Adjusting the credit lines of card users is a vital undertaking. It is crucial to establish an effective approach for credit card organizations in order to find out the proper amount of credit to advance to clients. Most of the related research tends to focus on the estimation of credit card users’ default. A holistic and heuristic strategy that explores the credit line that maximizes the net profits of the credit card companies is fundamental. For example, regression models can be employed to find the probability of default of customer and customer’s current balance as a function of credit line. The outcomes are then used to estimate the net profit. A rigorous research study can significantly contribute towards a viable strategic guideline in the management of credit lines for firms that deal with cards. Even though the concept of functional integration has been embraced across the board, little progress has been registered when it comes to credit employment. Some of the factors that have contributed towards the latter poor score in credit include limited understanding of the concept of credit, declining market performance, and lack of effective objectives that can be pursued by an organization.
Management of Risks and Credit in organizations
Credit service, therefore, creates an identifiable value which might be called “possession utility[1].” Such utility is not limited to the field of consumer buying, but is also involved in mercantile transactions and in lending.[2] Little Interest in Credit Work
The notions that “sellers give credit,” that credit service is not distinct from the commodities to which it is applied, that credit service has no value, and that the offering of credit service is primarily a finance function—^these are fallacies which have hindered the development of credit theory and practice[3].
For many years credit was regarded as a un- productive business activity, and the position of Credit manager was at a much lower level than other marketing roles.[4] Generally credit management has been assigned to the finance and bookkeeping departments and has been concerned mainly with allocation and utilization of working capital, turn-over of receivables, sources of long-term and short-term funds for carrying receivables, credit loss ratios, cash discount tactics, economic indicators of the quality of receivables, and the like.[5]
Anecdotal evidence on credit scoring has pointed to possible manipulation that may increase the credit scores of borrowers without any real improvements in their creditworthiness (see, e.g., Foust and Pressman 2008)[6]. In theory, score manipulation has minimum impact in terms of our metric if its occurrence were to be uniformly distributed. However, this is unlikely: A more probable scenario is one in which manipulation is more likely to occur at low levels of credit scores[7]. Moreover, most anecdotal accounts argue that such manipulation increased over time. Therefore, if credit score manipulation affects default rates, it is most likely to be reflected in our results at low levels of FICO and for later cohorts[8]. More importantly, evidence of manipulation of credit scores should be reflected in anomalous behavior in terms of our parametric measure – a measure that controls for other characteristics on the origination[9]. However, the evidence shows the opposite: parametric measures of FICO performance show improvement at all levels of FICO. This result is fairly robust and holds true for multiple variations of credit score groupings. In light of this, we conclude that the evidence from our data does not reflect any anomalous behavior that would suggest that such manipulation was widespread. That is not to say that such instances of manipulation did not occur, but simply that given our large sample size, score manipulation would have to be fairly widespread to affect our results.[10]
Portfolio guidelines and investment policy statements allow investors to communicate to portfolio managers their overall returns objectives and appetite for risk. By necessity, these guidelines need to be general enough to cover a wide range of possible assets and communicate objectives in a relatively concise way.[11] Standardized tools have helped to facilitate this communication and for fixed income portfolios, the most common of these is credit ratings.[12] Cantor et al. (2007) document that close to 80% of portfolio managers and fund sponsors explicitly rely on credit ratings in their portfolio guidelines[13].
In this paper we explore the mapping between rating categories assigned to a bond at issue and the yield that is ultimately required by investors. In particular, we ask whether there are industry differences in the yields required for bonds that are assigned the same credit rating[14]. Portfolio guidelines, financial regulations, and rating agencies themselves generally make no distinction across industries, treating a bond rated “A” as bearing the same amount of risk as a similarly rated bond, regardless of the issuer’s industry[15]. Yet, if ratings are noisy or imperfect assessments of how investors view the risks of a This is generally the case for both issue and issuer ratings, however there are a small number of cases in which a rating agency may provide a separate category of rating that is particularly relevant for firms in that industry. Examples include ratings with the designation “F” issued by S&P to assess the creditworthiness of a fixed income portfolio or “Insurer Financial Strength” ratings designed to assess the ability of an insurer to meet policy obligations rather than debt obligations[16]. Particular industry and systematic differences may exist in the yield required for bonds within the same rating category[17]. These differences may allow a portfolio manager to introduce higher yielding bonds into their holdings while remaining within their set rating constraints. As suggested by Becker and Ivashina (2013) this additional yield may be particularly attractive during extended periods of low interest rates.[18] The specific industry comparison we focus on in this paper is the yield on bonds issued by financial institutions versus non-financial firms.[19] We are motivated to focus on the finance industry for several reasons. First, financial firms are frequent issuers of debt.
[1] Robert, Bartels. “Credit management as a marketing function.” The Journal of Marketing (1964): 59-61
[2] Rajdeep, Sengupta, , and Bhardwa,j Geetesh. “Credit Scoring and Loan Default.” International Review of Finance (2015).
[3] Ibid 1, pp 59
[4] Lynnette, Purda., Sonmez, , Fatma and Zhong, Ligang. “Financial Institution Credit Assessment and Implications for Portfolio Managers.” Journal of International Financial Markets, Institutions and Money (2015).
[5] Young Sohn, Taek , Kyong, and Ju, Yonghan. “Optimization strategy of credit line management for credit card business.” Computers & Operations Research 48 (2014): 81-88.
[6] Ibid 2, pg 60
[7] Ibid 3, p.60
[8] Ibid 4, p.60
[9] Robert, Bartels. “Credit management as a marketing function.” The Journal of Marketing (1964): 59-61
[10] Robert p. 61
[11] Rajdeep, Sengupta, , and Bhardwa,j Geetesh. “Credit Scoring and Loan Default.” International Review of Finance (2015).
[12] Young Sohn, Taek , Kyong, and Ju, Yonghan. “Optimization strategy of credit line management for credit card business.” Computers & Operations Research 48 (2014): 81-88.
[13] Ibid 1, pg 60
[14] Lynnette, Purda., Sonmez, , Fatma and Zhong, Ligang. “Financial Institution Credit Assessment and Implications for Portfolio Managers.” Journal of International Financial Markets, Institutions and Money (2015).
[15] Ibid 1, pg 159
[16] Ibid 1, pg 161
[17] Ibid 1, pg 162
[18] Young Sohn, Taek , Kyong, and Ju, Yonghan. “Optimization strategy of credit line management for credit card business.” Computers & Operations Research 48 (2014): 81-88.
[19] Robert, Bartels. “Credit management as a marketing function.” The Journal of Marketing (1964): 59-61
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