see a jump of 960 percent in the period from now until 2021 – and a data scientist said there are lessons for local businesses in that finding.

In monetary terms, Juniper’s growth figures for this market should see it to rise to US$17 billion globally.

In its new study, ‘AI & Machine Learning: Fintech Dynamics, Disruption & Future Opportunities 2016-2021’ Juniper said the anticipated rise is driven by advances in analytics and accessible computing power.

It found that machine learning spend in fintech will advance rapidly, owing to the highly data-driven nature of the market. It also said AI integration is likely to spell substantial benefits.

Chief data scientist at Soltius Andrew Peterson, however, pours a slight dose of cold water on the claims. “It’s debatable whether or not these fintechs are really using AI or if they are just using the term ‘AI’ as a marketing strategy while using conventional analytics tools,” he said.

Nevertheless, Peterson said the industry is moving forward in a logical way by using a broader range of information about customers and a variety of algorithms that, combined, will likely provide a more accurate indication of the real risk each customer poses to the lender.

In its statement, Juniper said that until recently, machine learning was too expensive and computationally time-intensive to break into the mainstream, while access to extensive data sets for algorithm training were limited. It added, however, that the ability to use GPU (graphics processing unit) hardware for processing massive and highly available data sets, along with unlimited affordable computing power in the form of distributed architecture, has opened the market to a swathe of disruptive new players.

As for why fintech in particular is likely to receive this massive boost in revenue, Juniper explained that AI is particularly useful for risk-assessment purposes, where variables from numerous financial and non-financial data points are assessed by algorithms to approve loans. This widens the addressable market for financial institutions considerably over traditional FICO credit scoring, where lack of credit history may mean loan rejection despite a real low risk for the lender.

For his part, Peterson said that while the devil is in the details, if the approach is executed well, it could result in fairer business transactions for both the customers and the lenders.

And, he said, there are lessons for local companies. “This is another example of a creative application of advanced analytics that has the potential to benefit all stakeholders. The companies involved identified real business objectives – reduce lending risk, reduce application processing time, create a fair online market place, and then they thought creatively about how technology and advanced analytics could help them achieve those objectives. This is something every business can, and should, be doing right now.”

Moreover, Peterson said analytics is potentially applicable to any business and that there is no need to be put off by terms like big data, machine learning or AI. “Pretty much every company that collects and maintains reasonable data about its customers, suppliers, and operations stands to benefit tremendously from advanced analytics that don’t [need to] involve AI or super computers.

“The key is not to get drawn in or intimidated by the hype, but rather to focus on the company’s strategic plan and business objectives, then to think critically about how data and advanced analytics can help achieve those objectives. There is tremendous value to be had from simpler and cheaper technologies right now.”

His advice for an analytics journey is to start with uncomplicated approaches that target specific business objectives.

Juniper has put out a whitepaper, titled ‘Fintech AI: A new type of trader’ to accompany its research.

Questions or comments...

  1. Glenn Roberts

    Andrew is correct in avoiding the hype. In their book – Analytics at Work – Davenport, Harris and Morison state that “statistical analysis can be powerful, but it’s often complex, and sometimes employs untenable assumptions about the data and the business environment.” You have to leverage both – smart analytics and business acumen.


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