This piece was co-authored by Max Wolke, head of strategy, and Christian Mangold, CEO, Fraugster. 

Calls about the demise of BNPL (Buy Now Pay Later) are premature, even if economic headwinds, poor performance on a number of key metrics, stricter regulation and Apple’s entrance into the space should be a cause for concern for established brands. It is an open secret that on a bad debt to receivables comparison major BNPL players are performing badly:  Afterpay (13.9%), Zip (9.7%), Klarna (8.1%) and Affirm (6.5%). The same metric for credit cards is around 2.5%. Expressed differently, on average for every $1 Bn of processing volume BNPLs write down $19.2 m of bad debt, major credit card schemes write down just $270k. 

Source: Financial Review (2022)

Bad debt impairment losses are eating into late fee revenue

Until recently,  BNPL providers have been able to offset bad debt impairments with lucrative late fees. 1 in 3 BNPL users in the UK missed a payment or made a late payment in 2021 according to the Citizens Advice Bureau, the proportions are similar across other European markets. Until a few months ago a number of analysts including Morgan Stanley were forecasting an expansion of late fee revenue from a baseline of 10% to over 13.5%. Given how lucrative late fees can be - many are equivalent to 25% interest per month - the market was right to be bullish.  But as competition to acquire new customers and scale operations has intensified credit losses for even the most established players like Klarna are increasing; they reported a 4x increase in losses up from 1.6Bn SEK ($160m) to 4.65 Bn SEK ($460m) YoY to the end of 2021.   

With global inflation continuing to rise in May - it is now above 8% in both the Eurozone and North America - and further interest rate hikes on the horizon, many consumers will be tightening their belts to manage rising cost of living expenses from utility bills to groceries and transport. This situation presents both opportunities and risks for BNPLs. Let’s start with the risks.

Belt tightening

Rising prices are already hitting consumers' disposable income. Global e-commerce transaction volumes are down in H1, leading a number of major e-commerce merchants to forecast slower growth rates for the second half of the year.  These numbers include popular BNPL verticals like consumer electronics and fashion. Simply put, lower consumer spending means a slowdown in BNPL revenues from merchant fees and fewer opportunities to cross sell and up sell. But there is another side to the coin. 

Opportunities to support consumers 

An increase in the cost of living presents BNPL brands with the opportunity to help consumers manage their cash flow during difficult times, helping them avoid the debt trap of revolving credit and high interest charges. Amongst higher income customer segments we in fact see an inverse trend, namely pent up demand for travel and tourism. Our data is supported by consumer sentiment: 72% of Europeans feel “really excited to travel” or “happy to travel” this year which represents a 14 point increase compared to 2021, according to a European travel insurer. To capitalise, BNPLs will need to get even sharper when it comes to making accurate credit risk assessments, especially for higher ticket items like flight tickets, hotels and tourism services. Fraugster data shows that AOV (average order value) is close to €600 and predominantly on credit cards, but BNPLs are well positioned to capture share by giving customers more flexible repayment terms. 

Source: Fraugster, May 2021- May 2022

How to unlock opportunity without increasing risk exposure 

BNPLs can unlock opportunity without increasing their exposure to loan defaults, or adding unwanted friction into the BNPL customer experience. Here are three ways they can do it:

1.    De-risk new customer approvals by accurately risk profile a customer that you don’t know 

BNPLs are now able to risk profile customers they have seen for the first time, without introducing unwanted friction into the buyer journey, and without exposing themselves to unacceptable levels of risk. How, you ask? By using advanced AI (artificial intelligence) that leverages insights from across a global network of e-commerce transactions. This allows BNPLs to match similar buyer profiles based on thousands of behavioural indicators. We recommend linking single transactions into graph networks to more accurately identify customers and their probability to pay, in real-time. A number of solutions exist. 

2.    Remove friction and increase approvals for returning customers

Recognise double the number of returning customers in real time by leveraging network intelligence and connecting the dots between hundreds of attributes using machine learning. This means you can treat new, existing and guest customers equally at the checkout and benefit from a significant revenue uplift through increased approvals. 

 3.    Reduce the risks of expanding into new regions and verticals 

Enrich the data you already use to make credit risk assessment with additional data points that have a high analytical value for determining true credit risk, here are a few examples:

• Positive transaction history : Helps BNPLs to better establish the customers ability to repay and provides richer insights into their personal financial management. By focusing on a positive signal, rather than just debt (like traditional credit bureau scores), you can make a more accurate assessment of true credit risk

• Total paid amount : Measures how much of total debt, including late fees has been repaid by the customer. This is different from just calculating outstanding balances, because it identifies high value customers who have a low likelihood of loan default but are more likely to accrue fees for late payments. These types of customers account for about 10-15% of BNPL revenues 

• Unpaid amounts / outstanding balances : Helps to quantify the uncertainty of the risk, for example, a high unpaid amount is a strong indicator that the customer might not be willing to repay the loan and therefore represents a higher risk. It also helps BNPLs to make a more accurate assessment of bad debt impairment if they decide to approve a transaction with an unpaid

In addition to the benefits outlined, there are sophisticated fraud attacks like synthetic identity fraud that need to be addressed and that can now be mitigated without resorting to time consuming manual reviews. (For those unfamiliar with this fraud type, this is where a fake identity is created from stolen credentials like national security, financial instruments and social media footprint to pass KYC checks). This form of sophisticated fraud is difficult to detect and has grown into a $20B problem with 25% of such losses coming from online lenders. We believe it is the one of the biggest fraud and risk threats facing the industry and a source of high material losses. Don’t get caught out. 

Make better business decisions

BNPL players can make better business decisions that move the dial on metrics like customer approvals and bad debts. “Our mission is simple,” says Chris Mangold, CEO of Fraugster, “we want our customers to feel confident that they can trust the person they are approving to repay the amount they are borrowing. The positive results we are already seeing with trial customers makes me confident that we can help the e-commerce ecosystem approve more customers without increasing exposure to loan defaults".