1,354,178 research outputs found
Collections policy comparison in LGD modelling
No full textThis paper discusses the similarities and the differences in the collection process between in house and 3rd Party collection. The objective is to show that although the same type of modelling approach to estimating Loss Given Default (LGD) can be used in both cases the details will be significantly different. In particular the form of the LGD distribution suggests one needs to split the distribution in different easy in the two cases as well as using different variables. The comparisons are made use two data sets of the collections outcomes from two sets of unsecured consumer defaulters<br/
Comparing debt characteristics and LGD models for different collections policies
No full textThis paper discusses the similarities and differences in the collection process between in-house and 3rd party collection. The objective is to show that, although the same type of modelling approach to estimating the Loss Given Default (LGD) can be used in both cases, the details will be significantly different. In particular, the form of the LGD distribution suggests that one needs to split the distribution in different ways in the two cases, as well as using different variables. The comparisons are made using two data sets of the collection outcomes from two sets of unsecured consumer defaulter
Application of survival analysis to cash flow modelling for mortgage products
No full textIn this article, we describe the construction and implementation of a pricing model for a leading UK mortgage lender. The crisis in mortgage lending has highlighted the importance of incorporating default risk into such pricing decisions by mortgage lenders. In this case the underlying default model is based on survival analysis, which allows the estimation of month-to-month default probabilities at a customer level. The Cox proportional hazards estimation approach adopted is able to incorporate both endogenous variables (customer-specific attributes) and time-covariates relating to the macro-economy. This allows the lender to construct a hypothetical mortgage portfolio, specify one or more economic scenarios, and forecast discounted monthly cash flow for the lifetime of the loans. Monte Carlo simulation is used to compute different realisations of default and attrition rates for the portfolio over a future time horizon and thereby estimate a distribution of likely profit. This differs from a traditional scorecard approach in that it is possible to forecast default rates continually over a time period rather than within a fixed horizon, which allows the simulation of cash flow, and differs from the company's existing pricing model in incorporating the possibilities of both default and early closure
Modelling LGD for unsecured personal loans: decision tree approach
No full textThe Basel New Accord which is being implemented throughout the banking world on1 January 2007 has made a significant difference to the use of modelling withinfinancial organisations. In particular it has highlighted the importance of Loss GivenDefault (LGD) modelling.We propose a decision tree approach to modelling LGD in the consumer credit areaand using real data from the financial organisation in UK model the components thatmake up this tre
Modelling LGD for unsecured personal loans: decision tree approach
No full textThe New Basel Accord, which was implemented in 2007, has made a significant difference to the use of modelling within financial organisations. In particular it has highlighted the importance of Loss Given Default (LGD) modelling. We propose a decision tree approach to modelling LGD for unsecured consumer loans where the uncertainty in some of the nodes is modelled using a mixture model, where the parameters are obtained using regression. A case study based on default data from the in-house collections department of a UK financial organisation is used to show how such regression can be undertaken
Application of survival analysis to cash flow modelling for mortgage products
No full textIn this article we describe the construction and implementation of a pricing model for a leading UK mortgage lender. The crisis in mortgage lending has highlighted the importance of incorporating default risk into such pricing decisions b y mortgage lenders. In this case the underlying default model is based on survival analysis, which allows the estimation of month-to-month default probabilities at a customer level. The Cox proportional hazards estimation approach adopted is able to incorporate both endogenous variables (customer specific attributes) and time-covariates relating to the macro-economy. This allows the lender to construct a hypothetical mortgage portfolio, specify one or more economic scenarios, and forecast discounted monthly cashflow for the lifetime of the loans. Monte Carlo simulation is used to compute different realisations of default and attrition rates for the portfolio over a future time horizon and thereby estimate a distribution of likely profit. This differs from a traditional scorecard approach in that it is possible to forecast default rates continually over a time period rather than within a fixed horizon, which allows the simulation of cashflow, and differs from the company's existing pricing model in incorporating the possibilities of both default and early closur
Bayesian models of car lease frauds
No full textIn this research, we focus on fraud modelling for car leases. The research objective is to investigate whether it is possible to improve the performance of such models by using additional knowledge derived from the area of fraud modelling for car loans. Two logistic regression models are developed for this purpose. The first model only uses the data on car leases, whereas the second model also incorporates the additional knowledge. The performance of the two models is compared ex post to assess the improvement achieved by the incorporation of this extra knowledge. To formally include the additional knowledge, the models are developed in the Bayesian framework, where the prior information is described with the prior probability distributions of the model parameters. The research is based on real-life data included in two large datasets provided by a bank that operates in Europe. The car lease dataset is used to build the models, whereas the car loan dataset is the source of the prior information, i.e. the prior distributions of some parameters (non-informative priors are adopted for other model parameters). The obtained results show to what extent such prior information can help improve the performance of fraud models for car leases. Generally, the research demonstrates how to enrich fraud models by using additional knowledge and Bayesian methods
Modelling repayment patterns in the collections process for unsecured consumer debt: a case study
No full textOne approach to modelling Loss Given Default (LGD), the percentage of the defaulted amount of a loan that a lender will eventually lose is to model the collections process. This is particularly relevant for unsecured consumer loans where LGD depends both on a defaulter's ability and willingness to repay and the lender's collection strategy. When repaying such defaulted loans, defaulters tend to oscillate between repayment sequences where the borrower is repaying every period and non-repayment sequences where the borrower is not repaying in any period. This paper develops two models – one a Markov chain approach and the other a hazard rate approach to model such payment patterns of debtors. It also looks at simplifications of the models where one assumes that after a few repayment and non-repayment sequences the parameters of the model are fixed for the remaining payment and non-payment sequences. One advantage of these approaches is that they show the impact of different write-off strategies. The models are applied to a real case study and the LGD for that portfolio is calculated under different write-off strategies and compared with the actual LGD results
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