Latent factor models for credit scoring in P2P systems

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, 522, pp. 112-121. Elsevier 10.1016/j.physa.2019.01.130

[img] Text
1-s2.0-S0378437119301372-main.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (468kB) | Request a copy

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:

Journal Article (Original Article)

Division/Institute:

Business School > Institute for Applied Data Science & Finance
Business School

Name:

Ahelegbey, Daniel Felix;
Giudici, Paolo and
Hadji Misheva, Branka

Subjects:

H Social Sciences > HG Finance

ISSN:

03784371

Publisher:

Elsevier

Language:

English

Submitter:

Branka Hadji Misheva

Date Deposited:

17 Aug 2022 11:10

Last Modified:

17 Aug 2022 11:10

Publisher DOI:

10.1016/j.physa.2019.01.130

Uncontrolled Keywords:

Credit risk, Factor models, Financial technology, Peer-to-peer, Scoring models, Spatial clustering

ARBOR DOI:

10.24451/arbor.17301

URI:

https://arbor.bfh.ch/id/eprint/17301

Actions (login required)

View Item View Item
Provide Feedback