A Study on the Impact of SNS Usage Characteristics, Characteristics of Loan Products, and Personal Characteristics on Credit Loan Repayment

SNS 사용특성, 대출특성, 개인특성이 신용대출 상환에 미치는 영향에 관한 연구

  • 정원훈 (텐스페이스) ;
  • 이재순 (호서대학교 벤처대학원 벤처경영학과)
  • Received : 2023.09.10
  • Accepted : 2023.10.26
  • Published : 2023.10.31

Abstract

This study aims to investigate the potential of alternative credit assessment through Social Networking Sites (SNS) as a complementary tool to conventional loan review processes. It seeks to discern the impact of SNS usage characteristics and loan product attributes on credit loan repayment. To achieve this objective, we conducted a binomial logistic regression analysis examining the influence of SNS usage patterns, loan characteristics, and personal attributes on credit loan conditions, utilizing data from Company A's credit loan program, which integrates SNS data into its actual loan review processes. Our findings reveal several noteworthy insights. Firstly, with respect to profile photos that reflect users' personalities and individual characteristics, individuals who choose to upload photos directly connected to their personal lives, such as images of themselves, their private circles (e.g., family and friends), and photos depicting social activities like hobbies, which tend to be favored by individuals with extroverted tendencies, as well as character and humor-themed photos, which are typically favored by individuals with conscientious traits, demonstrate a higher propensity for diligently repaying credit loans. Conversely, the utilization of photos like landscapes or images concealing one's identity did not exhibit a statistically significant causal relationship with loan repayment. Furthermore, a positive correlation was observed between the extent of SNS usage and the likelihood of loan repayment. However, the level of SNS interaction did not exert a significant effect on the probability of loan repayment. This observation may be attributed to the passive nature of the interaction variable, which primarily involves expressing sympathy for other users' comments rather than generating original content. The study also unveiled the statistical significance of loan duration and the number of loans, representing key characteristics of loan portfolios, in influencing credit loan repayment. This underscores the importance of considering loan duration and the quantity of loans as crucial determinants in the design of microcredit products. Among the personal characteristic variables examined, only gender emerged as a significant factor. This implies that the loan program scrutinized in this analysis does not exhibit substantial discrimination based on age and credit scores, as its customer base predominantly consists of individuals in their twenties and thirties with low credit scores, who encounter challenges in securing loans from traditional financial institutions. This research stands out from prior studies by empirically exploring the relationship between SNS usage and credit loan repayment while incorporating variables not typically addressed in existing credit rating research, such as profile pictures. It underscores the significance of harnessing subjective, unstructured information from SNS for loan screening, offering the potential to mitigate the financial disadvantages faced by borrowers with low credit scores or those ensnared in short-term liquidity constraints due to limited credit history a group often referred to as "thin filers." By utilizing such information, these individuals can potentially reduce their credit costs, whereas they are supposed to accrue a more substantial financial history through credit transactions under conventional credit assessment system.

본 연구의 목적은 SNS 사용특성과 대출상품의 특성, 개인특성이 신용대출 상환에 미치는 영향력을 확인하여 SNS를 활용하는 대안신용평가가 기존 대출심사를 보완할 수 있는지를 검증하기 위함이다. 이를 위해 SNS를 활용하여 실제 대출심사에 반영하고 있는 T사 A 신용대출 프로그램 데이터를 이용하여 SNS 사용특성, 대출특성, 개인특성이 신용대출 상환에 미치는 영향력을 이항로지스틱 회귀분석을 통해 분석하였다. 분석결과 첫째, 사용자의 성격 및 개별 특성을 나타내는 프로필 사진의 경우 본인을 드러내지 않으려고 프로필 사진을 등록하지 않은 사람들과 달리 외향적인 경향의 사람이 선택할 가능성이 큰 본인 사진, 가족, 친구 등의 사적그룹 사진, 성실성의 경향이 강한 사람이 선택할 확률이 높은 취미 등 사회활동 사진, 개방성과 신경성이 높은 경향의 사람이 많이 선택하는 캐릭터·유머 사진, 개인의 사생활과 직결되는 가족·친구 등 사진을 SNS에 사용하는 사람들일수록 신용대출 상환에 적극적인 것으로 나타났다. 본인을 감추는 풍경 등의 사진 사용과 신용대출 상환과의 인과관계는 통계적으로 유의하지 않은 것으로 나타났다. 또한, SNS 사용량이 많을수록 신용대출 상환가능성이 높아지는 것으로 나타났다. 반면 SNS 소통량은 신용대출 상환가능성에 유의한 영향을 미치지 않는 것으로 나타났는데, 이는 소통량이라는 변수가 사용자가 직접 작성한 글보다는 타인의 댓글에 대한 공감을 나타내는 수동적 측면이 강하기 때문에 나타난 결과라 판단된다. 대출채권이 가진 특성을 나타내는 대출기간과 대출횟수도 신용대출 상환에 통계적으로 유의한 영향을 미치는 것으로 나타났다. 이는 대출기간과 대출횟수가 소액대출 상품에서도 중요한 영향요소로 고려되어야 함을 의미한다. 개인 특성 변수 중에서는 성별만 유의하게 나타났다. 이는 분석에 사용한 대출프로그램이 은행 등의 금융기관에서 대출이 불가능한 저신용 점수를 가진 20~30대 고객이 대부분인 상품으로 이용자의 나이와 신용점수에 있어서 차별성이 크지 않다는 것을 의미한다. 본 연구는 SNS사용량과 프로필 사진 등 기존 신용평가 연구에서 다루지 않은 변수를 사용하여 신용대출 상환과의 영향관계를 실증분석 하였다는 점에서 기존 연구와 차별성을 갖는다. SNS와 같은 주관적 비정형정보를 서민지원 대출심사에 활용한다면, 신용거래가 없어서 신용등급이 낮거나 단기적 유동성 함정에 빠진 차입자 즉 금융이력부족자(Thin filer)들이 신용거래 등의 금융 이력이 축적될 때까지의 신용비용에 대한 불이익을 감소시킬 수 있다는 점에서 의의가 있다.

Keywords

References

  1. Aichner, T., Grunfelder, M., Maurer, O., & Jegeni, D.(2021). Twenty-five years of social media: a review of social media applications and definitions from 1994 to 2019. Cyberpsychology, Behavior, and Social Networking, 24(4), 215-222. https://doi.org/10.1089/cyber.2020.0134
  2. Bernerth, J. B., Taylor, S. G., Walker, H. J., & Whitman, D. S.(2012). An empirical investigation of dispositional antecedents and performance-related outcomes of credit scores. Journal of applied psychology, 97(2), 469-478. https://doi.org/10.1037/a0026055
  3. Boyd, D. M., & Ellison, N. B.(2007). Social network sites: definition, history, and scholarship. Journal of Computer-Mediated Communication, 13, 210-230. https://doi.org/10.1111/j.1083-6101.2007.00393.x
  4. Chae, G. M.(2018). Advanced statistics using SPSS and AMOS. Seoul; Yangseowon.
  5. Choi, Y. N., & Hwang, H. S.(2016). A study on the effect of SNS user's personality on SNS usage type and SNS immersion: Focusing on Facebook. Journal of the Korean Internet Information Society, 17(3), 95-106.
  6. Ellison, N., & Boyd, D. M.(2013). Sociality through social network sites, In Dutton WH, ed. The oxford handbook of internet studies. Oxford, United Kingdom, Oxford University Press, 151-172.
  7. ETNews.(2016.04.17). The fintech goes to the financial industry. ETNews, Retrieved from https://zrr.kr/7lfq.
  8. Garton, L., Haythornthwaite, C., & Wellman, B.(1997). Studying online social networks. Journal of Computer-Mediated Communication, 3, 1-32.
  9. Ham, J. H., Kim, J. I., & Lee, Y. S.(2009). Risk analysis of household debt in Korea and implications for macroprudential supervision. Proceedings of the Winter Policy Symposium co-hosted by the Korean Financial Society, Financial Services Commission, and Financial Supervisory Service, 11.
  10. Han, B. S.(2015). An empirical analysis on individual credit risk. Doctoral Dissertation, Hanyang University Graduate School, Seoul.
  11. Harrast, S. A.(2004). Undergraduate Borrowing: A Study of Debtor Students and Their Ability to Retire Undergraduate Loans. Journal of Student Financial Aid, 34(1), 21-37. https://doi.org/10.55504/0884-9153.1081
  12. Herr, E., & Burt, L.(2005). Predicting Student Loan Default for the University of Texas at Austin. Journal of Student Financial Aid, 35(2), 27-49.
  13. International Financial Center(2019). Development of new credit evaluation methods by recent overseas fintech companies. Issue Analysis. Retrieved from 2023.08.31. https://zrr.kr/pBtW.
  14. Jung, K. S.(2004). A study on individual credit evaluation. Doctoral Dissertation, Hongik University, Seoul.
  15. Jeong, W. H., & Lee, J. S.(2021). A study on the effect of SNS usage characteristics on credit loan repayment. Humanities and Social Sciences 21, 12(4), 2741-2756.
  16. Kang, H. K., Bin, K. B., Lee, H. G., & Koo, B. H.(2020). A study on the financial status of small and medium-sized enterprises and the determinants of credit rating. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 15(6). 135-154.
  17. Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S.(2018). Advances in social media research: past, present and future. Information Systems Frontiers, 20(3), 531-558.
  18. Kim, D. W.(2017). The influence of language expression on loan repayment in P2P lending. Journal of the Korean Management Association, 30(6), 1031-1054.
  19. Kim, M. R., & Kim, H. S.(2011). Debt usage intention of defaulters in their 20s and 30s: Based on the theory of rational behavior. Journal of Family and Quality of Life, 29(6), 9-25.
  20. Kim, S. A., & Park, J. E.(2016). A study on the difference between personal and social influences in the use and spread of social network services. Journal of New Industry Management, 34(1), 73-102.
  21. Kim, S. S., Lee, Y. K., & Lee, J. M.(2018). A study on delinquency behavior according to the debt characteristics of Korean debt-holding households. Proceedings of the Korean Home Management Association Conference, 105-115.
  22. Korea Credit Information Service(2018). Changes and implications of personal credit evaluation models. CIS Issue Report, 2018-4.
  23. Lee, D. G., Jeon, S., Jung, J. I., & Byun, D. J.(2014). A study on the delinquency decision factors and vulnerabilities of household debt in Korea. Financial Research, 28(2), 137-178.
  24. Lee, J. K.(2020). The effect of household characteristics on the possibility of delinquency in credit loans. Doctoral Dissertation, Busan National University Graduate School, Seoul.
  25. Lee, J. S.(2019). Analysis of individual borrowers' loan behaviors and estimation of potential bankruptcy probability. Journal of Financial Management Research, 36(1), 63-94.
  26. Lee, J. Y., Hong, J. S., & Yoon, J. H.(2013). A study on the types of multiple selves through KakaoTalk profile images. Archives of Design Research, 26(4), 181-204.
  27. Liu, L., Preotiuc-Pietro, D., Samani, Z. R., Moghaddam, M. E., & Ungar, L.(2016). Analyzing personality through social media profile picture choice. Tenth international AAAI conference on web and social media. 211-220.
  28. McEvoy, M. J.(2014). Enabling financial inclusion through alternative data. Mastercard Advisors: Bentonville, AR, USA.
  29. Min, K. R., Ko, H. J., Lee, J. H., & Wi, K. W.(2007). An empirical study on the default factors of domestic credit card users. Journal of the Academic Association of Business Administration, 20(4), 1953-1976.
  30. Nosko, A., Wood, E., & Molema. S.(2010). All about me: Disclosure in online social networking profiles: The case of Facebook. Computers in human behavior, 26(3), 406-418. https://doi.org/10.1016/j.chb.2009.11.012
  31. Ntwiga, D. B., & Weke, P.(2016). Consumer lending using social media data. International Journal of Scientific Research and Innovative Technology, 3(2), 1-8
  32. Oh, S. T.(2019). A study on predicting non-performing loan claims. Doctoral Dissertation, Woodeok University, Gyeongbuk.
  33. Park, J. O.(2016). A study on consumer financial debt delinquency and default. Doctoral Dissertation, Seoul National University, Seoul.
  34. Park, S. B., & Oh, Y. H.(2020). The effects of individual characteristics, loan characteristics, and interest rate characteristics on the possibility of delinquency. Asia-Pacific Business Studies, 11(3), 63-78.
  35. Polena, M., & Regner, T.(2018). Determinants of borrowers' default in P2P lending under consideration of the loan risk class. Games, 9(4), 82.
  36. PWC(2015). Is it time for consumer lending to go social? How to strengthen underwriting and grow your customer base with social media data. Consumer Finance Group, Retrieved from www.pwc.com/consumerfinance
  37. Romm, C., Pliskin, N., & Clarke, R.(1997). Virtual communities and society: toward an integrative three phase model. International Journal of Information Management, 17, 261-270.
  38. Ross, C., Orr, E. S., Sisic, M., Arseneault, J. M., Simmering, M. G., & Orr, R. R.(2009). Personality and motivations associated with facebook use. Computers in human behavior, 25(2), 578-586. https://doi.org/10.1016/j.chb.2008.12.024
  39. Serrano-Cinca, C., Gutierrez-Nieto, B., & Lopez-Palacios, L.(2015). Determinants of default in P2P lending. PLoS ONE, 10(10), e0139427
  40. Shih, C. F., & Venkatesh, A.(2004). Beyond adoption: Development and application of a use-diffusion model. Journal of marketing, 68(1), 59-72. https://doi.org/10.1509/jmkg.68.1.59.24029
  41. Shin, D. H., & Chae, M. S.(2012). An empirical study on the failure factors of online P2P loan repayment. Journal of the Korean Management Association, 25(5), 2233-2254.
  42. Steiner, M., & Teszler, N.(2005). Multivariate Analysis of Student Loan Defaulters at Texas Aandm University. TG(Texas Guaranteed Student Loan Corporation).
  43. Whitty, M. T., Doodson, J., Creese, S., & Hodges, D.(2018). A picture tells a thousand words: What Facebook and Twitter images convey about our personality. Personality and Individual Differences, 133, 109-114. https://doi.org/10.1016/j.paid.2016.12.050
  44. Wilson, R. E., Gosling, S. D., & Graham, L. T.(2012). A review of Facebook research in the social sciences. Perspectives on psychological science, 7(3), 203-220. https://doi.org/10.1177/1745691612442904
  45. Wu, Y. C. J., Chang, W. H., & Yuan, C. H.(2015). Do Facebook profile pictures reflect user's personality? Computers in Human Behavior, 51, 880-889. https://doi.org/10.1016/j.chb.2014.11.014
  46. Yoon, J. E., & Kim, H. S.(2017). User perception types on personalized spaces of mobile messenger applications. Design Convergence Studies, 16(1), 15-36.
  47. Yu, J., Yao, J. R., & Chen, Y. G.(2019). Credit scoring with AHP and fuzzy comprehensive evaluation based on behavioural data from weibo platform. Tehnicki vjesnik, 26(2), 462-470.