Buy Now Pay Later apps require a mix of lending and payments capabilities. Hence, a strong technology ecosystem that brings together these capabilities in a flexible, intelligent, data-driven, secure, and compliant manner, is essential for Buy Now Pay Later to succeed.
Living from one paycheck to the next with a mounting credit card debt has long been a reality for many customers. Much before the COVID-19 pandemic forced them to tighten up their budgets and check spend on luxuries, shoppers had to weigh their purchase decisions. This created a huge need for financing at the point of sale so that consumers could shop with confidence, especially for the burgeoning ecommerce and direct-to-consumer websites post-pandemic. As per a recent survey, 60% customers expected more from their online experience than before the pandemic.
Fintech firms were quick to jump in with the (not-so-) new option of Buy Now Pay Later (BNPL) apps. Since then, there has been an influx of businesses offering Buy Now Pay Later to attract customers and make purchases they might have otherwise avoided. The success of the Buy Now Pay Later proposition is evident as e-tailers offering BNPL options are already seeing lower cart abandonment rates and a higher total order value. Buy Now Pay Later apps are centred around increasing affordability (pay-in-parts) or convenience (pay-later/post-paid) at the cost of increased spends.
To enable these experiences, Buy Now Pay Later apps require a mix of lending and payments capabilities such as creditworthiness assessment, integration with multiple merchants/payment providers, financiers management, offers and discounts, payment reconciliation, returns or dispute management, and so on. Thus, a strong technology ecosystem that brings together these capabilities in a flexible, intelligent, data-driven, secure, and compliant manner, is essential for Buy Now Pay Later to succeed.
Buy Now Pay Later applications rely on multiple third-party integrations in the form of merchant ecosystem tie-ups, merchant-specific offers, co-financiers, data verification services and other FinTech services. All these integrations need to work in real-time and at scale to offer a seamless checkout experience.
BNPL companies need an agile architecture which allows new integrations and the flexibility to customize offerings. The technology ecosystem must cater to the BNPL company’s own needs, while also facilitating an interface for merchants and financing partners to manage their interactions with the BNPL company. A flexible set up will also allow BNPL companies to create their own, white-labelled offerings, opening an entirely new revenue stream.
An online shopping transaction must be completed in seconds, which means that the Buy Now Pay Later application process cannot hit the pause button for approval. Customers want a shorter time-to-yes accompanied with automated checks and suggestions. To achieve such an experience, the technology setup needs to be equipped with high-performance analytics which deliver quicker and better credit decisions. Buy Now Pay Later companies can create new competitive advantages by move from simple rule-driven interfaces to self-evolving AI-driven decision-making algorithms. This can be enhanced by building unique credit scoring models which use lesser amounts of ‘traditional’ data and utilize newer variables or surrogates like purchase history, food orders, cab rides, phone usage etc. An effective analytics set up can prove to be the difference between a profitable Buy Now Pay Later set up vis-a-vis an NPA-laden business.
The increase in online shopping has been well-matched with a rise in online fraud. Fraudsters can steal BNPL credentials of genuine shoppers and splurge before getting caught, due to the significant gap between purchase and the first instalment. This underlines the criticality of early identification of fraudulent purchases, that too within seconds, at the point of sale. To mitigate fraud risks, it may be emphasised that Buy Now Pay Later apps need a super-efficient and scalable fraud score algorithms with multi-layered approaches which enable them to analyse a large number of attributes in real-time and accurately filter out false positives. PayPal is a great example of a payments platform using AI and ML to limit its fraud rate at a low of 0.32%.
Cyberthreats pose a great risk to various entities in BNPL chain and given the young age of this setup, there is need to be extra cautious in this regard. A strong security layer in the BNPL technology setup is of paramount importance to protect both the financier as also the consumer. Data encryption across the entire transaction value chain is essential to prevent cyberattacks, like other payment systems. On the regulatory front , while Buy Now Pay Later remains lightly regulated, the increasing popularity and frauds will see regulators starting to tighten the screws on Buy Now Pay Later propositions.
UK and Australia are considering new regulations which sets boundaries around Buy Now Pay Later apps to reign in mounting customer debt. Even now, Buy Now Pay Later companies need to adhere to strict laws related to data privacy, data residency, cybersecurity and payment compliance. Technology platforms need not only be compliant to current regulations but be built flexibly to enable future compliance with minimum effort as well.
In conclusion, true value of such real-time product proposition can only be realized when organization carefully knit the multi-coloured tech threads forming a solid yet elastic fabric. This requires deep tech acumen, conscious architectural choices and smart build-versus-buy decision making.
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