Credit Decisions: API, Creditsafe & Automation

Post by 
Philip Kornmann

Kompar, one of our clients, is running a broker platform for business loans on the Swedish market. Small and medium businesses (SMEs) apply for a loan on Kompar’s platform and get an offer (or a decline) from +20 banks and lenders in 24 hours, some even within a few seconds. With such speed in the process, the user experience improves significantly, and so does the conversion rate. To make all of this possible, automating the process of collecting credit data through APIs has been key. One of these API services that we have helped Kompar to integrate with is Creditsafe, one of the largest players in the world when it comes to credit data on consumers and SMEs.

Understanding the data used for credit decisions

In the project of building an API connection to Creditsafe, getting an understanding of banks and lenders’ credit decision models and processes have been key. What specific parameters are being evaluated and how when a credit decision is made? Some metrics are more obvious than others, such as revenue level and growth, profitability, liquidity ratio, leverage ratio, net debt to EBITDA ratio, etc. However, when one starts going more into depth, one understands the complexity of doing SME lending. For example, it is important to understand how debt enforcement claims work; at what stage is a claim registered, and at what exact point of time is the data available? What happens if it is contested or resolved? And what effect does this have on the credit decision? In addition to debt enforcement claims on the business it is important to understand how debt enforcement works when it comes to consumers as well, as the board is being evaluated, and many more similar topics.

In the early part of the process of building an API connection for an underwriting process, some of the questions to think through are the following

  • What parameters are of interest to the credit decision-makers?
  • What does each specific parameter actually mean?
  • What is the mechanics behind each parameter? E.g. will the data change depending on what happens in reality? Or will it even be removed?

Is the data “fresh” enough for a credit decision?

Another important element to consider is how “fresh” the data is that is being used. Most credit bureaus as Creditsafe use a wide range of sources to collect their information. Some information is collected through the tax authorities, other is collected from the company house or the debt enforcement agencies. Here it is important to get an understanding of how often the data is being collected and what it is that triggers the collection (is it done by routine every month or does it only happen once a specific type of credit report is ordered?).

Setting up the API

Once we had established an understanding of the data, how it is collected, what it actually means, and most importantly what the lenders required in their credit decisions, we proceeded with building the API connection. The documentation from Creditsafe is provided in PDF format, making it key to double-check that the latest version is provided. They use a SOAP API and all the data is sent in an XML format. We build the API connection in a way that when Kompar receives the data, it is parsed using Node.JS from XML to JSON and saved in a NoSQL database running on Amazon Web Services. 

The API in a live process

The API call is triggered by a user (a business owner for instance) when it applies for a loan on the Kompar website. Creditsafe sends the data to Kompar, which then repackages the data and passes it to the Lenders, either through a Kompar-Lender API or by displaying in the Lender interface on the Kompar platform. Simultaneously, Kompar does similar API calls to other services collecting even more data and sending it to the lenders the same way. The lenders who have connected to Kompar’s Lender API access the required data to make an automated credit decision and send the result back within a few seconds.

Et voilá, the SME has been delivered a credit decision from multiple lenders within seconds from making an application.

If you would like to learn more about how to build an API connection to a credit bureau and how to make use of the data in real-time loan underwriting, reach out to us. We enjoy discussing the ins and outs of the tech as well as the impact it has on the user experience and the business results. 

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