Beacon Score – Definition

Cite this article as:"Beacon Score – Definition," in The Business Professor, updated February 2, 2020, last accessed August 11, 2020, https://thebusinessprofessor.com/lesson/beacon-score-definition/.

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Beacon Score Definition

The Beacon score refers to a credit score that indicates the creditworthiness of individuals to borrowers. The Equifax Credit Bureau generates the Beacon scores which vary from person to person depending on the credit report of individuals. The scores are generated through a complex algorithm after a series of data is computed.

The Beacon score uses a 3-digit credit score to represent an individual’s creditworthiness. This credit score helps lenders to determine how much loan to offer a borrower and whether such borrower has the potential to pay back the loan without defaulting.

A Little More on What is a Beacon Score

There are three major credit bureaus in the United States, each of these bureaus develop their credit score using the same algorithm, and they assign different names to the credit score generated. The Beacon score is used by Equifax Credit Bureau, other bureaus are Transunion and Experian.

The credit score generated by these agencies is a three-digit score which range from 150-934 to depict the creditworthiness of an individual. Borrowers with high creditworthiness have higher credit scores while those with low or poor creditworthiness have the lowest scores.

Generally, there are various credit scores generated by different rating agencies, the score is often the same for one individual given that all these agencies use the same mathematical algorithm and credit information.

A typical lender will consider the creditworthiness of a borrower before issuing a loan. Lenders often depend on credit scores given by these agencies to have an insight into the credit history and report of individuals. All the three credit agencies have different methodologies they use in arriving at credit scores, this means there can be a variation of a credit score depending on the credit agency.

Equifax

The Equifax Credit Bureau is one of the three credit agencies in the United States, this agency uses Beacon and Pinnacle credit scores for credit reporting. Both credit scores use different methodologies to depict the creditworthiness of individuals. For instance, you can have Pinnacle 1, Pinnacle 2, Beacon 5.0 Bank Card, Beacon 09 Ban Card, and many others. Oftentimes, credit scores vary depending on the credit a borrower wants to take. Lenders who use the credit scores generated by Equifax have access to methods of calculating the credit scores and their variations.

Reference for “Beacon Score”

https://www.investopedia.com/terms/b/beacon-score.asp

https://en.wikipedia.org/wiki/Credit_score

realmortgagesolutions.ca/what_is_a_beacon_score.html

https://wallethub.com/edu/beacon-score/39288/

https://www.sapling.com › The Basics › Credit Cards

Academics research on “Beacon Score”

A decision support approach for accounts receivable risk management, Wu, D. D., Olson, D. L., & Luo, C. (2014). A decision support approach for accounts receivable risk managementIEEE Transactions on Systems, Man, and Cybernetics: Systems44(12), 1624-1632. Financial disasters in private firms led to increased emphasis on various forms of risk management, to include market risk management, operational risk management, and credit risk management. Financial institutions are motivated by the need to meet increased regulatory requirements for risk measurement and capital reserves. This paper describes and demonstrates a model to support risk management of accounts receivable. We present a decision support model for a large bank enabling assessment of risk of default on the part of loan recipients. A credit scoring model is presented to assess account creditworthiness. Alternative methods of risk measurement for fault detection are compared, and a logistic regression model selected to analyze accounts receivable risk. Accuracy results of this model are presented, enabling accounts receivable managers to confidently apply statistical analysis through data mining to manage their risk.

 

Enterprise risk management: small business scorecard analysis, Wu, D. D., & Olson, D. L. (2009). Enterprise risk management: small business scorecard analysis. Production Planning and Control20(4), 362-369. Enterprise risk management has become an important consideration in all aspects of business, including production planning. Business risk scorecards are important tools to monitor the performance of organisations. This article demonstrates the value of business scorecards as a means to monitor organisational performance with respect to risk management. A small bank credit loan case is used to make this demonstration. The relevance of small business scorecards to operations and supply chain management as a means to implement enterprise risk management is discussed.

Towards human control of robot swarms, Kolling, A., Nunnally, S., & Lewis, M. (2012, March). Towards human control of robot swarms. In Proceedings of the seventh annual ACM/IEEE international conference on human-robot interaction (pp. 89-96). ACM. In this paper we investigate principles of swarm control that enable a human operator to exert influence on and control large swarms of robots. We present two principles, coined selection and beacon control, that differ with respect to their temporal and spatial persistence. The former requires active selection of groups of robots while the latter exerts a passive influence on nearby robots. Both principles are implemented in a testbed in which operators exert influence on a robot swarm by switching between a set of behaviors ranging from trivial behaviors up to distributed autonomous algorithms. Performance is tested in a series of complex foraging tasks in environments with different obstacles ranging from open to cluttered and structured. The robotic swarm has only local communication and sensing capabilities with the number of robots ranging from 50 to 200. Experiments with human operators utilizing either selection or beacon control are compared with each other and to a simple autonomous swarm with regard to performance, adaptation to complex environments, and scalability to larger swarms. Our results show superior performance of autonomous swarms in open environments, of selection control in complex environments, and indicate a potential for scaling beacon control to larger swarms.

Balanced Scorecards to Measure Enterprise Risk Performance, Olson, D. L., & Wu, D. D. (2017). Balanced Scorecards to Measure Enterprise Risk Performance. In Enterprise Risk Management Models (pp. 133-144). Springer, Berlin, Heidelberg. Balanced scorecards are one of a number of quantitative tools available to support risk planning. A number of applications in production planning and control performance measurement are reviewed. Various forms of scorecards, e.g., company-configured scorecards and/or strategic scorecards, have been suggested to build into the business decision support system or expert system in order to monitor the performance of the enterprise in the strategic decision analysis. This chapter demonstrates the value of small business scorecards with a case from a bank operation.

 

MODELING THE EARLY SCREENING STAGE OF PRIVATE EQUITY DECISIONS, White, R. J., Hertz, G. T., & D’Souza, R. R. (2009). MODELING THE EARLY SCREENING STAGE OF PRIVATE EQUITY DECISIONS. In United States Association for Small Business and Entrepreneurship. Conference Proceedings (p. 2131). United States Association for Small Business and Entrepreneurship. Venture capital financing is one of the most studied subsets of private equity by entrepreneurship  researchers. Both the process and the criteria used by venture capitalists to make investment decisions have been examined.  Moreover, research has shown that such models may be valuable for both the entrepreneurs seeking funds and the investor. Building upon prior research, we  developed and tested a model for the early screening stage of venture capital funding. Our results suggest it is possible to successfully model the business opportunity screening stage of the  venture capital investment process. Implications of the model and the findings are offered.

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