Did you ever have a lemonade stand as a kid? Imagine you’re selling lemonade on the side of the road. It’s a sweltering hot afternoon and all you want to do is join your friends at the local pool. Then, along comes a kid from the class above offering to buy a whole jug of lemonade. He doesn’t have any cash on him, but he says he’ll come back with full payment the next day. On the basis that he seems trustworthy, you accept.
Your ten-year old self just agreed to your first trade credit: a scenario in which a customer buys goods or services on account without paying cash up front. The issue is that whether you’re selling lemonade or consulting services, you need to understand the risk involved before agreeing to the transaction.
How is this risk calculated, and by whom? In trade credit, a seller extends credit to the buyer for a fixed period, and accepts the risk of the buyer defaulting—or obtains cover through an insurer like Allianz Trade.
The insurer, in turn, performs a thorough calculation of the risk, in order to offer the supplier protection and peace of mind. It has various tools at its disposal to do this.
Let’s look at an example of an auto part manufacturer that used to do business with some of Allianz Trade’s clients. As part of our underwriting process, we ran the company through a classic credit analysis. We looked at four dimensions of their business: organizational management and business strategy, profitability, liquidity and leverage, and cash flow. Then, we combined automated grading with human analysis to assign the company a weighted average risk rating, ranging from 1 (“exceptional”) to 10 (“failed”).
It’s a pretty straightforward process…that is, for the 1% of companies in the US that are public, meaning their financial statements are readily available. But to get a high-value analysis of these and the rest of the companies trading with our clients worldwide, we need to dig deeper.
One key to Allianz Trade’s methodology is combining classic credit analysis with an unmatched bank of data. Our expert teams are dedicated to procuring financial statements and sourcing other valuable information about buyers. Thanks to their investigative skills, we are able to obtain the financial statements of 15 - 20,000 private US companies each year. But this still leaves a large number of companies for which no information is readily available.
This is where the value of Allianz Trade’s proprietary data comes in. We complete our view by drawing on the data we collect continually from our expansive customer base of over 55,000 companies. This interconnected data universe of overdue invoices and debt in excess of a specified level is our biggest source of information, and our trump card when it comes to credit analysis.
Let’s get back to our buyer in the automotive industry. Following trade tensions in September 2018, the company was given a risk grade 7 (“substandard”). The following year, it reached an agreement with its debtholders to reduce its annual interest expense by $14M and infuse $40M into the company. But based on our procured data, Allianz Trade’ analysts were able to spot a number of red flags. These included not just a high debt load but also decreased demand, rising raw material costs and the hiring of advisors to assist with turnaround efforts.
In October 2019, we downgraded the buyer to a risk grade 9 (“uninsurable”) and recommended a full risk withdrawal for our clients. Eight months later, in June 2020, the company filed for bankruptcy. As a result of our analysis, our clients were able to stop trading with the company in time.
Thanks to our sourced data, Allianz Trade’s algorithms can flag initial risk factors and use them to generate a predictive risk grade. But our AI-powered assessments would not be nearly as solid if we didn’t have a dedicated and knowledgeable human team to complete the story.
By understanding every aspect of our clients’ markets and placing buyers within a greater economic context, we can make confident predictions about their financial outlook. Our combination of data and human analysis is indispensable to helping businesses thwart the threat of future losses.
