Operations research in consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance- and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these are to developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer financ

Consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

Research on Pre-loan Risk Management Problems of Personal Consumption Credit in Commercial Banks ---- Study of the Application of Multi-level Fuzzy Comprehensive Evaluation Model in X Bank Xiamen Branch

改革开放以来，中国经济持续飞速增长，GDP多年持续稳定保持增长率在6%以上。对GDP起到推动作用的主要来源于消费、投资及进出口，但由于投资与进出口的发展较为成熟，消费逐渐成为经济发展的重要动力。与此同时，个人消费贷款增长迅猛，已经成为银行利润突破的关键点。对个人消费贷款业务的风险管理不但有利于银行经济的发展，而且有助于银行控制信贷业务中存在的风险，制定一套有效的个人消费信贷的贷前风险管理流程，研究如何提高银行风险管理水平，具有较强的现实意义。 为了对个人消费信贷贷前风险管理进行研究，本文首先对国内外文献进行综述，并对相关概念进行梳理；其次，对X银行厦门分行个人消费信贷风险管理现状和存在的问题...Since the reform and opening up, China's economy has continued to grow at a rapid rate, and its GDP has maintained steady growth of over 6% for many years. Consumption, investment, import and export are the main drivers of GDP. However, due to the mature development of investment, import and export, consumption has gradually become an important driving force for economic development. At the same t...学位：金融硕士院系专业：经济学院_金融硕士学号：1562015115288

Estimation of loan portfolio risk on the basis of Markov chain model

A change of shares of credits portfolio is described by Markov chain with discrete time. A credit state is determined on as an accessory to some group of credits depending on presence of indebtedness and its terms. We use a model with discrete time and fix the system state through identical time intervals - once a month. It is obvious that the matrix of transitive probabilities is known incompletely. Various approaches to the matrix estimation are studied and methods of forecast the portfolio risk are proposed. The portfolio risk is set as a share of problematic loans. We propose a method to calculate necessary reserves on the base of the considered model. © 2013 IFIP International Federation for Information Processing.German Sci. Found. (DFG) Eur. Sci. Found. (ESF);Natl. Inst. Res. Comput. Sci. Control France (INRIA);DFG Research Center MATHEON;Weierstrass Institute for Applied Analysis and Stochastics (WIAS);European Patent Offic

Semi-Markov credit risk modeling for a portfolio of consumer loans in the Kenyan banking industry

Paper presented at the 11th African Finance Journal Conference, Durban, South Africa.Based on simulations of implied values for credit worthiness over a period of 5 years for 1000 consumers, we establish a case for the semi-markov models as a proxy for internal credit risk models for a portfolio of consumer loans. With ample calibration, we prove the robustness of the semi-markov models in forecasting probabilities of default and loss given default. With a view of credit risk as a reliability problem, we generate credit risk indicators as qualifications of adequacy of a loan portfolio. This informs prospective holding of capital based on forecast delinquencies as opposed to the current retrospective practice that relies on the trigger event of default. We use Monte-Carlo simulation techniques to generate consumer ratings and adopt this to the Merton model to derive the initial probability transition matrix. Initial consumer rating is in accordance with industry practice using a credit score sheet backed by the logit model. The banking credit function could espouse the study results to fulfill regulatory credit risk capital requirements for consumer loans in line with the Central Bank of Kenya Prudential Risk Guidelines or banks in other jurisdictions compliant with the Basel banking framework.Based on simulations of implied values for credit worthiness over a period of 5 years for 1000 consumers, we establish a case for the semi-markov models as a proxy for internal credit risk models for a portfolio of consumer loans. With ample calibration, we prove the robustness of the semi-markov models in forecasting probabilities of default and loss given default. With a view of credit risk as a reliability problem, we generate credit risk indicators as qualifications of adequacy of a loan portfolio. This informs prospective holding of capital based on forecast delinquencies as opposed to the current retrospective practice that relies on the trigger event of default. We use Monte-Carlo simulation techniques to generate consumer ratings and adopt this to the Merton model to derive the initial probability transition matrix. Initial consumer rating is in accordance with industry practice using a credit score sheet backed by the logit model. The banking credit function could espouse the study results to fulfill regulatory credit risk capital requirements for consumer loans in line with the Central Bank of Kenya Prudential Risk Guidelines or banks in other jurisdictions compliant with the Basel banking framework

Determinants of default in P2P lending

This paper studies P2P lending and the factors explaining loan default. This is an important issue because in P2P lending individual investors bear the credit risk, instead of financial institutions, which are experts in dealing with this risk. P2P lenders suffer a severe problem of information asymmetry, because they are at a disadvantage facing the borrower. For this reason, P2P lending sites provide potential lenders with information about borrowers and their loan purpose. They also assign a grade to each loan. The empirical study is based on loans'' data collected from Lending Club (N = 24, 449) from 2008 to 2014 that are first analyzed by using univariate means tests and survival analysis. Factors explaining default are loan purpose, annual income, current housing situation, credit history and indebtedness. Secondly, a logistic regression model is developed to predict defaults. The grade assigned by the P2P lending site is the most predictive factor of default, but the accuracy of the model is improved by adding other information, especially the borrower''s debt level

Modelo de cálculo de capital económico por riesgo de crédito para portafolios de créditos a personas físicas

This paper discusses a new methodology to estimate the economic capital by credit risk for a retail portfolio based on the general concepts of copula and dependence measures as well as some core results of the Extreme Value Theory (EVT). The superiority of the proposed approach over the traditional estimation techniques is demonstrated in the application of Elliptical Generalized copulas and Grouped copulas of the t Student type to model the dependence structure of the risk parameters PD, EAD and LGD. Furthermore, the POT method is used to analyze the extreme losses behavior.Capital Económico, Riesgo de Crédito, Cópulas, Valores Extremos

Estimating expected lifetime of revolving credit facilities in an IFRS 9 framework

This paper sets out to estimate expected lifetime of revolving credit facilities (e.g. credit card products) and is motivated by the introduction of the International Financial Reporting Standard 9 (IFRS 9) and its requirements for loan impairments. The reporting entity is required to estimate lifetime expected credit losses for certain nancial instruments. In practice, maximum contractual period for revolving credit facilities cannot be used in dening lifetime for the facility and credit risk mitigation actions need to be considered. A data set for a retail credit card portfolio was provided by a Nordic bank and for the lifetime denition derived, a model based on a conditional Markov chain was selected. Expected lifetime was estimated and an analytical expression for expected lifetime of revolving credit facilities was derived and validated.What period should credit losses be estimated over in IFRS 9? When the standard on how to account for credit losses moves to an expected loss approach, there is a need to find out how far into the future to look for losses. This is known as the expected lifetime, and I have dived into interpreting how the IFRS 9 standard can be implemented in a model for expected lifetime for credit cards and similar instruments. I propose and validate a model, along with a methodology for estimation. The Great Financial Crisis had a grand effect on most economies. Banks were assessed and it was recognized that in order to reduce effects of future downturns, provisions that were more forward-looking and higher were needed for credit losses. IFRS 9 is the answer to this (for reporting purposes) and the provisioning based on expected credit losses (rather than incurred) is the ingredient enabling this. A relevant consideration in this context is the expectation of lifetime for the instrument (e.g. a credit card) and since the phase of IFRS 9 concerning provisioning for loan losses is recently published, how to measure expected lifetime is not yet established. For this purpose, I introduce a concept called “End of lifetime event”. This concept brings together a common form of credit risk model for credit card portfolios and similar (where risk rating is based on how late the borrower is on a payment) with events related to expectations of how this type of instrument is managed. The result is a list of End of lifetime events that will function as absorbing states in the Markov chain implemented to estimate lifetimes. The Markov chain is a popular form of model, suitable for this task, both in its connection to common credit risk models and broad applicability. The selection methodology is based on testing if different candidate models possess the Markov property (that the distribution of future state depends on the past only in the present state), and finding a good trade-off between complexity (as low as possible) and accurate modelling of the data. For this, two statistical tests are used and a first-order Markov chain is selected, shown to be dependent on maximum historical risk of a borrower (which is reasonable, since this would explain a lot about a borrower beyond how late he or she currently is on a payment). The parameters for all allowed transitions in this extended model are estimated based on data from a portfolio of credit cards provided by a bank, where transitions between internal risk ratings (or states) were used for estimating the transition probabilities in the model. It is convenient at this point that a Markov chain is used, since expected lifetime now corresponds to what is known as expected absorption time, a straight-forward tool in analyzing absorbing Markov chains. There is a need to modify the way expected lifetime for this model is calculated, since the End of lifetime event for removal of what is known as the undrawn commitment component (the difference between the credit limit on e.g. a credit card and how much has been drawn) occurs with a delay from when the process reaches the corresponding absorbing state in the Markov chain. So, (expected) credit losses should be estimated over the period given by the following expression