IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v522y2019icp112-121.html
   My bibliography  Save this article

Latent factor models for credit scoring in P2P systems

Author

Listed:
  • Ahelegbey, Daniel Felix
  • Giudici, Paolo
  • Hadji-Misheva, Branka

Abstract

Peer-to-Peer (P2P) FinTech platforms allow cost reduction and service improvement in credit lending. However, these improvements may come at the price of a worse credit risk measurement, and this can hamper lenders and endanger the stability of a financial system. We approach the problem of credit risk for Peer-to-Peer (P2P) systems by presenting a latent factor-based classification technique to divide the population into major network communities in order to estimate a more efficient logistic model. Given a number of attributes that capture firm performances in a financial system, we adopt a latent position model which allow us to distinguish between communities of connected and not-connected firms based on the spatial position of the latent factors. We show through empirical illustration that incorporating the latent factor-based classification of firms is particularly suitable as it improves the predictive performance of P2P scoring models.

Suggested Citation

  • Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Latent factor models for credit scoring in P2P systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 112-121.
    • Handle: RePEc:eee:phsmap:v:522:y:2019:i:c:p:112-121
    DOI: 10.1016/j.physa.2019.01.130
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119301372
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.01.130?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Barfuss, Wolfram & Massara, Guido Previde & Di Matteo, T. & Aste, Tomaso, 2016. "Parsimonious modeling with information filtering networks," LSE Research Online Documents on Economics 68860, London School of Economics and Political Science, LSE Library.
    2. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Daniele Durante & David B. Dunson, 2014. "Nonparametric Bayes dynamic modelling of relational data," Biometrika, Biometrika Trust, vol. 101(4), pages 883-898.
    5. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    6. Paolo Giudici, 2001. "Bayesian data mining, with application to benchmarking and credit scoring," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 17(1), pages 69-81, January.
    7. Andrew Briggs & Taane Clark & Jane Wolstenholme & Philip Clarke, 2003. "Missing.... presumed at random: cost‐analysis of incomplete data," Health Economics, John Wiley & Sons, Ltd., vol. 12(5), pages 377-392, May.
    8. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Bayesian Graphical Models for STructural Vector Autoregressive Processes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 357-386, March.
    9. Raffaella Calabrese & Paolo Giudici, 2015. "Estimating bank default with generalised extreme value regression models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(11), pages 1783-1792, November.
    10. Minoiu, Camelia & Reyes, Javier A., 2013. "A network analysis of global banking: 1978–2010," Journal of Financial Stability, Elsevier, vol. 9(2), pages 168-184.
    11. Chinazzi, Matteo & Fagiolo, Giorgio & Reyes, Javier A. & Schiavo, Stefano, 2013. "Post-mortem examination of the international financial network," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1692-1713.
    12. P. Giudici & A. Spelta, 2016. "Graphical Network Models for International Financial Flows," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 128-138, January.
    13. Luis Javier Sánchez Barrios & Galina Andreeva & Jake Ansell, 2014. "Monetary and relative scorecards to assess profits in consumer revolving credit," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 443-453, March.
    14. Paolo Giudici & Laura Parisi, 2017. "Sovereign risk in the Euro area: a multivariate stochastic process approach," Quantitative Finance, Taylor & Francis Journals, vol. 17(12), pages 1995-2008, December.
    15. Andreeva, Galina & Ansell, Jake & Crook, Jonathan, 2007. "Modelling profitability using survival combination scores," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1537-1549, December.
    16. Hoff, Peter D., 2007. "Model Averaging and Dimension Selection for the Singular Value Decomposition," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 674-685, June.
    17. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Annals of Economics and Statistics, GENES, issue 123-124, pages 333-361.
    18. Milne, Alistair & Parboteeah, Paul, 2016. "The Business Models and Economics of Peer-to-Peer Lending," ECRI Papers 11594, Centre for European Policy Studies.
    19. Musmeci, Nicoló & Nicosia, Vincenzo & Aste, Tomaso & Di Matteo, Tiziana & Latora, Vito, 2017. "The multiplex dependency structure of financial markets," LSE Research Online Documents on Economics 85337, London School of Economics and Political Science, LSE Library.
    20. Aste, T. & Di Matteo, T. & Hyde, S.T., 2005. "Complex networks on hyperbolic surfaces," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(1), pages 20-26.
    21. Elisa Letizia & Fabrizio Lillo, 2017. "Corporate payments networks and credit risk rating," Papers 1711.07677, arXiv.org, revised Sep 2018.
    22. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
    23. Nicolò Musmeci & Vincenzo Nicosia & Tomaso Aste & Tiziana Di Matteo & Vito Latora, 2017. "The Multiplex Dependency Structure of Financial Markets," Complexity, Hindawi, vol. 2017, pages 1-13, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Xiao & Chong, Zhaohui & Giudici, Paolo & Huang, Bihong, 2022. "Network centrality effects in peer to peer lending," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    2. Chong, Zhaohui & Wei, Xiaolin, 2023. "Exploring the spatial linkage network of peer-to-peer lending in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    3. Tang, Xinyin & Feng, Chong & Zhu, Jianping & He, Minna, 2022. "How Can We Learn from Borrowers’ Online Behaviors? The Signal Effect of Borrowers’ Platform Involvement on Their Credit Risk," SocArXiv qga8j, Center for Open Science.
    4. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    5. Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Factorial Network Models To Improve P2P Credit Risk Management," MPRA Paper 92633, University Library of Munich, Germany.
    6. Ahelegbey, Daniel & Giudici, Paolo & Pediroda, Valentino, 2023. "A network based fintech inclusion platform," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel Felix Ahelegbey & Luis Carvalho & Eric D. Kolaczyk, 2020. "A Bayesian Covariance Graph And Latent Position Model For Multivariate Financial Time Series," DEM Working Papers Series 181, University of Pavia, Department of Economics and Management.
    2. Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Factorial Network Models To Improve P2P Credit Risk Management," MPRA Paper 92633, University Library of Munich, Germany.
    3. Agosto, Arianna & Ahelegbey, Daniel Felix & Giudici, Paolo, 2020. "Tree networks to assess financial contagion," Economic Modelling, Elsevier, vol. 85(C), pages 349-366.
    4. Giudici, Paolo, 2018. "Financial data science," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 160-164.
    5. Nicola, Giancarlo & Cerchiello, Paola & Aste, Tomaso, 2020. "Information network modeling for U.S. banking systemic risk," LSE Research Online Documents on Economics 107563, London School of Economics and Political Science, LSE Library.
    6. Ahelegbey, Daniel Felix & Giudici, Paolo & Hashem, Shatha Qamhieh, 2021. "Network VAR models to measure financial contagion," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    7. Daniel Felix Ahelegbey & Paolo Giudici, 2020. "Market Risk, Connectedness and Turbulence: A Comparison of 21st Century Financial Crises," DEM Working Papers Series 188, University of Pavia, Department of Economics and Management.
    8. Paolo Giudici & Laura Parisi, 2018. "CoRisk: Credit Risk Contagion with Correlation Network Models," Risks, MDPI, vol. 6(3), pages 1-19, September.
    9. Andrieş, Alin Marius & Ongena, Steven & Sprincean, Nicu & Tunaru, Radu, 2022. "Risk spillovers and interconnectedness between systemically important institutions," Journal of Financial Stability, Elsevier, vol. 58(C).
    10. Feng, Yusen & Wang, Gang-Jin & Zhu, You & Xie, Chi, 2023. "Systemic risk spillovers and the determinants in the stock markets of the Belt and Road countries," Emerging Markets Review, Elsevier, vol. 55(C).
    11. Zhang, Xiaoyuan & Zhang, Tianqi, 2022. "Dynamic credit contagion and aggregate loss in networks," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    12. Gong, Jue & Wang, Gang-Jin & Zhou, Yang & Zhu, You & Xie, Chi & Foglia, Matteo, 2023. "Spreading of cross-market volatility information: Evidence from multiplex network analysis of volatility spillovers," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).
    13. Dai, Zhifeng & Tang, Rui & Zhang, Xiaotong, 2023. "A new multilayer network for measuring interconnectedness among the energy firms," Energy Economics, Elsevier, vol. 124(C).
    14. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    15. Dai, Zhifeng & Tang, Rui & Zhang, Xinhua, 2023. "Multilayer network analysis for measuring the inter-connectedness between the oil market and G20 stock markets," Energy Economics, Elsevier, vol. 120(C).
    16. Ahelegbey, Daniel Felix & Cerchiello, Paola & Scaramozzino, Roberta, 2022. "Network based evidence of the financial impact of Covid-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 81(C).
    17. Fianu, Emmanuel Senyo & Ahelegbey, Daniel Felix & Grossi, Luigi, 2022. "Modeling risk contagion in the Italian zonal electricity market," European Journal of Operational Research, Elsevier, vol. 298(2), pages 656-679.
    18. Yong Tang & Jason Jie Xiong & Zi-Yang Jia & Yi-Cheng Zhang, 2018. "Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets," Complexity, Hindawi, vol. 2018, pages 1-31, November.
    19. Paolo Giudici & Laura Parisi, 2019. "Bail-In or Bail-Out? Correlation Networks to Measure the Systemic Implications of Bank Resolution," Risks, MDPI, vol. 7(1), pages 1-25, January.
    20. Billio, Monica & Casarin, Roberto & Costola, Michele & Iacopini, Matteo, 2024. "COVID-19 spreading in financial networks: A semiparametric matrix regression model," Econometrics and Statistics, Elsevier, vol. 29(C), pages 113-131.

    More about this item

    Keywords

    Credit risk; Factor models; Financial technology; Peer-to-peer; Scoring models; Spatial clustering;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:522:y:2019:i:c:p:112-121. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.