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

Klarna is one of the only profitable FinTech unicorns in the world: Klarna. An online purchase customer 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. In this article, we will be talking about that area/segment. 

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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 major factors for overall data growth. 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 would 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 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 that propel big data and analytics requirements.

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

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Credit Scoring

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Undoubtedly, one of the major sectors that have seen unprecedented new solutions leveraging big data is lending and credit scoring. For decades, credit scores based on basic financial transactions 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 results from these innovative scoring models. Examples of such startups include Credit Sesame, Faircent, OnDeck, Kabbage, LendingClub, Prosper, ZestFinance, and Vouch Financial.

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Customer Acquisition

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The cost of acquisition drops drastically for customer acquisition when we compare the physical to digital channels providing considerable benefits to both financial services firms and startups. Place – one of the four Ps of marketing – has been dominated by both customers and clients through the digital channel. Increasingly, the customers’ behavior to use digital channels coupled with low-cost advantages for clients (especially in financial services) makes this a significant focus area. Leveraging big data, financial services are moving to digital channels to acquire customers. The growth in several offerings that are moving online – direct investment plans, online savings/deposit account opening, automated advisory services – clearly indicates the importance of digital channels for financial services.

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Marketing, Customer Retention, and Loyalty Programs

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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 historical transactional data coupled with external information sources increase the overall conversion rate. Many financial services firms partners/acquire/invest in startups and growth-stage companies and actively pursue these services. Firms effectively leverage these solutions to increase the cross-sell and upsell opportunities, understand customer requirements, and provide customized packaging. Card-linked offers, customized reward solutions are some of the offerings that FinTech firms are providing.

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

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Risk Management

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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 combination) for better risk management solutions in the fraud and authentication space. Firms that execute well on eradicating vulnerable access points would benefit from lower losses and increased stickiness to their solutions. Apart from banks’ initiatives, various regulations also enforce 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.

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Investment Management

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Investment management as a segment has witnessed innovation on multiple fronts. While robo-advisory solutions take the spotlight in the segment, other solutions are leveraging the power of big data to provide efficient investment management solutions – the ability to utilize search data, combine multiple macroeconomic factors, quantify the latest news/insights, and combine 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.

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