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Analytics, ML and Data Science Help FinTech Offer Better Services

Prove
September 7, 2021

The use of big data, machine learning, data science in FinTech space is not a new concept (at least from its sound). However, the growth in data or data explosion is a function of multiple technological advancements. Adoption of cloud, mobile technologies and apps, wearable devices, intelligent/smart networks and systems, Internet penetration, and usage are major factors for overall data growth. Therefore, we wanted to understand how FinTech players are using it (or not). Some of the areas in financial services that are applying analytics, ML, and big data are listed below:

Credit Scoring: Undoubtedly, one of the major sectors that have seen unprecedented new solutions leveraging big data is lending and credit scoring. For decades, credit scores provided data based on basic financial transactions and 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 P2P lending and SME financing results from 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 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 major focus area. Financial services are moving to digital channels to leverage big data to acquire customers. The growth in the number of offerings that are moving online – direct investment plans, online savings/deposit account opening, and automated advisory services – clearly indicates the importance of digital channels for financial services.

Analytics, ML and Data Science Helps FinTech Offer Better Services

Source: BBVAOpen4U

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 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 and customized reward solutions are some of FinTech firms’ offerings.

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 combination) 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 also increase stickiness to their solutions. Apart from banks’ own 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.

Investment Management: Investment management has witnessed innovation on multiple fronts as a segment. 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.

So what we are seeing is that the ability to draw insights and the ability to monetize available data optimally would place companies in a unique position, challenging established rules and processes. Low-cost storage technology, smartphone and app usage, and cloud are underlying forces that propel big data and analytics requirements.

How major US banks are relying on big data

Bank of America (BofA): BofA leverages big data across the customer life cycles. The bank provides a complete customer picture by capturing data across channels. Accordingly, the bank provides various offers like loans, refinancing, deals, discount coupons, etc. When a customer walks into a bank with specific queries, the customer’s picture – what the customer could need, his current situation, history, and predictions – are all available to the sales associate at the branch. In addition, BankAmeriDeals utilizes deals and offers as an incentive for customers who leave the bank’s services. The bank also utilizes big data in enhancing its risk management capabilities. Reports suggest that they reduced their loan default calculation time by around 95%.

Being one of the largest banks, BofA had quant analysts in various functions before big data. However, to meet the bank’s goals and utilize existing talent, BofA had reorganized its organizational structure and governance model. They started working more closely with business line executives and increased cross-communication among different business lines. They run BankAmeriDeals (cashback offers) to credit and debit cardholders by leveraging big data. They also are one of the major partners of Cardlytics, a card-linked offers provider.

JPMC: A comprehensive analysis by DeZyre in this article shows that JPMorgan applies big data across multiple lines of its services. With 150 petabytes of data, 3.5 billion user accounts, and 30,000 databases, JPMC handles a mine of data waiting to be explored. Using open-source frameworks like Hadoop along with other proprietary technologies, the firm applies big data for:

  1. To read US Economy from users’ data.
  2. To enhance fraud detection.
  3. Enhance value to corporate clients by providing them with information that can be used to benchmark their performance with their peers and competitors.
  4. Won Euromoney’s Best Online Fixed Income Research for using their CreditMap application (leverages Datawatch) to bridge the gap between providing informative research and valuable analytics to its customers.
  5. To effectively handle end-to-end cash management for customers.
  6. Enhance customer experience by providing context-relevant payment messages.

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