Big data and credit risk assessment: a bibliometric review, current streams, and directions for future research

Figures & data

Table 1. Search queries using keywords

Description	Conditions	Number of documents
The search query of TITLE -ABS—KEY	TITLE-ABS-KEY ((“big data” AND “credit risk”) OR (“big data” AND “credit expos*”) OR (“big data” AND “ loan risk”) OR (“big data” AND “default risk”) OR (“big data” AND “credit default”) OR (“big data” AND “risk of default”) OR (“big data” AND “insolvency risk”) OR (“big data” AND “exposure at default”) OR (“big data” AND “downgrade risk”) OR (“big data” AND “credit rating”) OR (“big data” AND “credit score”) OR (“big data” AND “credit rate”) OR (“big data” AND “credit assessment”) OR (“big data” AND “credit evaluation”) OR (“big data” AND “credit analysis”) OR (“big data” AND “credit valuation”) OR (“big data” AND “credit appraisal”) OR (“big data” AND “credit grading”) OR (“big data” AND “ credit check”) OR (“big data” AND “credit history”))	219 documents
Search query after refining	TITLE-ABS-KEY ((“big data” AND “credit risk”) OR (“big data” AND “credit expos*”) OR (“big data” AND “ loan risk”) OR (“big data” AND “default risk”) OR (“big data” AND “credit default”) OR (“big data” AND “risk of default”) OR (“big data” AND “insolvency risk”) OR (“big data” AND “exposure at default”) OR (“big data” AND “downgrade risk”) OR (“big data” AND “credit rating”) OR (“big data” AND “credit score”) OR (“big data” AND “credit rate”) OR (“big data” AND “credit assessment”) OR (“big data” AND “credit evaluation”) OR (“big data” AND “credit analysis”) OR (“big data” AND “credit valuation”) OR (“big data” AND “credit appraisal”) OR (“big data” AND “credit grading”) OR (“big data” AND “ credit check”) OR (“big data” AND “credit history”)) AND (LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cr”) OR LIMIT-TO (DOCTYPE, “ch”) OR LIMIT-TO (DOCTYPE, “ed”) OR LIMIT-TO (DOCTYPE, “re”)) AND (LIMIT-TO (LANGUAGE, “English”))	214 documents

Table 2. Most occurrence keywords

Rank	Keyword	Occurrences	Rank	Keyword	Occurrences
1	Big Data	145	11	Credit Risks	20
2	Risk Assessment	80	12	Artificial Intelligence	19
3	Finance	43	13	Forecasting	19
4	Data Mining	35	14	Financial Institution	15
5	Credit Evaluations	27	15	Information Management	15
6	Machine Learning	27	16	Learning Algorithms	15
7	Risk Management	25	17	Neural Networks	15
8	Learning Systems	22	18	Credit Risk Assessment	14
9	Credit Risk	21	19	Credit Risk Management	14
10	Decision Trees	21	20	Credit Ratings	13

Figure 1. Credit risk and big data publications in the last nine years.

Figure 2. Shows the number of articles published by different organizations.

Table 3. Leading sources by citations

Rank	Source	Documents	Citations
1	Electronic Commerce Research and Applications	3	96
2	Policy and Internet	1	66
3	Financial Innovation	3	59
4	Risks	1	47
5	Competition and Change	1	36
6	2016 3rd International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2016	2	29
7	Industrial Management and Data Systems	1	28
8	Journal of Business Research	1	27

Table 4. Leading sources by number of documents

Rank	Source	Documents	Citations
1	Journal of Physics: Conference Series	15	18
2	ACM International Conference Proceeding Series	10	7
3	Advances in Intelligent Systems and Computing	10	4
4	Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	7	12
5	Electronic Commerce Research and Applications	3	96
6	Financial Innovation	3	59
7	Information Systems and E-Business Management	3	8

Figure 3. Network mapping of the leading sources.

Figure 4. Shows network mapping of the leading authors.

Table 5. Leading authors by the number of articles and citations

Rank	Author	Documents	Citations
1	Zhang Y.	6	32
2	Li X.	4	20
3	Hayashi Y.	3	39
4	Song X.	3	28
5	Wang H.	3	17
6	Yang X.	3	16
7	Yu Y.	3	83
1	Yu Y.	3	83
2	Ma X.	2	77
3	Yang Q.	2	76
4	Niu X.	1	76
5	Sha J.	1	76
6	Wang D.	1	76

Figure 5. Shows network mapping of the most influential affiliations.

Table 6. Leading affiliations by citations

Rank	Organization	Documents	Citations
1	Asia Australia Business College, Liaoning University	1	76
2	Harbin Institute of Technology	1	76
3	School of Statistics, Dongbei University of Finance and Economics	1	76
4	Department of Communication Studies, University of Michigan	1	66
5	Department of Political Science, University of Michigan	1	66
6	Ford School of Public Policy, University of Michigan	1	66
7	ISR Center for Political Studies, University of Michigan	1	66

Figure 6. Network mapping of the most influential countries.

Table 7. Shows the leading countries by number of documents and by citations

Rank	Country	Documents	Citations	Rank	Country	Documents	Citations
1	China	136	348	1	China	136	348
2	United States	22	53	2	Viet Nam	1	173
3	United Kingdom	10	22	3	Hong Kong	4	58
4	India	9	46	4	Italy	4	55
5	Japan	5	40	5	United States	22	53
6	Taiwan	5	48	6	Taiwan	5	48
7	Canada	4	41	7	France	1	47
8	Hong Kong	4	58	8	India	9	46
9	Italy	4	55	9	Canada	4	41

Table 8. Most cited documents

Rank	Document	Citations	Rank	Document	Citations
1	(Shen et al., 2018)	76	11	(Çığşar & Ünal, 2019)	20
2	(Liang et al., 2018)	66	12	(Hayashi et al., 2016)	18
3	(Addo et al., 2018b)	47	13	(Zhang et al., 2015)	16
4	(Aitken, 2017)	36	14	(Xia et al., 2018)	15
5	(Yan et al., 2015)	30	15	(Zhu et al., 2018)	15
6	(Pérez-Martín et al., 2018b)	27	16	(Saabni, 2016)	15
7	(Favilukis et al., 2015)	27	17	(Botev, 2017)	15
8	(Agarwal, Chomsisengphet, Liu, song & Souleles, 2018)	25	18	(Tsao et al., 2017)	14
9	(Pan et al., 2021)	24	19	(Turkson et al., 2016)	14
10	(Hayashi, 2016)	21	20	(Lin & Shen, 2015)	13

Figure 7. Shows network mapping of the most cited documents.

Shen, F., Ma, X., Li, Z., Xu, Z., & Cai, D. (2018). An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation. Information Sciences, 428, 105–119. https://doi.org/10.1016/j.ins.2017.10.045

 Web of Science ®Google Scholar

Çığşar, B., & Ünal, D. (2019). Comparison of data mining classification algorithms determining the default risk. Scientific Programming, 2019, 1–8. https://doi.org/10.1155/2019/8706505

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Liang, F., Das, V., Kostyuk, N., & Hussain, M. M. (2018). Constructing a data-driven society: China’s social credit system as a state surveillance infrastructure: China’s social credit system as state surveillance. Policy & Internet, 10(4), 415–453. https://doi.org/10.1002/poi3.183

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Hayashi, Y., Tanaka, Y., Takagi, T., Saito, T., Iiduka, H., Kikuchi, H., Bologna, G., & Mitra, S. (2016). Recursive-rule extraction algorithm with j48graft and applications to generating credit scores. Journal of Artificial Intelligence and Soft Computing Research, 6(1), 35–44. https://doi.org/10.1515/jaiscr-2016-0004

 Google Scholar

Addo, P., Guegan, D., & Hassani, B. (2018b). Credit risk analysis using machine and deep learning models. Risks, 6(2), 38. https://doi.org/10.3390/risks6020038

 Web of Science ®Google Scholar

Zhang, S., Xiong, W., Ni, W., & Li, X. (2015). Value of big data to finance: Observations on an internet credit service company in China. Financial Innovation, 1(1), 17. https://doi.org/10.1186/s40854-015-0017-2

 Google Scholar

Aitken, R. (2017). ‘All data is credit data’: Constituting the unbanked. Competition & Change, 21(4), 274–300. https://doi.org/10.1177/1024529417712830

 Web of Science ®Google Scholar

Xia, Y., Liu, C., Da, B., & Xie, F. (2018). A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Systems with Applications, 93(1), 182–199. https://doi.org/10.1016/j.eswa.2017.10.022

 Web of Science ®Google Scholar

Yan, J., Yu, W., & Zhao, J. L. (2015). How signaling and search costs affect information asymmetry in P2P lending: The economics of big data. Financial Innovation, 1(1), 19. https://doi.org/10.1186/s40854-015-0018-1

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Zhu, B., Yang, W., Wang, H., & Yuan, Y. (2018). A hybrid deep learning model for consumer credit scoring. 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD), 205–208. Chengdu: IEEE. https://doi.org/10.1109/ICAIBD.2018.8396195

 Google Scholar

Pérez-Martín, A., Pérez-Torregrosa, A., & Vaca, M. (2018b). Big data techniques to measure credit banking risk in home equity loans. Journal of Business Research, 89, 448–454. https://doi.org/10.1016/j.jbusres.2018.02.008

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Saabni, R. (2016). Recognizing handwritten single digits and digit strings using deep architecture of neural networks. 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR), 1–6. Lodz: IEEE. https://doi.org/10.1109/ICAIPR.2016.7585206

 Google Scholar

Favilukis, J., Lin, X., & Zhao, X. (2015). The Elephant in the room: The impact of labor obligations on credit risk. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2648763

 Google Scholar

Botev, Z. I. (2017). The normal law under linear restrictions: Simulation and estimation via minimax tilting. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(1), 125–148. https://doi.org/10.1111/rssb.12162

 Web of Science ®Google Scholar

Agarwal, S., Chomsisengphet, S., Liu, C., Song, C., & Souleles, N. S. (2018). Benefits of relationship banking: Evidence from consumer credit markets. Journal of Monetary Economics, 96, 16–18. https://doi.org/10.1016/j.jmoneco.2018.02.005

 Web of Science ®Google Scholar

Tsao, Y.-C., Lee, P.-L., Chen, C.-H., & Liao, Z.-W. (2017). Sustainable newsvendor models under trade credit. Journal of Cleaner Production, 141(10), 1478–1491. https://doi.org/10.1016/j.jclepro.2016.09.228

 Web of Science ®Google Scholar

Pan, W.-F., Wang, X., Wu, G., & Xu, W. (2021). The COVID-19 pandemic and sovereign credit risk. China Finance Review International, 11(3), 287–301. https://doi.org/10.1108/CFRI-01-2021-0010

 Web of Science ®Google Scholar

Turkson, R. E., Baagyere, E. Y., & Wenya, G. E. (2016). A machine learning approach for predicting bank credit worthiness. 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR), 1–7. Lodz: IEEE. https://doi.org/10.1109/ICAIPR.2016.7585216

 Google Scholar

Hayashi, Y. (2016). Application of a rule extraction algorithm family based on the Re-RX algorithm to financial credit risk assessment from a Pareto optimal perspective. Operations Research Perspectives, 3, 32–42. https://doi.org/10.1016/j.orp.2016.08.001

 Web of Science ®Google Scholar

Lin, Y.-M., & Shen, C.-A. (2015). Family firms’ credit rating, idiosyncratic risk, and earnings management. Journal of Business Research, 68(4), 872–877. https://doi.org/10.1016/j.jbusres.2014.11.044

 Web of Science ®Google Scholar