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How Big data 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. It 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.

Big Data FinTech 2

Customer Acquisition: The cost of acquisition drops drastically for customer acquisition 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.

Marketing, Customer Retention, and Loyalty Programs: Contextual and personalized engagements – be it in product/service advertising or discount offerings, have become the norm of many new-age companies. Analytic solutions that combine historic transactional data coupled with external information sources increase the overall conversion rate. Many financial services firms partner/acquire/invest in startups and growth-stage companies, and are actively pursuing these services. Firms are effectively leveraging these solutions to increase the cross-sell and upsell opportunities, understanding customer requirements and providing customized packaging. Card-linked offers, customized reward solutions are some of the offerings that are being provided by FinTech firms.

Some solutions in marketing, loyalty and customer acquisition space are Cartera Commerce,Cardlytics, Truaxis (acquired by MasterCard in 2012), Segmint, Personetics, etc.

Risk Management: World-over, real-time payments have taken center stage in the past decade and hence, there is a requirement for enhanced risk management solutions in this new environment. Predictive analytics that utilizes device identification, biometrics, behavior analytics, etc. are major driving factors (each solution or a combination of each of them) for better risk management solutions in the fraud and authentication space. Firms that execute well on eradicating vulnerable access points would benefit not only in terms of lower losses but it also increases stickiness to their solutions. Apart from banks’ own initiatives, various regulations are also enforcing rules that make it vital for banks to store and manage more information about payments. Hence, apart from just storing this data, banks look at building powerful algorithms that mine this data and provide actionable insights. Some startup solutions in this space are BillGuard, Centrifuge, Feedzai, Klarna, etc.

Investment Management: Investment management as a segment has witnessed innovation on multiple fronts. While robo-advisory solutions take the spotlight in the segment, there are other solutions that are leveraging the power of big-data to provide efficient investment management solutions – the ability to utilize search data, combine multiple macroeconomic factors, quantifying latest news/insights and combining all these to provide potential upside/downside scenarios. Also, there are solutions developed to detect specific market anomalies and provide preventive action steps in the investment portfolio. Specific startup solutions in this space include Wealthfront,EidoSearch, SigFig, Betterment, LearnVest, Personal Capital, Jemstep, etc.

The srticle first appeared in LTP

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