How Buy Now Pay Later (BNPL) Is Bringing E-Commerce into the AI Era by@ilyshka

How Buy Now Pay Later (BNPL) Is Bringing E-Commerce into the AI Era

Buy Now Pay Later (BNPL) is a reimagining of short-term financing for the e-commerce era. The application process does not affect the customer's credit score. BNPL offers more flexibility than do other payments plans, typically allowing customers to customize the number of installments and payment schedule. Revenue in this model is instead from referrals and from penalty fees that collected on late payments. The model has only really become viable over the last year or so because of technological innovation.
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Elay Romanov

Entrepreneur. Machine learning enthusiast

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Have you ever noticed your Amazon shopping cart getting a bit too full, then had to delete a few items in order to stay within budget? Is there a luxury item or neat new gadget you'd love to buy, but just can't justify the expense at the moment? Tired of pulling out your credit card for every little purchase? Buy Now Pay Later (BNPL) may just be the solution you've been looking for.

BNPL is a reimagining of short-term financing for the e-commerce era. This new kind of personal loan, issued at the point-of-sale, is an innovative way that companies can take advantage of data-driven strategies in order to unlock value in a sales environment that is rapidly shifting online. Now, two years into the Covid pandemic, with virtually all of our everyday purchases being carried out on the internet, BNPL is certainly a trend to watch.

From the customer's perspective, BNPL is a definite upgrade over legacy lending models. For one, the application process does not affect the customer's credit score. Second, BNPL offers more flexibility than do other payments plans, typically allowing customers to customize the number of installments and payment schedule. Third, BNPL plans usually do not charge interest. Revenue in this model is instead from referrals and from penalty fees that collected on late payments.

There are numerous advantages for merchants as well. To begin, BNPL platforms have their own user bases and can drive traffic to merchants' online stores. This kind of referral marketing comes out as cheaper than other forms of customer acquisition. Second, statistics show that e-commerce users who pay in installments tend to purchase more items than those who pay in full. Finally, BNPL helps to eliminate chargebacks and other kinds of fraud risk for online stores.

E-Commerce, Brought To You By AI

The Buy Now Sell Later model has only really become viable over the last year or so because of technological innovation. Namely, BNPL has been made possible by the relatively recent maturity of artificial intelligence and machine learning.

Short-term point of sale loans are by no means a new thing. Indeed, stores and restaurants have attempted to increase sales by allowing customers to open tabs for ages. Already decades ago, major retailers and big box stores would, in partnership with banks, offer credit cards and installment loans to customers right at the cash register.

E-commerce companies have been trying to find ways to offer customers credit since the start of the information age. More recently, this has become an essential need for millions of consumers and the companies that serve them. Indeed, today, in the midst of the global COVID 19 pandemic, more and more people are finding themselves in situations where they can only make purchases online. Very often, these people need access to credit. BNPL, it seems, is the answer. Here are just a few reasons why:

Better Customer Onboarding

In the legacy credit system, the issuance of short-term, PoS, loans depends on the ability of customers to prove good credit. This is usually achieved by looking up customers' credit scores. This may sound straightforward, but actually it presents two key problems: First, the customer may have too low a score to be issued a loan. This customer, who many have been quite costly to acquire, will need to be rejected. Second — since frequent checks can negatively affect scores — many customers who do have good credit may not be willing to allow merchants to look up their scores. These customers end up only purchasing a lower cost item, or nothing at all.

Buy Now Pay Later addresses both of these issues using, first, a procedural innovation, and second, a technical one.

To begin, BNPL payment plans are designed in such a way so as to bypass credit checks. When a customer chooses to purchase an item, they never actually attempt to apply for a loan through the merchant. Instead, they create an account with the BNPL provider, who then carries out what is called a "soft inquiry" on their credit. In other words, the customer authorizes the installment plan provider to look up their credit score not as a financial institution evaluating a loan application, but instead as some other interested party. This kind of check should not have any positive or negative effect on the rating.

Second, by taking advantage of AI/ML, BNPL platforms are empowered to collect and analyze customer data in order to make more sophisticated decisions. When a customer chooses to make a purchase via BNPL, the provider may prompt them to answer a series of questions or to submit certain documents. Natural language processing can then be applied to this information. NLP technology can provide valuable insights about the customer.

Customers that are shown via data analytics to be low-risk could be offered better loan conditions, such as longer repayment periods. If the customer is considered higher-risk, their payment plan would be configured differently.

Ongoing Data Analytics and Risk Modeling

As was discussed above, BNPL platforms regularly collect and analyze data in order to better evaluate their customers for future payment arrangements during the onboarding process. Increasingly, BNPL providers continue to collect this information from customers even following the initial purchase. By applying AI and data analytics strategies to this ongoing stream of data, it becomes possible to turn one-time customers into highly-valuable, long-term clients.

Provided that this data has been collected with the user's consent, there are a couple of interesting ways this data can be used. One possibility is for BNPL companies to look at what the user is purchasing and make suggestions for additional purchases from other merchants. This not only increases overall turnover, but it also boosts revenue in the form of referral fees.

Another application of data analytics, here, is reflected in BNPL providers' ability to use data from merchants' platform and websites to different kinds of scoring of customers, based on their online behavior. For example, AI can be used to analyze the links that a customer clicks (their "clickstream") and previous purchases made through merchants' stores.

Moreover, BNPL providers can develop their own risk models based on their own interactions with customers. These models can be used to create value further down the line. For example, a user who always pays their installments on time could be offered exclusive products, better loan conditions, and even things like loyalty rewards and cashback. A user with a less-than-perfect record could be targeted with higher interest rates and worse conditions, such as lower installment allowances and higher late-payment fees.

Fraud prevention

The early detection and prevention of fraud has long been one of the primary applications of AI/ML technology in both fintech and e-commerce. Indeed, payment processors today use machine learning to detect suspicious transactions as a default, and AI-powered identity checks have become standard for many kinds of regulated services.

For BNPL providers, AI/ML provides numerous benefits as well. Like in other forms of online lending, photo recognition and natural language processing can be utilized in order to verify the personal documents of borrowers. Machine learning can be used to analyze the transactions of users in real time and block suspicious activity before any money changes hands. Moreover, predictive algorithms can be applied to data collected by merchants in order to flag users who may attempt to repeat fraudulent transactions or schemes that had been attempted in the past.


One of the unfortunate truths about the lending industry is that not all borrowers will be willing to pay on time. This holds true in the BNPL model as well. Luckily for providers, great progress has been made of late in the introduction of AI into the collections process. Primarily, AI can be used to address problems relating to communication. These issues are significant when dealing with borrowers that were initially engaged online.

In legacy lending, collectors tend to only attempt to reach debtors using a few channels, such as by phone or email. Many individuals who owe money have little issue avoiding these interventions. Using AI-powered data analytics strategies, lenders can monitor all the different channels that a borrower might use. If the customer has been shown to regularly post to Instagram, the collection team will be able to understand that a direct message may be the best way to approach the client. This can work for virtually any social network or messaging platform.


Buy Now Pay Later and the technology behind it is having a massive effect on e-commerce. Since we specialize in developing unique AI/ML solutions that unlock value for businesses operating in this sector, we at Daiger have been observing this process closely. Please feel to get in touch, we would be happy to discuss how we can create innovation in online retail together.

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