Credit scoring for profitability objectives

In consumer credit markets lending decisions are usually represented as a set of classification problems. The objective is to predict the likelihood of customers ending up in one of a finite number of states, such as good/bad payer, responder/non-responder and transactor/non-transactor. Decision rules are then applied on the basis of the resulting model estimates. However, this represents a misspecification of the true objectives of commercial lenders, which are better described in terms of continuous financial measures such as bad debt, revenue and profit contribution. In this paper, an empirical study is undertaken to compare predictive models of continuous financial behaviour with binary models of customer default. The results show models of continuous financial behaviour to outperform classification approaches. They also demonstrate that scoring functions developed to specifically optimize profit contribution, using genetic algorithms, outperform scoring functions derived from optimizing more general functions such as sum of squared error.     

Lookahead scorecards for new fixed term credit products

Standard approaches to scorecard construction require that a body of data has already been collected for which the customers have known good/bad outcomes, so that scorecards can be built using this information. This requirement is not satisfied by new financial products. To overcome this lack, we describe a class of models based on using information about the length of time customers have been using the product, as well as any available information which does exist about true good/bad outcome classes. These models not only predict the probability that a new customer will go bad at some time during the product's term, but also evolve as new information becomes available. Particular choices of functional form in such models can lead to scorecards with very simple structures. The models are illustrated on a data set relating to loans.     

Accounting and actuarial smoothing of retirement payouts in participating life annuities

Life insurers use accounting and actuarial techniques to smooth reporting of firm assets and liabilities, seeking to transfer surpluses in good years to cover benefit payouts in bad years. Yet these techniques have been criticized as they make it difficult to assess insurers’ true financial status. We develop stylized and realistically-calibrated models of a participating life annuity, an insurance product that pays retirees guaranteed lifelong benefits along with variable non-guaranteed surplus. Our goal is to illustrate how accounting and actuarial techniques for this type of financial contract shape policyholder wellbeing, along with insurer profitability and stability. Smoothing adds value to both the annuitant and the insurer, so curtailing smoothing could undermine the market for long-term retirement payout products.     

网络借贷收窄利率定价区间的影响研究——基于倾向性分数匹配法

随着网络借贷的发展,学术界对网络借贷的研究逐渐深入。利率定价机制是网络借贷机制设计的核心,体现了金融的本质——对风险的定价,并逐渐成为学术研究的话题。Wei和Lin^[1]曾记录和分析了美国网络借贷机制变更的过程。本文选用国内一家代表性的网络借贷平台数据,用倾向性分数匹配法对其利率定价机制变化前后的交易行为进行研究,发现当平台收窄了利率区间且降低了合格借款人的审核通过率,违约率反而更高。狭窄的利率区间降低了利率区分度,而利率区分度是投资人判断具体贷款所处风险水平的重要依据,实际上恶化了信息不对称,影响投资人投资行为,具体表现为满标时间延长、单笔贷款投标占比减少,并且投资人羊群行为加重。本文以期限利率周度标准差为利率区间的代理变量,解释了上述变化产生的原因。平台收窄利率区间,降低了贷款质量优劣的区分度,使得平台和投资人风险识别效率降低,未能达到平台运营优化的结果。本文丰富了网络借贷的学术研究,为网络借贷利率定价机制的发展提供参考。     

基于财务状况多分类的上市公司财务危机预警

目前的财务危机预警模型大多局限于二分类研究,而公司陷入财务危机往往会经历一个逐步衰败的过程,简单的二分类有时会掩盖某些上市公司财务状况逐渐变差的事实.为了更准确地判断公司的财务状况,将公司分为财务稳定公司、潜在危机公司、财务危机公司和破产公司四类,运用t-2年的财务数据构建了有序多分类logistic回归模型.实证结果表明,模型的预测能力较好,误判成本较低,能提供更加准确的信息.     

Modeling churn using customer lifetime value

The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution is to redefine the notion of customer loyalty by considering it from a customer-centric viewpoint instead of a product-centric one. We hereby use the customer lifetime value (CLV) defined as the discounted value of future marginal earnings, based on the customer’s activity. Hence, a churner is defined as someone whose CLV, thus the related marginal profit, is decreasing. As a second contribution, the loss incurred by the CLV decrease is used to appraise the cost to misclassify a customer by introducing a new loss function. In the empirical study, we compare the accuracy of various classification techniques commonly used in the domain of churn prediction, including two cost-sensitive classifiers. Our final conclusion is that since profit is what really matters in a commercial environment, standard statistical accuracy measures for prediction need to be revised and a more profit oriented focus may be desirable.     

Time will tell: behavioural scoring and the dynamics of consumer credit assessment

This paper discusses the use of dynamic modelling in consumercredit risk assessment. It surveys the approaches and objectivesof behavioural scoring, customer scoring and profit scoring.It then investigates how Markov chain stochastic processes canbe used to model the dynamics of the delinquency status andbehavioural scores of consumers. It discusses the use of segmentation,mover–stayer models and the use of second- and third-ordermodels to improve the fit of such models. The alternative survivalanalysis proportional hazards approach to estimating when defaultoccurs is considered. Comparisons are made between the wayscredit risk is modelled in consumer lending and corporate lending.     

European generic scoring models using survival analysis

Credit scoring discriminates between ‘good’ and ‘bad’ credit risks to assist credit-grantors in making lending decisions. Such discrimination may not be a good indicator of profit, while survival analysis allows profit to be modelled. The paper explores the application of parametric accelerated failure time and proportional hazards models and Cox non-parametric model to the data from the retail card (revolving credit) from three European countries. The predictive performance of three national models is tested for different timescales of default and then compared to that of a single generic model for a timescale of 25 months. It is found that survival analysis national and generic models produce predictive quality, which is very close to the current industry standard—logistic regression. Stratification is investigated as a way of extending Cox non-parametric proportional hazards model to tackle heterogeneous segments in the population.     

Likelihood-ratio changepoint features for consumer-behaviour models

Some predictive models for customer value management might benefit from information about certain changes in individual-consumer behaviour. We take changepoint methods as the first step in producing a model-input feature for this purpose. An unusual feature in the application of changepoint methods to consumer data is there are as many streams of data as there are customers. This property is used to help decide whether an individual has changed their behaviour by ordering likelihood-ratio statistics from the changepoint models. Following a review of changepoint methods, the approach is demonstrated on cash machine transactions. Models for the amount, location and time of transaction are used and accounts exhibiting large evidence of change are examined in detail. For the data set used the approach performs sensibly. The worth of likelihood-ratio statistics to rank evidence for change is considered more generally through some of the literature.     

A comparison of strategies to develop a customer default scoring model

Behavioural scoring models are generally used to estimate the probability that a customer of a financial institution who owns a credit product will default on this product in a fixed time horizon. However, one single customer usually purchases many credit products from an institution while behavioural scoring models generally treat each of these products independently. In order to make credit risk management easier and more efficient, it is interesting to develop customer default scoring models. These models estimate the probability that a customer of a certain financial institution will have credit issues with at least one product in a fixed time horizon. In this study, three strategies to develop customer default scoring models are described. One of the strategies is regularly utilized by financial institutions and the other two will be proposed herein. The performance of these strategies is compared by means of an actual data bank supplied by a financial institution and a Monte Carlo simulation study.     

基于BP神经网络的P2P网贷借款人信用评估

评估借款人信用是P2P网贷公司控制风险的重要步骤,对于网贷公司的正常运行有着极其重要的意义。论文参考商业银行信用指标体系并根据P2P网贷自身特点,建立了P2P网贷借款人的信用评估指标体系。根据建立的指标体系构建相应的BP神经网络模型,并利用一步正切法进行优化。然后选取具有代表性的P2P网贷平台的相关数据,对该模型进行训练和仿真,证明了该模型对P2P网贷平台的风险控制起到一定的作用。     

A bi‐level neural‐based fuzzy classification approach for credit scoring problems

The credit scoring is a risk evaluation task considered as a critical decision for financial institutions in order to avoid wrong decision that may result in huge amount of losses. Classification models are one of the most widely used groups of data mining approaches that greatly help decision makers and managers to reduce their credit risk of granting credits to customers instead of intuitive experience or portfolio management. Accuracy is one of the most important criteria in order to choose a credit‐scoring model; and hence, the researches directed at improving upon the effectiveness of credit scoring models have never been stopped. In this article, a hybrid binary classification model, namely FMLP, is proposed for credit scoring, based on the basic concepts of fuzzy logic and artificial neural networks (ANNs). In the proposed model, instead of crisp weights and biases, used in traditional multilayer perceptrons (MLPs), fuzzy numbers are used in order to better model of the uncertainties and complexities in financial data sets. Empirical results of three well‐known benchmark credit data sets indicate that hybrid proposed model outperforms its component and also other those classification models such as support vector machines (SVMs), K‐nearest neighbor (KNN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA). Therefore, it can be concluded that the proposed model can be an appropriate alternative tool for financial binary classification problems, especially in high uncertainty conditions. © 2013 Wiley Periodicals, Inc. Complexity 18: 46–57, 2013     

Enhanced decision support in credit scoring using Bayesian binary quantile regression

Fierce competition as well as the recent financial crisis in financial and banking industries made credit scoring gain importance. An accurate estimation of credit risk helps organizations to decide whether or not to grant credit to potential customers. Many classification methods have been suggested to handle this problem in the literature. This paper proposes a model for evaluating credit risk based on binary quantile regression, using Bayesian estimation. This paper points out the distinct advantages of the latter approach: that is (i) the method provides accurate predictions of which customers may default in the future, (ii) the approach provides detailed insight into the effects of the explanatory variables on the probability of default, and (iii) the methodology is ideally suited to build a segmentation scheme of the customers in terms of risk of default and the corresponding uncertainty about the prediction. An often studied dataset from a German bank is used to show the applicability of the method proposed. The results demonstrate that the methodology can be an important tool for credit companies that want to take the credit risk of their customer fully into account.     

基于变精度加权平均粗糙度决策树的财务预警研究

运用聚类方法把公司财务状况分为5个等级,分别为财务状况健康,良好,一般,预警和危机,与以往将研究样本分为ST和非ST两类的财务预警模型相比,5分类模型更加精确合理,贴近实际。同时基于指标相关性和指标重要度对33个财务指标进行了约简,得到9个能够反映企业财务状况的财务指标。以约简后的9个指标及5个等级的财务状况来建立决策树,指标体系和财务等级更加合理。树的生成过程运用粗糙集中的变精度加权平均粗糙度作为选择测试属性的方法,每次选择变精度加权平均粗糙度值最小的属性作为分支结点。变精度加权平均粗糙度的应用提高了决策树的防噪声能力,复杂性较低且能有效提高分类效果。实证研究表明将它应用到财务预警领域,提高了财务预警的分类精度。     

Samsung card lending model

Samsung Card Lending Model (SCLM) analyzes cash flow in individual accounts and measures the level of company-wide risk. Serving as a risk and portfolio management model in the consumer lending business, the main features of SCLM are as follows. Default ratios such as intrinsic balance default probability and annual default ratio are computed using the past, present, and future cash flows of accounts. The provision is shown as the total sum of write-offs. The size of capital required is determined by default probability distribution. The price for new accounts is quoted based on cash flow simulations reflecting future business environments. SCLM has shown good performance in Samsung card consumer lending business since the Korean credit card crisis of 2003.     

Binary formally self-dual odd codes

Most of the research on formally self-dual (f.s.d.) codes has been developed for binary f.s.d. even codes, but only limited research has been done for binary f.s.d. odd codes. In this article we complete the classification of binary f.s.d. odd codes of lengths up to 14. We also classify optimal binary f.s.d. odd codes of length 18 and 24, so our result completes the classification of binary optimal f.s.d. odd codes of lengths up to 26. For this classification we first find a relation between binary f.s.d. odd codes and binary f.s.d. even codes, and then we use this relation and the known classification results on binary f.s.d. even codes. We also classify (possibly) optimal binary double circulant f.s.d. odd codes of lengths up to 40.     

Analyzing consumers’ shopping behavior using RFID data and pattern mining

The development of sensor networks has enabled detailed tracking of customer behavior in stores. Shopping path data which records each customer??s position and time information is attracting attention as new marketing data. However, there are no proposed marketing models which can identify good customers from huge amounts of time series data on customer movement in the store. This research aims to use shopping path data resulting from tracking customer behavior in the store, using information on the sequence of visiting each product zone in the store and staying time at each product zone, to find how they affect purchasing. To discover useful knowledge for store management, shopping paths data has been transformed into sequence data including information on visit sequence and staying times in the store, and LCMseq has been applied to them to extract frequent sequence patterns. In this paper, we find characteristic in-store behavior patterns of good customers by using actual data of a Japanese supermarket.     

Quantile regression with group lasso for classification

Applications of regression models for binary response are very common and models specific to these problems are widely used. Quantile regression for binary response data has recently attracted attention and regularized quantile regression methods have been proposed for high dimensional problems. When the predictors have a natural group structure, such as in the case of categorical predictors converted into dummy variables, then a group lasso penalty is used in regularized methods. In this paper, we present a Bayesian Gibbs sampling procedure to estimate the parameters of a quantile regression model under a group lasso penalty for classification problems with a binary response. Simulated and real data show a good performance of the proposed method in comparison to mean-based approaches and to quantile-based approaches which do not exploit the group structure of the predictors.     

多层感知器信用评模型及预警研究

本文利用多层感知器 ( MLP)原理建立神经网络信用评价模型 ,用来对我国 2 0 0 0年 1 0 6家上市公司进行信用评级 ,并进一步对我国 2 0 0 1年公布的 1 3家预亏公司进行预警研究 .按照各上市公司的经营状况分为“好”、“差”两类 ,每一类由 5 3家上市公司构成数据样本 .对于每一家上市公司 ,主要考虑其经营状况的四个财务指标 :每股收益 ,每股净资产 ,净资产收益率和每股现金流量 .仿真结果表明 ,本文所建立的神经网络信用评价模型有很高的分类准确率 ,达到 98.1 1 % .又由于该信用评价模型有很强的适应能力 ,故可以进一步用来对企业的财务危机进行预警研究 .预警实证分析表明 ,该信用评价模型对我国 2 0 0 1年公布的 1 3家预亏公司进行预警分析 ,预警准确率达到 1 0 0 % .此外 ,文中还给出 MLP网络模型的学习算法和步骤     

Groups of Invertible Binary Operations of a Topological Space

In this paper, continuous binary operations of a topological space are studied and a criterion of their invertibility is proved. The classification problem of groups of invertible continuous binary operations of locally compact and locally connected spaces is solved. A theorem on the binary distributive representation of a topological group is also proved.