IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v11y2023i7p138-d1201431.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/11/7/138/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/11/7/138/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    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. Sahar Karimi, 2021. "Cross-visiting Behaviour of Online Consumers Across Retailers’ and Comparison Sites, a Macro-Study," Information Systems Frontiers, Springer, vol. 23(3), pages 531-542, June.
    2. Diego Aparicio & Zachary Metzman & Roberto Rigobon, 2024. "The pricing strategies of online grocery retailers," Quantitative Marketing and Economics (QME), Springer, vol. 22(1), pages 1-21, March.
    3. Yuriy Gorodnichenko & Viacheslav Sheremirov & Oleksandr Talavera, 2018. "Price Setting in Online Markets: Does IT Click?," Journal of the European Economic Association, European Economic Association, vol. 16(6), pages 1764-1811.
    4. Carattini, Stefano & Gillingham, Kenneth & Meng, Xiangyu & Yoeli, Erez, 2024. "Peer-to-peer solar and social rewards: Evidence from a field experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 219(C), pages 340-370.
    5. Alex Nikolsko‐Rzhevskyy & Oleksandr Talavera & Nam Vu, 2023. "The flood that caused a drought," Economic Inquiry, Western Economic Association International, vol. 61(4), pages 965-981, October.
    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. Sheremirov, Viacheslav, 2020. "Price dispersion and inflation: New facts and theoretical implications," Journal of Monetary Economics, Elsevier, vol. 114(C), pages 59-70.
    8. Franz Hackl & Michael Hölzl‐Leitner & Rudolf Winter‐Ebmer & Christine Zulehner, 2021. "Successful retailer strategies in price comparison platforms," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(5), pages 1284-1305, July.
    9. Pallant, Jason I. & Danaher, Peter J. & Sands, Sean J. & Danaher, Tracey S., 2017. "An empirical analysis of factors that influence retail website visit types," Journal of Retailing and Consumer Services, Elsevier, vol. 39(C), pages 62-70.
    10. Michael R. Baye & J. Rupert J. Gatti & Paul Kattuman & John Morgan, 2009. "Clicks, Discontinuities, and Firm Demand Online," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 18(4), pages 935-975, December.
    11. Michael R. Baye & John Morgan, 2009. "Brand and Price Advertising in Online Markets," Management Science, INFORMS, vol. 55(7), pages 1139-1151, July.
    12. Li, Han & Dinlersoz, Emin, 2012. "Quality-based Price Discrimination: Evidence from Internet Retailers’ Shipping Options," Journal of Retailing, Elsevier, vol. 88(2), pages 276-290.
    13. Debashrita Mohapatra & Debi Prasad Mohapatra & Ram Sewak Dubey, 2023. "Price dispersion across online platforms: Evidence from hotel room prices in London (UK)," Papers 2310.12341, arXiv.org.
    14. Richards, Timothy J. & Hamilton, Stephen F. & Allender, William, 2016. "Search and price dispersion in online grocery markets," International Journal of Industrial Organization, Elsevier, vol. 47(C), pages 255-281.
    15. Vanhala, Mika & Lu, Chien & Peltonen, Jaakko & Sundqvist, Sanna & Nummenmaa, Jyrki & Järvelin, Kalervo, 2020. "The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research," Journal of Business Research, Elsevier, vol. 106(C), pages 46-59.
    16. Timothy J. Richards & Stephen F. Hamilton & Koichi Yonezawa, 2017. "Variety and the Cost of Search in Supermarket Retailing," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 50(3), pages 263-285, May.
    17. Baye, Michael & GATTI, RUPERT J & Kattuman, Paul & Morgan, John, 2004. "Estimating Firm-Level Demand at a Price Comparison Site: Accounting for Shoppers and the Number of Competitors," Competition Policy Center, Working Paper Series qt923692d1, Competition Policy Center, Institute for Business and Economic Research, UC Berkeley.
    18. Annika Baumann & Johannes Haupt & Fabian Gebert & Stefan Lessmann, 2019. "The Price of Privacy," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 413-431, August.
    19. Anindya Ghose & Michael D. Smith & Rahul Telang, 2006. "Internet Exchanges for Used Books: An Empirical Analysis of Product Cannibalization and Welfare Impact," Information Systems Research, INFORMS, vol. 17(1), pages 3-19, March.
    20. Xu, Xianhao & Shen, Yaohan & (Amanda) Chen, Wanying & Gong, Yeming & Wang, Hongwei, 2021. "Data-driven decision and analytics of collection and delivery point location problems for online retailers," Omega, Elsevier, vol. 100(C).

    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:gam:jrisks:v:11:y:2023:i:7:p:138-:d:1201431. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.