信用风险分析

    进入21世纪信用市场持续动荡,对资本和信用风险模型的需求日益迫切。我们的团队致力于构建更好的信用风险模型。目前主要对信用风险分析的如下课题进行研究:

  • 风险最优模型校准程序

    信用风险模型的目的是按违约率(PD)、违约损失(LGD)和违约风险暴露(EAD)区分债务和债务人。损失需要由资本来补偿。因此,我们的目标是通过考虑过去的经验和未来期望,开发基于最优风险一致性度量的校准程序。更进一步,我们将考查“当前型”和“贯穿周期型”校准程序的影响。

  • 基于生存分析的信用风险建模

    生存分析是一种在医疗领域常用的方法,特别在研究预测患者存活时间长度的医学研究中。广义上讲,生存分析模型预测一个特定的事件发生所需时间。在可用二元分类器的领域,我们可以使用生存分析从时间维度对事件的发生进行分析。
    信用评分分为两类:申请评分和行为评分。前者是指在客户申请时使用客户及贷款相关数据对客户是否拖欠贷款进行预测。贷款获批后,债权人开始收集有关客户的行为信息,例如付款或消费。生存分析模型将行为数据纳入模型,不仅预测一个特定帐户是否违约,而且预测什么时候发生违约。这一时间信息让金融机构,更好地预测潜在风险,为更好地使用资本做好准备。在一些情况下,一个账户在违约后一段时期仍然是有利可图,传统二元分类模型在此情况下,对财务计划帮助不大。因此,我们需要研究何种统计技术在信用风险的时间序列数据上具有较好的表现,以及如何将生存分析用到不同时间轴上。

  • 信用风险参数监测和验证框架

    在新的监管体系下,必须设计新方法对经济资本模型参数进行监测和验证。这一方面需要对模型后验测试、参照管理和参数定性验证的方法进行理论探讨。另一方面,我们必须找出如何正确测量数据质量的方法。此外探讨如何在模型层面对模型的绩效、可理解度和可信度进行量化。

  • 引入新压力测试程序

    对于金融机构来说,需要新方法对资本模型进行压力测试。使用压力测试,可以对资本模型在极端不利的经济环境下的表现进行监测。这项研究重点放在界定压力情景,整合各类风险,理解损失分布尾部的行为,并在企业层面上衡量对压力对信用风险和资本状况的影响。

  • 模型风险的量化

    风险量化模型是不完美的,使用它们存在一定的危险。意识到这一点很重要,并应对此采取措施,例如使用保守参数校准或其他新方法。我们致力于定义基于数据质量和模型局限性的模型风险。

  • 构建微型金融信用风险模型

    由于日益激烈的竞争和过度负债,小微金融机构不得不在受管制的环境下,追求它们的社会和经济目标。因此,使用功能强大的风险管理工具成为了生存的关键。正是在这样的背景下,我们传统金融机构的经典方法引入到小微金融行业,提高社会影响和经济效率。

  • 研究数据质量对信用风险模型的影响

    最近的研究已经表明,随着累计的数据越来越复杂,很多公司正面临数据质量的问题。为了解决这一问题,过往研究推荐使用总体数据质量管理(TQM)计划,TQM包括以下阶段:数据质量定义、测量、分析和改进。数据质量常被定义为“使用适应度”,虽然这一定义抓住了质量的本质,但是导致了测度的困难。因此,有必要从多个维度对数据质量进行分析和描述。数据质量依赖于场景,数据质量维度需要根据具体场景设定,我们在信用风险评估场景下对数据质量维度进行了研究。

代表性论文:

  • Dirick L, Claeskens G., Baesens B., An Akaike information criterion for multiple event mixture cure models, European Journal of Operational Research, Volume 24, pp. 449-457, 2015.
  • Lessmann, S., Baesens, B., Seow, H., Thomas, L. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: A ten-year update. European Journal of Operational Research, Accepted.
  • Tobback E., Martens D., Van Gestel T., Baesens B., Forecasting loss given default models: impact of account characteristics and the macroeconomic state, Journal of the Operational Research Society, Volume 65, Number 3, pp. 376-392, 2014.
  • Louis P., Baesens B., Do for-profit microfinance institutions achieve better financial efficiency and social impact?, Journal of Development Effectiveness, Volume 5, Number 3, pp. 359-380, 2013.
  • Louis P., Van Laere E., Baesens B., Understanding and predicting bank rating transitions using optimal survival analysis models, Economics Letters, Volume 119, Number 3, pp. 280-283, 2013.
  • Louis P., Seret A., Baesens B., Financial Efficiency and Social Impact of Microfinance Institutions using Self-Organizing Maps, World Development, Volume 45, pp. 197-210, 2013.
  • Berteloot K., Verbeke W., Castermans G., Van Gestel T., Martens D., Baesens B., A Novel Credit Rating Migration Modeling Approach using Macroeconomic Indicators, Journal of Forecasting, Volume 32, Issue 7, pp. 654–672, 2013.
  • Van Gool, J., Verbeke, W., Sercu, P., Baesens, B. (2012). Credit Scoring For Microfinance – Is It Worth It?. International Journal Of Finance & Economics, 17 (2), 102-123.
  • Martens D., Van Gestel T., De Backer M., Haesen R., Vanthienen J., Baesens B., Credit Rating Prediction Using Ant Colony Optimization, Journal Of The Operational Research Society, Vol. 61, Pp. 561-573, 2010
  • Setiono R. , Baesens B., Mues C., A Note On Knowledge Discovery Using Neural Networks And Its Application To Credit Card Screening, European Journal Of Operational Research, Vol. 192, Number 1, Pp. 326-332, 2009
  • Van Gestel T., Martens D., Baesens B., Feremans D., Huysmans J., Vanthienen J., Forecasting And Analyzing Insurance Companies’ Ratings, International Journal Of Forecasting, Vol. 23, Number 3, Pp. 513-529, 2007
  • Baesens, B., Van Gestel, T., Stepanova, M., Vanthienen, J., Van Den Poel, D. (2005). Neural Network Survival Analysis For Personal Loan Data. Journal Of The Operational Research Society, 56 (9 (sept.)), 1089-1098.
  • Moges, H., Dejaeger, K., Lemahieu, W., Baesens, B. (2012). A Multidimensional Analysis Of Data Quality For Credit Risk Management: New Insights And Challenges. Information & Management.
  • Moges, H., Dejaeger, K., Lemahieu, W., Baesens, B. (2012). A Total Data Quality Management For Credit Risk: New Insights And Challenges. International Journal Of Information Quality, 3 (1), 1-27.
  • Martens D., Baesens B., Van Gestel T., Vanthienen J., Comprehensible Credit Scoring Models Using Rule Extraction From Support Vector Machines, European Journal Of Operational Research, Vol. 183, Pp. 1466-1476, 2007
  • Hoffmann F., Baesens B., Mues C., Van Gestel T., Vanthienen J., Inferring Descriptive And Approximate Fuzzy Rules For Credit Scoring Using Evolutionary Algorithms, European Journal Of Operational Research, Vol. 177, Number 1, Pp. 540-555, 2006
  • Van Gestel T., Baesens B., Van Dijcke P., Suykens J., Garcia J., Alderweireld T., Linear And Nonlinear Credit Scoring By Combining Logistic Regression And Support Vector Machines, Journal Of Credit Risk, Vol. 1, Number 4, 2005
  • Van Gestel T., Baesens B., Van Dijcke P., Garcia J., Suykens J.a.k., Vanthienen J., A Process Model To Develop An Internal Rating System: Sovereign Credit Ratings, Decision Support Systems, Vol. 42, Number 2, Pp. 1131-1151, 2006
  • Huysmans J., Baesens B., Van Gestel T., Vanthienen J., Failure Prediction With Self Organizing Maps ,expert Systems With Applications, Vol. 30, Number 3, Pp. 479-487, 2006
  • Somol P., Baesens B., Pudil P., Vanthienen J., Filter-versus Wrapper-based Feature Selection For Credit Scoring, International Journal Of Intelligent Systems, Vol. 20, Number 10, Pp. 985-999, 2005
  • Baesens B., Van Gestel T., Mues C., Vanthienen J., Intelligent Information Systems For Financial Engineering, Expert Systems With Applications, Vol. 30, Number 3, Pp. 413-414, 2006
  • Van Gestel T., Baesens B., Suykens J.a.k., Van Den Poel D., Baestaens D.-e., Willekens M., Kernel Based Classification For Financial Distress Detection, European Journal Of Operational Research, Vol. 172, Number 3, Pp. 979-1003, 2006
  • Baesens B., Van Gestel T., Stepanova M., Van Den Poel D., Vanthienen J., Neural Network Survival Analysis For Personal Loan Data, Journal Of The Operational Research Society, Vol. 59, Number 9, Pp. 1089-1098, 2005
  • Mues C., Baesens B., Files C.m., Vanthienen J., Decision Diagrams In Machine Learning: An Empirical Study On Real-life Credit-risk Data, Expert Systems With Applications, Vol. 27, Number 2, Pp. 257-264, 2004
  • Baesens B., Setiono R., Mues C., Vanthienen J., Using Neural Network Rule Extraction And Decision Tables For Credit-risk Evaluation, Management Science, Vol. 49, Number 3, Pp. 312-329, 2003
  • Lima E., Mues C., Baesens B., Monitoring And Backtesting Churn Models, Expert Systems With Applications, 2010
  • Van Gestel T., Martens D., Baesens B., From Linear To Non-linear Kernel Based Classifiers For Bankruptcy Prediction, Neurocomputing, 2010