Have you ever noticed that your Amazon shopping cart was getting too full, and you had to get out with a few things to stay on budget? Is there any luxury item that you’d love to buy but can’t afford right now? Are you sick of using your credit card for everything? You can rely on the Buy Noe and Pay Later options.
BNPL is a new way to think about short-term loans in the age of e-commerce. It is the new kind of loan given out at the POS. It is an innovative way for companies to use data-driven strategies to unlock value in a fast-changing online sales environment. The covid pandemic has been going on for two years, and almost all of our everyday shopping is done online; BNPL is a trend to watch.
Looking from the customer’s point of view, it is better than most of the old ways of lending. The only good thing about it is that the application process does not affect the customer’s credit score. Second, BNPL is more flexible than other payment plans because customers can choose how many instalments they want and when to pay. Third, most BNPL plans do not have interest fees. Instead, this model gets money from referrals and late payment fees.
Merchants are also having ample benefits from this method. The BNPL platforms use their user and drive traffic to the merchants’ online stores. This type of marketing works out to be cheaper than other ways to get customers. Second, statistics show that people who shop online and pay in instalments tend to buy more than people who pay for everything. BNPL also helps online stores eliminate the risk of chargebacks and other types of fraud.
E-commerce, Made Possible by AI
Thanks to new technologies, the “buy now, sell later” model only worked last year. Specifically, BNPL is now possible because artificial intelligence and machine learning have become very good recently.
Short-term sales have been around for a long time. Stores and restaurants have been trying to boost sales for a long time by letting customers open tabs. Retailers and big-box stores partner with banks to offer credit cards and instalment loans at the register.
Since the advent of the digital age, e-commerce enterprises have struggled to provide customers with payment plans. It’s become a prerequisite for tens of millions of customers and firms that serve them. Due to COVID 19, most people can only shop online. They need credit often. The answer is BNPL, and the Reasons includes are mentioned below:
Better Customer Onboarding
In the old credit system, customers who could show they had good credit could get short-term loans at the point of sale (POS). It is done by looking up a customer’s credit score most of the time. It may seem simple, but there are two significant problems with it: First, the customer’s score may be too low for a loan to be given. This customer, who could have been very expensive to get, will have to be turned away. Second, many customers with good credit may not want merchants to check their scores because too many checks can hurt them. In the end, these customers only buy a cheaper item or nothing at all.
Buy Now Pay Later solves both of these problems by introducing a new way of doing things and then technically doing things.
First, BNPL payment plans are made so that credit checks are not done. When customers think of purchasing something, they never actually try to get a loan from the merchant. They make an account with the BNPL provider, who makes then a “soft inquiry” on their credit. You can even say, the customer always allows the BNPL offer provider to check their credit score. They never let a bank evaluate a loan application, but as someone else is interested. Such checking never affects the good or bad rating.
Second, AI/ML allows BNPL platforms to collect and analyse customer data to make smarter decisions. If a customer wants to purchase via BNPL, the provider may ask them to answer a series of questions or send in certain documents. Then, one can use this information for natural language processing. NLP technology can give you essential information about a customer.
Ongoing Data Analytics and Risk Modeling
As we’ve already talked about, BNPL platforms regularly collect and analyse data to learn more about their customers so they can set up better payment plans for the future. BNPL providers are more likely to keep ordering this information from customers after the initial purchase. Using AI and data analytics on this constant data flow can turn one-time customers into valuable, long-term customers.
As long as one collects this information with the user’s permission, you can use it in some exciting ways. One option is for BNPL companies to look at what users are buying and suggest other things they might want to buy from different stores. It makes more money overall, bringing in more money through referral fees.
The fact that BNPL providers can use data from merchants’ platforms and websites to give customers different scores based on how they act online is another way that data analytics is used here. You can use AI to look at things like a customer’s “clickstream” (the links they click on) and what they’ve bought before from a merchant’s store.
BNPL providers can also create risk models based on interacting with their customers. You can use these models to create value in the future. For example, a user who always pays their instalments on time could be offered unique products, better loan terms, and even things like loyalty rewards and cashback. A user with a less-than-perfect record might get higher interest rates and worse terms, like less time to pay in instalments and higher fees for being late.
Fraud prevention
One of the primary uses of AI/ML technology in fintech and e-commerce has been finding and stopping fraud as soon as possible. Payment processors use machine learning to find suspicious transactions by default, and AI-powered identity checks have become standard for many regulated services.
AI/ML also has a lot of benefits for BNPL providers. One can use photo recognition and natural language processing to check the personal documents of borrowers, just like in other types of online lending. You can use machine learning to look at users’ transactions in real-time and stop the suspicious activity before any money changes hands. Also, merchants can use predictive algorithms on the data they collect to spot users who may try to repeat fraudulent transactions or schemes that have been tried before.
Collections
One bad thing about the lending business is that not all borrowers will be able to pay back on time. It is also true for the BNPL model. AI is being used more and more in the collections process, which is good news for service providers. AI is mainly used to solve problems with how people talk to each other. When working with borrowers who were found online, these problems are essential.
In legacy lending, debt collectors usually only use a few ways to contact debtors, like the phone or email. Many people who owe money find it easy to avoid these interventions. Using data analytics strategies powered by AI, lenders can watch all the different ways a borrower might reach them. If the customer regularly posts on Instagram, the collection team will know that a direct message might best contact them. Almost any social network or messaging app can use this.