Machine-learning algorithms are the future of credit scoring
The Credit2b team is excited to announce the launch of our Payment Outlook and Business Viability Indicators, two new credit scoring algorithms that perform at a greater than 80% accuracy of prediction. Why is this important? Let’s take a few steps back and understand how credit scoring works, and how data is traditionally processed to make smarter decisions.
We live in a world where machines can learn from large amounts of data over the course of time and make very clear, accurate predictions of events in the future. If a data analyst were to try to make predictions manually using the same data, it would take an infinitely long time to analyze all of it, and the final result wouldn’t be as accurate the computer-generated predictions.
In the world of financial credit, we help clients choose to do business with financially viable partners by using machine-learning which can rapidly summarize massive information sets to uncover trends that can be used for future decisions. For example, we know from experience that there are specific, identifiable patterns in financial statements during the two years before an organization declares bankruptcy or a few months before the same event. We can also know that machine-learning algorithms can detect financial distress in its earliest stages, and predict results that help our customers protect themselves from extending credit to businesses that may not be able to pay in the future.
The power of machine-learning algorithms
What makes machine-learning technology so powerful is that it works better when it is given more data with added complexity. Older data systems and scoring methods slow down when given more data, and are limited by their programming, analyzing the data the same way each time, reducing information to a simple score. Machine-learning software can become smarter, can develop a sophisticated understanding of the valuable connections between pieces of data, and can be adapted and updated anytime to keep up with the pace of business today.
From our experience within the credit space which dates back to 1906, we know that credit practitioners are typically confronted by two primary scenarios:
- The first one asks, “What is the near term risk of non-payment given that I need to ship product very shortly to support the sale?”
- In the second scenario, they ask, “How strong is this business financially so my organization can create a long term relationship with them as a customer?”
So why are the new scores we mentioned earlier so important? Because they give us more than 80% accuracy of prediction in both of those scenarios. In the case of the short-term horizon, we are using trade payment data, credit bureau data, collections alerts, peer remarks, and more to determine the short range likelihood that a company will go “beyond terms” defined by a set of data rules (e.g., 20% would be 60 days past due). In the long range view, we are able to use financial records, credit agency ratings and other business operations metrics to determine the likelihood they will be around in the long run. We call this the Business Viability Indicator, as it relies heavily on financials, but also incorporates other elements like ordering quantity trends, age of the business or scale of operations.
Machine-Learning and Artificial Intelligence (AI) tools and techniques are able to forecast a near term outlook and a long-term viability scenario with great precision using traditional financial data and non-traditional industry data. It is hard for human analysts alone to match this speed, efficiency and accuracy of data processing.
But unlike other credit industry scores from the bureaus or rating agencies, we introduced two more aspects in our scores to increase transparency, integrity and simplicity which are core to Credit2b’s philosophy.
Credit scoring made easy
In order to create credit scores that provide utility and value, Credit2b’s scores are on a scale of 0-100, with each point on this scale representing the probability of a positive outcome for the score. Therefore, a score of 80 simply means that there is an 80% probability that the company will pay on time for example. Other ratings agency scores are described in either tiers or bands that often cause confusion for credit practitioners who need to make important decisions quickly. With the tiered approach, two very similar companies may be scored in separate bands with completely different interpretations due to the randomness of the bands, and the simplicity of their data analysis algorithms. Machine-learning solutions deal with continuum, and give our customers information they can process and use quickly.
Additionally, this new launch gives businesses confidence in the quality and timeliness of the underlying data. In fact, we literally call it the “percent confidence”. Our customers now have the ability to make an informed decision depending on the freshness and richness of the available data for any business. This percent confidence is unique to the Billtrust-Credit2B score indicator. Obviously, any machine AI environment craves data, so we are always seeking more and better data sources to help give us the most accurate scores.
With the launch of our new scores, we have not only refined the use of newer AI techniques in scoring, but also been able to differentiate between two time financial horizons – both short-term and long-term. This is how our clients think, and it’s our mission to respond to their ever-changing needs.
About the Author:
With the recent acquisition of Credit2B by Billtrust, our blog will now feature product releases and information from the Credit2B team and its President, Shyarsh Desai. Shyarsh can be reached on LinkedIn.