Buy Now Pay Later and The Credit Decisioning Dilemma for Traditional Banks

Consumers around the world are now familiar with the dynamic nature and evolving trends of the banking and payments industry. As fintech companies introduce new services and products to meet the changing needs of customers, one banking factor remains constant – the financing of purchases through repayable loans or credit.

The concept of lending has been around for thousands of years. However, the modern method of assessing a customer’s credit worthiness is a more recent advancement, moving forward from unreliable methods such as character judgements, character references, and collateral guarantees, to the more reliable and comprehensive data analytical models, or the process of credit decisioning.

Traditional banks and financial institutions cite credit decisioning as one of the most important elements of the lending business. Accurately assessing the credit worthiness of an applicant before deciding to grant them a loan or credit card, provides greater assurance that the customer will repay the borrowed principal plus interest, as per an agreed schedule. Over time, with new technologies, and the ability of banks to collect and analyze vast amounts of data, credit decisioning has become faster, more streamlined, and effective. Before approving or rejecting a customer’s application for a credit or loan product, the right paperwork is submitted and validated, and credit scores are checked by the provider.

Enter ‘Buy Now Pay Later’ or ‘BNPL’, a newer, more convenient credit payment option that is disrupting the credit decisioning process. BNPL allows customers to make a purchase and break the payment down into installments that can be paid either with low or no interest, over a period of weeks or months, depending on the service provider’s terms. Let’s see why BNPL is gaining traction so quickly as a preferred mode of payment.

Using credit cards to make purchases is so popular, that one might incorrectly believe that BNPL is not a threat to its business share. But consider this, surveys in the last two years indicate a huge growth in the use of BNPL in America. Reports also show that close to 20% of Australian consumers held BNPL debt in 2021, the fourth most common type of debt after credit cards. A part of this growth is attributed to the COVID-19 pandemic, with people looking to conserve cash in case of an emergency and others losing their regular source of income. Survey respondents have also cited making unbudgeted purchases as a common reason for using BNPL. We will soon see why this is very relevant to the credit decisioning process of banks that provide BNPL, in a bid to garner their share of the market.

According to research reports last year, over 50% of BNPL users in the UK were found to be millennials. For both millennials and Gen Z, credit cards are losing their luster. Many do not have the high credit scores and good credit history required to be approved for a credit card. These generations also appreciate instant gratification. The time-consuming application and approval process to get a credit card simply doesn’t appeal to them. By contrast, the approval of BNPL is quicker and at the point of sale, which is attractive to this target audience – a segment of consumers that traditional banks and merchants cannot afford to ignore.

The BNPL model of allowing repayment in installments with low or no interest, means that banks earn very little or nothing on paid up amounts. Their real earnings come from merchants who are willing to pay a higher commission for using a bank’s BNPL service, which in turn enables them to cater to a larger consumer base, including those who are not approved for a credit card. This makes it even more imperative for banks to join the BNPL revolution.

On the other hand, since research shows that many users opt for BNPL to buy items that are not within their budget, there is a very real possibility that they cannot afford to repay the installments. And, since many people who use BNPL are those with a thin credit history or no credit history, banks must implement an effective process for determining if they are eligible for BNPL.

Therefore, traditional banks face a dilemma. They can stick with providing existing loan and credit products using their tried and tested credit decisioning mechanism – this is less risky but results in losing a new revenue earning stream. Alternatively, they can become BNPL providers and move from a stringent and time-consuming credit decisioning process to a more relaxed and quicker decisioning strategy – this opens them up to the risk of defaulters but earns them a slice of the BNPL market.

So where does the balance lie?

Here are some strategies that traditional financial institutions can implement to help them formulate a good credit decisioning process for BNPL:

• While deciding on a strategy for credit decisioning, banks must consider countries where there are no credit bureaus to provide credit scores. Some countries also do not have an organized, centralized, and consistent way to access a customer’s financial details and payment behavior, for example, many countries in Africa and other emerging markets.
• Limiting the BNPL transaction amount will help lower the risk of a potential defaulter. When customers only enjoy credit to buy smaller ticket items, rather than large ticket items, it is easier for them to make the repayments. Banks also face a lower risk related to defaulting.
• Over time, customers with limits imposed on their BNPL credit spending, will build a credit history that banks can further analyze to determine if they should raise the credit limit or further restrict the user.
• If a customer already has a credit card, or a good credit history, banks can use that data to determine if the person is a good candidate for BNPL.
• Alternative credit scoring based on existing information about the applicant, such as their bank balance, e-commerce shopping history, data from their use of open banking applications, social media interactions, and more, could be a useful option for evaluating the BNPL credit worthiness of consumers with no credit history or low credit scores.
• Banks can monitor the inflow and outflow of funds in their open banking solutions, which can aid them in credit decisioning. Since in the open banking model, customers approve the sharing of their banking data, providers have access to detailed data about the customer’s transaction and credit use history.
• For faster credit decisioning, a bank can leverage the details of existing customers and their historic transactional data.
• Merchants too can provide valuable insights about their customers who have a long purchase history with them. This information on past purchases and payments can be used for credit decisioning. Merchants will find it in their best interests to share this data, as it could mean more BNPL approvals for customers, translating into more business for them.
• As it is essential that BNPL approval is quick, at the point of sale, the credit decisioning mechanism should not involve asking the applicant for extensive details.
• Artificial Intelligence (AI) and Machine Learning (ML) have proven applicability in traditional credit decisioning and can be leveraged for BNPL credit decisioning as well. A combination of AI and ML models used with a set of features can be leveraged to predict default risk and assess credit worthiness. While BNPL transaction data may not initially be available to configure, train and test the models, specific financial data of other loan products like credit cards can be used as a starting point, and these models can be refined as banks start gathering actual customer data of BNPL transactions.
• Any credit decisioning model used for BNPL approval or rejection, can be finetuned over time, as BNPL customers build a purchase and repayment history based on their BNPL transactions.

To conclude, the credit decisioning process that works well when approving credit cards and disbursing loans, is not ideal when it comes to BNPL. For BNPL, traditional banks must establish a more flexible credit decisioning mechanism based on a combination of factors, such as the value of a customer’s purchase, availability of current and historic customer data, the customer’s relationship with the bank (new or existing), the bank’s risk appetite, the markets they operate in, the purchasing and payment history with merchants, and their target audience. Implementing this change in credit decisioning will prove to be highly beneficial and profitable in the long term, as banks can gain their market share in the BNPL lending space, with the assurance of lowering their risk of defaulting BNPL customers.

Any traditional banks that are still on the fence about joining the BNPL revolution, must consider that the value of the global BNPL market is reported to reach close to $4 trillion by 2030, when compared to over $90 billion in 2020. The pandemic has already had a massive impact on the rising popularity of BNPL, making this payment method an opportunity ripe for the plucking – low hanging fruit that contains the seeds of long-term gains. It is therefore clear, that if banks are not all in, they will lose their share in an extremely lucrative and rapidly growing market.

Deepak Haria is a Senior Client Partner in the Synechron Payments business division. He has over 18 years of experience in the cards and payments domain, spanning application development, consulting, and delivery management. In his role, he assists customers with product feature assessments, product strategy, new product launches, innovative solutions, and the implementing of highly beneficial technology and business accelerators.
If you have any questions on payment-related technology services, you can email deepak.haria@synechron.com