Random Survival Forests Models for SME Credit Risk Measurement

Fantazzini, Dean; Figini, Silvia

doi:10.1007/s11009-008-9078-2

Cited by 97 publications

(54 citation statements)

References 26 publications

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“…Peel and Wilson (1989) estimate a multi-logit model that identifies 'distressed acquisitions' as an important outcome from bankruptcy situations. Fantazzini and Figini (2008) propose a non-parametric approach based on Random Survival…”

Section: Introductionmentioning

confidence: 99%

The value of non-financial information in SME risk management

Altman¹,

Sabato²,

Wilson³

2010

JCR

299

302

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Within the commercial client segment, small business lending is gradually becoming a major target for many banks. The new Basel Capital Accord has helped the financial sector to recognize small and medium sized enterprises (SMEs) as a client, distinct from the large corporate. Some argue that this client base should be treated like retail clients from a risk management point of view in order to lower capital requirements and realize efficiency and profitability gains. In this context, it is increasingly important to develop appropriate risk models for this large and potentially even larger portion of bank assets. So far, none of the few studies that have focused on developing credit risk models specifically for SMEs have included non-financial information as predictors of the company credit worthiness. For the first time, in this study we have available non-financial, regulatory compliance and 'event' data to supplement the limited accounting data which are often available for non-listed firms. We employ a sample consisting of over 5.8 million sets of accounts of unlisted firms of which over 66,000 failed during the period 2000-2007. We find that qualitative data relating to such variables as legal action by creditors to recover unpaid debts, company filing histories, comprehensive audit report/opinion data and firm specific characteristics make a significant contribution to increasing the default prediction power of risk models built specifically for SMEs.JEL classification: G33, G32, M13

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Section: Introductionmentioning

confidence: 99%

The value of non-financial information in SME risk management

Altman¹,

Sabato²,

Wilson³

2010

JCR

299

302

View full text Add to dashboard Cite

show abstract

“…Other studies using a variety of statistical techniques, have contributed to the knowledge of the insolvency indicators, both financial ( [9], [3]) and non-financial ( [14], [2]) that arise in SMEs. In particular, ( [12]) propose a nonparametric survival approach with a random-forest model, but they also conclude that a simpler logit model outperforms the random-forest model in the out-of-sample validation.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Scoring Models for Trade Credit in Small and Medium Enterprises

Terrádez

Kizys

Juan

et al. 2015

Springer Proceedings in Mathematics &Amp; Statistics

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Trade credit refers to providing goods and services on a deferred payment basis. Commercial credit management is a matter of great importance for most small and medium enterprises (SMEs), since it represents a significant portion of their assets. Commercial lending involves assuming some credit risk due to exposure to default. Thus, the management of trade credit and payment delays is strongly related to the liquidation and bankruptcy of enterprises. In this paper we study the relationship between trade credit management and the level of risk in SMEs. Despite its relevance for most SMEs, this problem has not been sufficiently analyzed in the existing literature. After a brief review of existing literature, we use a large database of enterprises to analyze data and propose a multivariate decision-tree model which aims at explaining the level of risk as a function of several variables, both of financial and non-financial nature. Decision trees replace the equation in parametric regression models with a set of rules. This feature is an important aid for the decision process of risk experts, as it allows them to reduce time and then the economic cost of their decisions.

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“…Note that this resulted in data sets with either only personal loans or data sets with a mix of personal and small enterprise loans, for banks C and D. As the SMEs (small and medium-sized enterprises) in our data sets were all sole proprietorships, their properties were nearly identical to those of personal loans. More information on the use of survival models in SMEs in the broader sense can be found in, among others (Fantazzini and Figini, 2009;Gupta et al, 2015;Holmes et al, 2010). For the banks with data covering several loan terms, the data were split in order to get only one loan term per data set, resulting in ten data sets.…”

Section: Data Preprocessing and Missing Inputsmentioning

confidence: 99%

Time to default in credit scoring using survival analysis: a benchmark study

Dirick

Claeskens

Baesens

2017

Journal of the Operational Research Society

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We investigate the performance of various survival analysis techniques applied to ten actual credit data sets from Belgian and UK financial institutions. In the comparison we consider classical survival analysis techniques, namely the accelerated failure time models and Cox proportional hazards regression models, as well as Cox proportional hazards regression models with splines in the hazard function. Mixture cure models for single and multiple events were more recently introduced in the credit risk context. The performance of these models is evaluated using both a statistical evaluation and an economic approach through the use of annuity theory. It is found that spline-based methods and the single event mixture cure model perform well in the credit risk context.

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Random Survival Forests Models for SME Credit Risk Measurement

Cited by 97 publications

References 26 publications

The value of non-financial information in SME risk management

The value of non-financial information in SME risk management

Risk Scoring Models for Trade Credit in Small and Medium Enterprises

Time to default in credit scoring using survival analysis: a benchmark study

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