Credit Scoring and Trade Secrecy: An Algorithmic Quagmire or How the Lack of Transparency in Complex Financial Models Scuttled the Finance Market
Brenda Reddix-Smalls
Posted Friday, March 3, 2017

In the field of business and finance, financial institutions utilize algorithms in complex mathematical models to create predictive risk models. These algorithmic models, or finance risk models, can be found throughout the financial marketplace to assist in credit decision problem solving. Notwithstanding their widespread use, finance risk models with their intellectual property proprietary protections lack transparency in their usage and insufficient regulatory control in their applications.

Finance risk models are used to predict risks, to compute probabilities in credit decision making outcomes, to predict marketplace movement of financial assets, to determine credit scores for individuals, and even to assess the worthiness of credit derivatives, or default probability of bundled mortgages for sale to investors. Particularly troublesome for the individual is the use of these predictive risk models in credit scoring. The trade secrecy surrounding credit scoring risk models, and the misuse of the models coupled with the lack of governmental control concerning their use, contributed to a financial industry wide recession (2007-2008). The lack of transparency and the legal environment led to the use of these risk models as predatory credit pricing instruments as opposed to accurate credit scoring predictive instruments. As a result, the combination of faulty credit scoring and trade secrecy laws helped to scuttle the financial marketplace of 2007-2008. Without a change in this financial and legal environment, these events could occur again. This article explores the use of finance risk models in credit scoring, the misuse of credit scoring models, and the lack of transparency and governmental regulation concerning credit scoring for the individual.