Latent Factor Models for Credit Scoring in P2P Systems

Ahelegbey, Daniel Felix and Giudici, Paolo and Hadji-Misheva, Branka (2018): Latent Factor Models for Credit Scoring in P2P Systems. Forthcoming in: Physica A: Statistical Mechanics and its Applications No. 522 (10 February 2019): pp. 112-121.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Credit Risk, Factor Models, Financial Technology, Peer-to-Peer, Scoring Models, Spatial Clustering
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models; G - Financial Economics > G1 - General Financial Markets > G10 - General; G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks ; Depository Institutions ; Micro Finance Institutions ; Mortgages

References

Ahelegbey, D. F., Billio, M., and Casarin, R. (2016a), “Bayesian Graphical Models for Structural Vector Autoregressive Processes,” Journal of Applied Econometrics, 31, 357–386.

Ahelegbey, D. F., Billio, M., and Casarin, R. (2016b), “Sparse Graphical Vector Autoregression: A Bayesian Approach,” Annals of Economics and Statistics, 123/124, 333–361.

Andreeva, G., Ansell, J., and Crook, J. (2007), “Modelling Profitability Using Survival Combination Scores,” European Journal of Operational Research, 183, 1537–1549.

Aste, T., Di Matteo, T., and Hyde, S. (2005), “Complex Networks on Hyperbolic Surfaces,” Physica A: Statistical Mechanics and its Applications, 346, 20–26.

Aste, T., Shaw, W., and Di Matteo, T. (2010), “Correlation Structure and Dynamics in Volatile Markets,” New Journal of Physics, 12, 085009.

Bai, J. and Ng, S. (2002), “Determining the Number of Factors in Approximate Factor Models,” Econometrica, 70, 191–221.

Barfuss, W., Massara, G. P., Di Matteo, T., and Aste, T. (2016), “Parsimonious Modeling with Information Filtering Networks,” Physical Review E, 94, 062306.

Barrios, L. J. S., Andreeva, G., and Ansell, J. (2014), “Monetary and Relative Scorecards to Assess Profits in Consumer Revolving Credit,” Journal of the Operational Research Society, 65, 443–453.

Billio, M., Getmansky, M., Lo, A. W., and Pelizzon, L. (2012), “Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors,” Journal of Financial Economics, 104, 535 – 559.

Briggs, A., Clark, T., Wolstenholme, J., and Clarke, P. (2003), “Missing.... Presumed at Random: Cost-analysis of Incomplete Data,” Health Economics, 12, 377–392.

Burton, A., Billingham, L. J., and Bryan, S. (2007), “Cost-effectiveness in clinical trials: using multiple imputation to deal with incomplete cost data,” Clinical Trials, 4, 7.

Calabrese, R. and Giudici, P. (2015), “Estimating Bank Default with Generalized Extreme Value Regression Models,” Journal of the Operational Research Society, 66, 1783–1792.

Chinazzi, M., Fagiolo, G., Reyes, J. A., and Schiavo, S. (2013), “Post-Mortem Examination of International Financial Network,” Journal of Economic Dynamics and Control, 37, 1692–1713.

Diebold, F. and Yilmaz, K. (2014), “On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms,” Journal of Econometrics, 182, 119–134.

Durante, D. and Dunson, D. B. (2014), “Nonparametric Bayes Dynamic Modelling of Relational Data,” Biometrika, 101, 883–898.

Emekter, R., Tu, Y., Jirasakuldech, B., and Lu, M. (2015), “Evaluating Credit Risk and Loan Performance in Online Peer-to-Peer (P2P) Lending,” Applied Economics, 47, 54–70.

Giudici, P. and Hadji-Misheva, B. (2017), “Scoring Models for P2P Lending Platforms: A Network Approach,” Working paper, University of Pavia.

Giudici, P. and Parisi, L. (2017), “Sovereign risk in the Euro area: a multivariate stochastic approach,” Quantitative Finance, 17, 1995–2018.

Giudici, P. and Parisi, L. (2018), “Credit risk contagion through correlation network models,” Risks, 1.

Giudici, P. and Spelta, A. (2016), “Graphical Network Models for International Financial Flows,” Journal of Business & Economic Statistics, 34, 128–138.

Giudici, P., Sarlin, P., and Spelta, A. (2017), “The interconnected nature of financial systems: direct and common exposures,” Journal of Banking and Finance.

Hoff, P. D. (2007), “Model Averaging and Dimension Selection for the Singular Value Decomposition,” Journal of the American Statistical Association, 102, 674–685.

Hoff, P. D. (2008), “Modeling Homophily and Stochastic Equivalence in Symmetric Relational Data,” in Advances in Neural Information Processing Systems, pp. 657–664.

Khan, B. S. and Niazi, M. A. (2017), “Network Community Detection: A Review and Visual Survey,” Working paper, arXiv:1708.00977.

Letizia, E. and Lillo, F. (2018), “Corporate Payments Networks and Credit Risk Rating,” Working paper, https://arxiv.org/abs/1711.07677.

Mantegna, R. N. (1999), “Hierarchical Structure in Financial Markets,” The European Physical Journal B-Condensed Matter and Complex Systems, 11, 193–197.

Massara, G. P., Di Matteo, T., and Aste, T. (2016), “Network Filtering for Big Data: Triangulated Maximally Filtered Graph,” Journal of complex Networks, 5, 161–178.

Milne, A. and Parboteeah, P. (2016), “The Business Models and Economics of Peer-to-Peer Lending,” Tech. rep., Centre for European Policy Studies.

Minoiu, C. and Reyes, J. A. (2013), “A Network Analysis of Global Banking: 1978–2010,” Journal of Financial Stability, 9, 168–184.

Musmeci, N., Nicosia, V., Aste, T., Di Matteo, T., and Latora, V. (2017), “The Multiplex Dependency Structure of Financial Markets,” Complexity, 2017.

Pozzi, F., Di Matteo, T., and Aste, T. (2013), “Spread of risk across financial markets: better to invest in the peripheries,” Scientific reports, 3, 1665.

Sarkar, S. and Dong, A. (2011), “Community Detection in Graphs using Singular Value Decomposition,” Physical Review E, 83, 046114.

Serrano-Cinca, C. and Gutiérrez-Nieto, B. (2016), “The Use of Profit Scoring as an Alternative to Credit Scoring Systems in Peer-to-Peer Lending,” Decision Support Systems, 89, 113–122.

Tumminello, M., Aste, T., Di Matteo, T., and Mantegna, R. N. (2005), “A Tool For Filtering Information In Complex Systems,” Proceedings of the National Academy of Sciences, 102, 10421–10426.

West, D. (2000), “Neural Network Credit Scoring Models,” Computers & Operations Re-search, 27, 1131 - 1152.

Wilson, R. L. and Sharda, R. (1994), “Bankruptcy Prediction Using Neural Networks,” Decision Support Systems, 11, 545–557.