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Fintech Adopts Big Data and Cognitive Computing Apps

Given that the financial services sector thrives on large quantities of data, it is not surprising that new technology is expected to play a key role in this industry's digital transformation. Innovative and disruptive financial technology (Fintech) ventures will create new business models that drive progressive change.

Juniper Research has found that Fintech platform revenues for unsecured consumer loans issued using machine learning technology are set to grow by 960 percent during 2016 to 2021, rising to $17 billion globally. This growth is being driven by advances in big data analytics and cognitive computing.

Juniper's latest market study found that machine learning investment in Fintech will advance rapidly, owing to the highly data-driven nature of the market -- it's anticipated that AI integration is likely to produce substantial benefits.

Machine learning technology advances -- a subset of artificial intelligence (AI) -- have grown significantly since 2011, with substantial increases in related venture capital (VC) and research & development (R&D) investment.


Fintech Market Development Opportunities

For example, two Fintech start-up companies -- Kabbage and ZestFinance -- have collectively raised $500 million in funding. Meanwhile, vendors analyzed by Juniper have invested a total of $83 billion in R&D during 2015. Each of these vendors names AI as a part of their core business strategy.

Until recently, machine learning was too expensive and computationally time-intensive to break into the mainstream. Moreover, access to extensive data sets for algorithm training were somewhat limited.

Presently, 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.

Big Data Analytics and Cognitive Computing Apps

AI and other forms of cognitive computing are particularly useful for risk-assessment purposes, where variables from numerous financial and non-financial datapoints 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.

"Where Big Data analytics offered retrospective business intelligence, machine learning offers predictive and even prescriptive capabilities," said Steffen Sorrell, senior analyst at Juniper Research. "Data is key -- and industries able to draw expertise from data scientists will be the first to capitalize on the AI opportunity."

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