How Big Data Analytics, AI and Machine Learning is Being Leveraged Across FinTech


I am sure that you have heard of one of the only profitable FinTech unicorns in the world: Klarna. A customer making an online purchase enters only their email address and zip code on an e-commerce merchant site to buy an item. Klarna pays that merchant immediately and then collects the amount due from the consumer within 14 days. Imagine the amount of work the engines in the background are doing.

Today, I will be talking about that area/segment a bit. The use of analytics in its many forms – big data, data science and many more – is not a new concept in FinTech. The growth in data or data explosion is a function of multiple technological advancements. Adoption of cloud, mobile technologies, apps, wearable devices, intelligent/smart networks, and Internet penetration/usage are some of the major factors for growth in overall data. To put this into perspective, IDC estimated that the digital universe is doubling its size yearly and would reach 44 ZB in 2020 from 4.4 ZB of data generated in 2013.

IDC also forecasted that the big data technology and services market will grow at a 26.4% compound annual growth rate to $41.5 billion through 2018, or about six times the growth rate of the overall information technology market. The ability to draw insights and the ability to optimally monetize available data would place companies in a unique position challenging established rules and processes. Low-cost storage technology, smartphone penetration and cloud are underlying forces which propel the requirement of big data and analytics.

Here is a list of some of the areas in financial services that are seeing major overhauls:

Credit Scoring: Undoubtedly, one of the major sectors that has seen unprecedented new solutions leveraging big data is lending and credit scoring. For decades, credit scores provided based on basic financial transaction served as the norm for all credit activities in the financial services space. Essentially, these new sources go beyond the available quantitative data from banks and assess qualitative concepts like – behavior, willingness, ability, etc. The growth in segments such as P2P Lending, SME Financing is a result of these innovative scoring models. Examples of such startups include Credit Sesame, Faircent, OnDeck, Kabbage, LendingClub, Prosper, ZestFinance and Vouch Financial.

Customer Acquisition: The cost of acquisition drops drastically for customer acquisition (illustrated above) when we compare the physical to digital channels providing huge benefits to both financial services firms as well as startups. Place – one of the four Ps of marketing – has been dominated by the digital channel by both customers and clients. Increasingly, the customers’ behavior to use digital channels coupled with low-cost advantages for clients (especially in financial services) makes this a major focus area. Leveraging big data, financial services are moving to digital channels to acquire customers. The growth in number of offerings which are moving online – direct investment plans, online savings/deposit account opening, automated advisory services – provides a clear indication of the importance of digital channels for financial services.

  • By Amit, cofounder and chief curator of Let’s Talk Payments. 

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