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Alternative Data Transforming B2B Credit

The creation of digital data doubles each year and is expected to continue through 2020. Within this vast array of data are pieces of information that, when combined, offer insights into the viability of a company that can’t be found through traditional methods. This alternative data, coupled with Artificial Intelligence and Machine Learning, is giving rise to new models to determine a company’s credit worthiness in 2018.

Today most businesses rely solely on financial data from traditional providers like D&B, Lexis Nexis, or a credit bureau to determine if a line of credit or terms will be extended to a customer. Traditional data gives you a look in the rearview mirror and can be a predictor of future performance – but it doesn’t provide the whole picture or key indicators that may outline the true trajectory of a company’s financial health and performance.

Blending traditional financial data with alternative data allows businesses to better assess the credit worthiness of its customers. For example, if you wanted to lend to a business with good credit based on traditional data, then saw numerous complaints about a series of product recalls on Twitter, you might reconsider. If you were on the fence about lending to a startup with average credit, but looked at their social profiles and found on Facebook that they just secured five Fortune 100 clients, LinkedIn displayed 10 new positions, and TechCrunch reported they received another round of funding, this alternative data might tip the scales in favor of offering them a line of credit.

Businesses can acquire a lot of real-time information from sources that didn’t exist 10 years ago and alternative data is likely to be more current than financial data. Think about how long it takes for a poor product release or the acquisition of a new customer to make its way into the financial performance of a company. By using traditional financial data and cross-correlating it with social media information, we can access highly-relevant, timely data that provides an accurate context for making smarter credit and risk decisions.

A fundamental piece to building and scaling payments and commerce businesses is getting the necessary data models and integrations to successfully assess credit and risk in real-time. Thus, machine learning has emerged as a specific implementation of artificial intelligence throughout the credit lifecycle, from underwriting to payment application and credit line management. With machine learning, both static data and real-time performance data is passed to algorithms that then automatically recommend adjustments to underwriting score cards, run champion/challenger tests, and proactively adjust credit lines as good payers get close to their credit line. A single proactive credit line increase can avoid customer frustration, a call to support, and a painful emergency process to manually approve the request. The more fine-tuned the credit decisioning process, the faster the business.

Extending credit is a competitive advantage. It isn’t simply a way to establish how a customer will pay, it tips the scales and increases the likeliness a customer will purchase and the amount and frequency in which they will continue to purchase.

The bottom line: credit is the spark that ignites the entire customer journey. Businesses using a blended approach to determine customers credit worthiness will have a distinct advantage over their competitors and will build stronger, more loyal relationships with customers from day one.


By Brandon Spear, President

Originally posted in PYMNTS 2018 eBook.

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