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Estimating the Acceptance Probabilities of Consumer Loan Offers in an Online Loan Comparison and Brokerage Platform

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  • Renatas Špicas

    (Department of Finance, Faculty of Economics and Business Administration, Vilnius University, 10222 Vilnius, Lithuania)

  • Airidas Neifaltas

    (Department of Finance, Faculty of Economics and Business Administration, Vilnius University, 10222 Vilnius, Lithuania)

  • Rasa Kanapickienė

    (Department of Finance, Faculty of Economics and Business Administration, Vilnius University, 10222 Vilnius, Lithuania)

  • Greta Keliuotytė-Staniulėnienė

    (Department of Finance, Faculty of Economics and Business Administration, Vilnius University, 10222 Vilnius, Lithuania)

  • Deimantė Vasiliauskaitė

    (Department of Finance, Faculty of Economics and Business Administration, Vilnius University, 10222 Vilnius, Lithuania)

Abstract

It is widely recognised that the ability of e-commerce businesses to predict conversion probability, i.e., acceptance probability, is critically important in today’s business environment. While the issue of conversion prediction based on browsing data in various e-commerce websites is broadly analysed in scientific literature, there is a lack of studies covering this topic in the context of online loan comparison and brokerage (OLCB) platforms. It can be argued that due to the inseparable relationship between the operation of these platforms and credit risk, the behaviour of consumers in making loan decisions differs from typical consumer behaviour in choosing non-risk-related products. In this paper, we aim to develop and propose statistical acceptance prediction models of loan offers in OLCB platforms. For modelling, we use diverse data obtained from an operating OLCB platform, including on customer (i.e., borrower) behaviour and demographics, financial variables, and characteristics of the loan offers presented to the borrowers/customers. To build the models, we experiment with various classifiers including logistic regression, random forest, XGboost, artificial neural networks, and support vector machines. Computational experiments show that our models can predict conversion with good performance in terms of area under the curve (AUC) score. The models presented are suitable for use in a loan comparison and brokerage platform for real-time process optimisation purposes.

Suggested Citation

  • Renatas Špicas & Airidas Neifaltas & Rasa Kanapickienė & Greta Keliuotytė-Staniulėnienė & Deimantė Vasiliauskaitė, 2023. "Estimating the Acceptance Probabilities of Consumer Loan Offers in an Online Loan Comparison and Brokerage Platform," Risks, MDPI, vol. 11(7), pages 1-30, July.
  • Handle: RePEc:gam:jrisks:v:11:y:2023:i:7:p:138-:d:1201431
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    References listed on IDEAS

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    1. Judith Chevalier & Austan Goolsbee, 2003. "Measuring Prices and Price Competition Online: Amazon.com and BarnesandNoble.com," Quantitative Marketing and Economics (QME), Springer, vol. 1(2), pages 203-222, June.
    2. Uddin, Main & Wang, Liang Choon & Smyth, Russell, 2021. "Do government-initiated energy comparison sites encourage consumer search and lower prices? Evidence from an online randomized controlled experiment in Australia," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 167-182.
    3. Yuriy Gorodnichenko & Oleksandr Talavera, 2017. "Price Setting in Online Markets: Basic Facts, International Comparisons, and Cross-Border Integration," American Economic Review, American Economic Association, vol. 107(1), pages 249-282, January.
    4. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    5. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
    6. Ramazan Esmeli & Mohamed Bader-El-Den & Hassana Abdullahi, 2021. "Towards early purchase intention prediction in online session based retailing systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 697-715, September.
    7. Mona Gupta & Happy Mittal & Parag Singla & Amitabha Bagchi, 2017. "Analysis and characterization of comparison shopping behavior in the mobile handset domain," Electronic Commerce Research, Springer, vol. 17(3), pages 521-551, September.
    8. Vladimir Marianov & H. A. Eiselt & Armin Lüer-Villagra, 2020. "The Follower Competitive Location Problem with Comparison-Shopping," Networks and Spatial Economics, Springer, vol. 20(2), pages 367-393, June.
    9. R. John Irwin & Timothy C. Irwin, 2013. "Appraising Credit Ratings: Does The Cap Fit Better Than The Roc?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 18(4), pages 396-408, October.
    10. Drechsler, Wenzel & Natter, Martin, 2011. "Do Price Charts Provided by Online Shopbots Influence Price Expectations and Purchase Timing Decisions?," Journal of Interactive Marketing, Elsevier, vol. 25(2), pages 95-109.
    11. Michael R. Baye & John Morgan & Patrick Scholten, 2004. "Price Dispersion In The Small And In The Large: Evidence From An Internet Price Comparison Site," Journal of Industrial Economics, Wiley Blackwell, vol. 52(4), pages 463-496, December.
    12. Bodur, H. Onur & Klein, Noreen M. & Arora, Neeraj, 2015. "Online Price Search: Impact of Price Comparison Sites on Offline Price Evaluations," Journal of Retailing, Elsevier, vol. 91(1), pages 125-139.
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