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B2B Credit Risk

What is B2B Credit Risk Management?

Many companies supply goods, loans, and services based on business and trade credit, either invoicing customers for payment at a later date or providing B2B loans. Business credit risk management assists companies with lending decisions based on a client’s financial health as well as other parameters that may indicate how likely they are to pay on time. Providing the right amount of credit will reduce the risk of late payments or defaults, which expose the vendor to financial risk.


Why Is It Important to Have a Good B2B Credit Risk Management Model?

The best way for lenders to understand the probability of a single loan to be repaid is credit risk modelling. The importance of it derives from the dynamic nature of the factors in every deal. A good B2B credit risk management model will not only mitigate risk but save a large amount of time and resources, helping companies provide credit at scale.

Keep in mind that, over time, the financial conditions can change, affecting the estimation. Therefore it’s imperative to have clear guidelines for retraining the model and updating the data, both internal and external.

What Internal Data Should I Have for a Good B2B Credit Risk Model?

In order to create a good credit risk management model, you should receive as much financial information as possible from the companies you are extending credit to, including back transactions, historic evaluations or loans, assets, and more. You should also obtain as much enrichable data as possible, including business emails, addresses, websites, and so on.

Best practices for credit risk management models consist of the following four credit risk components:

  1. Probability of Default (PD)
    –The likelihood that a borrower will default on debt over a one-year period
  2. Exposure at Default (EAD)
    –The expected default amount
  3. Effective Maturity (M)
    –Loan due date
  4. Loss Given Default (LGD)
    –The amount the lender would lose if a borrower defaults on payment. Factors that influence LGD include whether the lender has already partially paid off the loan, whether the lender holds anything in collateral, and so on.
    –The formula for calculating LGD is (EAD – PV(recovery) – PV(cost)) ÷ EAD

PV refers to the present value of a future sum. In other words, a certain amount of money will not be of the same value in the future due to factors like inflation in interest accrued if the money were invested. PV(recovery) refers to the present value of the amount recovered by the time of default. Finally, PV(cost) refers to the present value of the cost of the amount at the time of default.

What External Data is Essential for a Good B2B Credit Risk Model?

B2B credit risk management models use a variety of external and alternative data enrichments about companies:

  1. Fico/Credit Scores: companies like Equifax or Experian provide credit scores based on external data
  2. Company Registry Data: information on the age of a company and its current and recent status
  3. Company Online Presence Data: social presence, website traffic, and online activity by and about the company

What External Data May Prove Useful for a B2B Credit Risk Model?

There are different types of credit risk based on the loan type and other factors. However, a large number of alternative data sources remain useful for model building, including:

  1. Geospatial Data: foot traffic data as well as economic, demographic, and establishment data physically surrounding the company indicate the stability of a specific area or region in which the company operates
  2. Industry Data: any financial indication on the health of a company’s industry can be useful. For example, industry employment numbers, revenue and proceedings, even social media buzz
  3. Personal Data: usually company data forms the basis of B2B models but it also helps to include information on the business owner when considering lending to smaller companies

What Are the Main Challenges of the B2B Credit Risk Use Case?

Risk management and strategic planning go hand in hand; only through carefully planned risk taking will a business with a mix of products and services reach their goals. Assessing the credit risk of smaller business, however, remains one of the most challenging tasks in the financial sector. In this case, potential lenders contend with fragmented financial data, weak risk models, and lengthy processes. Moreover, they must contend with broader issues like the tension between sales and credit. Finally, the competitive lending environment, regulatory requirements, different geographies, and positions in the economic and credit cycles also have an impact.

Interesting Case Studies and Blogs to Look Into

IOSR Journal of Economics and Finance: Credit Risk Analysis & Modeling: A Case Study
Deloitte: Credit scoring Case study in data analytics

Tangible Examples of Impact

Tillful harnesses [Flowcast]’s unique, patent-pending machine learning (ML) offering that has been educated and validated with real-world information in conjunction with financial institutions (FIs).

The platform is safeguarded by “bank-level encryption,” according to the announcement, which noted that the offering is currently available to U.S. small- and medium-sized businesses (SMBs).

“In today’s rapidly changing environment, companies need a better way to know what lenders are looking for, monitor their financial health, and get the credit they need,” Flowcast CEO Ken So said in the announcement. “Tillful lets small businesses grow and thrive by leveling the playing field between business owners and lenders.”

PYMNTS: Flowcast Introduces Tillful For SMB Credit Risk Modeling

Relevant datasets

Home by Vendigi

by Vendigi

Home by Vendigi provides audience data for all things home buyers, remodelers, and sellers. Their data comes from first-party sources like top multiple listing systems (MLSs) major brokers like RE/MAX, Coldwell Banker, Century 21, and Sotheby’s.

Users of Vendigi’s Home data range from home and garden retailers to insurance institutions to telecom companies.

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Distil Networks Data Risk Analytics

by Distil-Networks

Distil Networks Data Risk Analytics monitors suspicious data activity and alerts businesses to possible threats using data analytics

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Turkrating Services Rating List

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Turkrating Services Rating List provides detailed credit data on Turkish institutions and debt instruments, in both the long- and short-term

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Pakistan Credit Ratings Agency Data


Pakistan Credit Ratings Agency Data ranks the financial strength of brokerages, projects, insurers, sukuk bonds, & entire industries

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Pacific Credit Rating Data


Pacific Credit Rating Data provides financial ratings and corporate ESG reports for companies in Central and South America

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