Raleigh, NC, January, 2013- As part of the development of the Sageworks Probability of Default Model, industry leading academics were asked to peer review and validate the model and the methodology used to determine the data set and the variables.
Professor Peter J. Nigro, Ph.D., of Bryant University reviewed the Sageworks business-only and global probability of default models and their applications for assessing credit risk for small and medium sized businesses. Prior to joining Bryant University, Professor Nigro was a Senior Financial Economist with the Office of the Comptroller of the Currency for over a decade. Professor Nigro's research spans the fields of commercial banking, small firm finance, credit risk, mortgage finance and mutual funds:
“Over the past twenty years, credit scoring has revolutionized consumer lending in the financial services industry. Sageworks’ automated credit scoring model employing proprietary data continues this transformation by quantifying the credit risk associated with private-company commercial loan performance. The Sageworks probability of default model, which includes a business-only as well as a global version, promotes efficiency in the loan approval process, reduces subjectivity in the loan approval process, and incorporates both firm and individual attributes to assess small business credit risk.Historically, the primary impediment to credit scoring for private company credit risk has been the lack of adequate data. Sageworks’ extensive private-company financial statement database and relationships with community banks across the country, however, have permitted the development of statistically valid, small firm scoring models. Sageworks has developed unmatched customized scoring models that incorporate both the financial characteristics of businesses and their owners. Specifically, Sageworks’ default models are based on over 22,000 financial statements from over 6,600 businesses distributed across industries and sales ranges within the United States.
Proprietary data spanning industries, time and geographies was only one of the ingredients for a successful Sageworks model. The Sageworks development team also spent tremendous time and effort in the two most important aspects of model development: variable reduction and variable analysis. First, Sageworks successfully narrowed a large number of potential variables for inclusion into the model to a smaller number that could be more easily analyzed in a multivariate setting. Second, the development team conducted extensive variable analysis that investigated not only the ability of a variable to predict default, but also its relationship with other variables in the model. Specifically, from an initial list of 76 financial statement variables, including those for the operating business only and those for the global entity (business and its owners), eight variables for business financials and two variables for personal financials were chosen using a factor analysis process. Finally, Sageworks’ development team did extensive out of sample testing to validate the construction of their scoring models and their performance compared to other, publicly available models. Sageworks’ default prediction models contain an intuitive yet diverse set of factors that accurately predict the default behavior of small, private companies. The model compares very favorably with industry leading models and puts Sageworks on the cutting edge of credit risk management.”
Peter J. Nigro, Ph.D.
Bryant University, Professor of Finance, Sarkisian Chair in Financial Services
About Sageworks, Inc.
Raleigh, NC-based Sageworks, Inc. is a financial information company and provider of the Business Credit Report, which assesses business credit risk. Sageworks’ data and applications are used by thousands of banks and accounting firms across North America. The company has been named to the Inc. 500 list of the fastest growing privately held companies in the U.S. and to the Deloitte Technology Fast 500.