Payments are no longer something people do – they’re integrated into consumers’ everyday activities. As illustrated below, payments are occurring every hour of the day, but they are largely invisible – subscriptions, card on file scenarios, embedded in apps and automated.
Data and analytics now allow rapid response to changing preferences and emerging value propositions to seed future growth in the digital payments area. One emerging space is “Buy Now Pay Later," also known as point of sale financing. This provides a convenient way to finance larger purchases with an installment loan offering superior terms to the typical revolving line on a credit card. This requires real-time automated underwriting, powered by data, and there are several FinTechs pioneering this capability in partnership with lending banks, including Klarna, Affirm, AfterPay, and PayPal’s new Pay in 4.
According to OnDot
, more than one-third of potential credit card customers abandon applications due to frustration with the process. This might lead us to reimagine the customer journey using data and mobile, meeting expectations set by Big Tech – Apple and Amazon. The entire customer journey from acquisition to money management is elegant and seamless. A consumer can apply with just a few keystrokes, a selfie and an ID scan, opening a new account within a few seconds. The payment credential is instantly provisioned into the mobile wallet so the customer can start using it right away. This makes a huge difference: nearly half of customers say
instant issuance would influence where they bank. Finally, tools are available to help customers understand and manage their money. For example, I recently saw a transaction on my credit card called “MULTIPLE SHOPS 8446593879” with no location specified. Issuers that enrich transaction data by clarifying the merchant name (in this case an Etsy storefront), and augmenting it with merchant location, category and purchase channel, create a far superior experience for the customer.
Another pain point addressed with data is the annoying experience of having to update card on file and recurring payment credentials with each individual merchant or biller for reissued cards (either expiring or compromised). Why not offer this as a service to cardholders and do it for them? Increasingly it is also possible to offer a much richer set of card controls such as turning the card on and off, specifying the card only works if in proximity to mobile device, geolocation limits and budgeting limits. These empowering tools make cardholders feel more secure and satisfied.
Small businesses can also benefit from these data-enabled features. In today’s environment, with so many small businesses suffering, the ability to underwrite their business based on cash flow insights from the acquiring line of business enables the issuance of more credit cards. According to the Federal Reserve
, 66% of small business face financial challenges, with 88% of firms rely on the owner’s personal credit score to secure financing. This could have a negative impact on the owner’s personal credit and cause ongoingl hardship. Only 44% were getting bank loans. Why aren’t more turning to their banks?
According to Aliaswire
, banks are declining 65-70% of small business credit card applications. Why? Rigid underwriting processes often view small businesses like consumers, without considering cash flow cycles and ability to cross-sell multiple banking products such as 401Ks, SEPs and merchant services. This might be the result of a lack of understanding of the business sector, very small credit lines and on-boarding processes that take weeks or months.
solution is an innovative data-driven example of a way to better serve small businesses. As the acquirer, the bank has cash flow insights to better gauge the creditworthiness of the small business. Risk is mitigated in real time because the credit card payments are taken from a daily split settlement file.
Data-driven analytics drives critical business outcomes including the understanding of rapid changes in customer behavior and leverages real-time analytics to integrate with new form factors and value propositions. See more at Leveraging Teradata Vantage's Superior Performance for Real-Time Analytics
Our next post will discuss the value of data and machine learning to deal with rapidly evolving risk patterns.