千里之堤溃于蚁穴：商业银行内部欺诈风险形成机理的随机实验研究

doi:10.16381/j.cnki.issn1003-207x.2022.0188

Abstract

Abstract:

Human-induced operational risk is the operational risk directly caused by employees' misbehavior. It is the type of operational risk with the highest frequency and loss intensity in commercial banks. According to the difference in the initiative of misbehavior， the human-induced operational risk can be subdivided into human error risk and internal fraud risk. The nature and impact of internal fraud risk caused by employees’willful misbehavior are more serious，such as defrauding and embezzling property， or violating laws， regulatory rules， or internal systems.Researches pointed out that the key to optimizing operational risk measurement models and improving risk management effectiveness lies in the mastery of risk drivers. Therefore， it focuses on the formation mechanism of internal fraud risk in this paper， in order to provide theoretical support and basis for operational risk management. At present， there are few studies on the formation mechanism of internal fraud risk in the banking industry， but experimental evidence and theoretical explanations have been provided from the perspectives of institutional culture and colleague behavior. In addition， numerous studies on unethical decision-making provide evidence on individual psychological characteristics. Based on integrating the above characteristic factors， the scenario characteristic factor of internal fraud is introduced， and a systematic framework of the formation mechanism of internal fraud risk among employees in the banking industry is constructed.In this research， a randomized controlled trial is used to test this theoretical framework. Taking 244 employees of a commercial bank as effective samples， a random experiment in a completely unsupervised daily environment is carried out. The participants were randomly divided into three groups according to different decision-making scenarios to intervene with scenario characteristic.On this basis， mature scale tools are used to measure the personal psychological characteristics of employees and situational characteristics in the decision-making process， and a coin toss game is used to observe theinstitutional ethical culture. In addition， the actual misbehavior data of the bank's employees before and after the experiment are obtained based on the research project in cooperation with the bank.The research shows that scenario characteristic has a significant impact on the decision-making results and decision-making process of bank employees' internal fraud： the weaker the pressure employees feel in the scenario， the stronger the fraud intention； in different moral intensity scenarios， the impact of personal psychological characteristics and organizational environmental characteristics is different. The positive impact of personal psychological characteristics is more significant in low-intensity scenarios， and the inhibitory effect of organizational environmental characteristics， especially department culture， and the same group infection of colleague misbehavior are more significant in high-intensity situations.This research further enriches the theoretical research on the formation mechanism of operational risk， especially human-induced operational risk and provides a behavioral motivation explanation for the loss distribution characteristics of "low risk and high frequency" of operational risk in banking industry. In addition， the research conclusions of this paper provide a theoretical and empirical basis for commercial banks to implement key risk indicator tools， which will help commercial banks optimize operational risk measurement models and improve dynamic control technology. Moreover， management inspiration is provided for commercial banks to optimize the dynamic prevention and control mechanism of human-induced operational risk.

Key words: human-induced operational risk, internal fraud risk, misbehavior, scenario characteristic, randomized controlled trial

CLC Number:

F832
C934

Haohua Li, Xindan Li, Binqing Xiao. The Embankment of a Thousand Miles Collapses in the Ant's Nest:An Experimental Study on the Formation Mechanism of Internal Fraud Risk in the Banking Industry[J]. Chinese Journal of Management Science, 2024, 32(11): 25-36.

Figures/Tables 8

变量	被解释变量：IFB Index
变量	(1)	(2)	(3)	(4)
MI	-1.591^***	-1.575^***	-1.687^***	-4.710^*
MI	(-8.38)	(-5.73)	(-5.38)	(-1.72)
AFB		1.873^**	1.570^*	1.579^*
AFB		(2.08)	(1.65)	(1.69)
PBC		-0.038	0.032	-0.005
PBC		(-0.22)	(0.16)	(-0.03)
MO		-0.260	-0.236	-0.207
MO		(-1.50)	(-1.27)	(-1.07)
DC		-5.096^***	-6.037^***	-5.993^***
DC		(-4.60)	(-4.54)	(-4.44)
CFB		0.279^*	0.313	0.302
CFB		(1.68)	(1.61)	(1.53)
MD			-0.036^**	-0.091
MD			(-2.13)	(-0.87)
SIT			-0.106	-0.087
SIT			(-0.24)	(-0.19)
SEX			-0.943^**	-3.779
SEX			(-1.97)	(-1.27)
AGE			-0.055	-0.293
AGE			(-1.59)	(-1.42)
EDU			-2.145^***	-5.493
EDU			(-2.66)	(-1.22)
TEC			-0.286	-0.271
TEC			(-0.54)	(-0.48)
MD×MI				0.009
MD×MI				(0.50)
SEX×MI				0.456
SEX×MI				(0.95)
AGE×MI				0.038
AGE×MI				(1.16)
EDU×MI				0.554
EDU×MI				(0.76)
截距项	是	是	是	是
观测数	244	244	244	244
似然比检验	$X 2$ (1)=119.52 p=0.000	$X 2$ (6)=161.05 p=0.000	$X 2$ (12)=178.44 p=0.000	$X 2$ (16)=180.58 p=0.000
PseudoR²	0.387	0.522	0.578	0.585

变量	被解释变量：IFB Index
变量	子样本1：情境一和情境二	子样本2：情境一和情境三
AFB	2.760^*	3.192^**
AFB	(1.76)	(2.22)
PBC	0.154	0.235
PBC	(0.51)	(0.78)
MO	-1.399^***	-1.444^***
MO	(-3.82)	(-3.91)
DC	-0.762	0.056
DC	(-0.30)	(0.02)
CFB	-0.195	-0.215
CFB	(-0.43)	(-0.46)
CASE₂×AFB	-1.724
CASE₂×AFB	(-0.73)
CASE₂×PBC	0.416
CASE₂×PBC	(1.01)
CASE₂×MO	0.522
CASE₂×MO	(1.16)
CASE₂×DC	-6.505^*
CASE₂×DC	(-1.84)
CASE₂×CFB	0.676
CASE₂×CFB	(1.22)
CASE₂	1.874
CASE₂	(0.40)
CASE₃×AFB		-0.438
CASE₃×AFB		(-0.21)
CASE₃×PBC		1.315**
CASE₃×PBC		(2.21)
CASE₃×MO		1.774^***
CASE₃×MO		(3.30)
CASE₃×DC		-9.494^**
CASE₃×DC		(-2.17)
CASE₃×CFB		1.493^**
CASE₃×CFB		(2.17)
CASE₃		-8.355
CASE₃		(-1.51)
控制变量	是	是
截距项	是	是
观测数	163	164
似然比检验	$X 2$ (17)=116.061 p=0.000	$X 2$ (17)=129.44 p=0.000
PseudoR²	0.561	0.619

变量	被解释变量：IFB Index
变量	情境一	情境二	情境三	Difference 1 vs 2	Difference 1 vs 3
AFB	4.006^**	1.566	2.546	-2.441	-1.461
AFB	(2.08)	(0.87)	(1.61)	-2.441	-1.461
PBC	0.334	0.515	1.657^***	0.181	1.323^**
PBC	(0.90)	(1.57)	(2.76)	0.181	1.323^**
MO	-1.785^***	-0.928^***	0.336	0.857	2.122^**
MO	(-3.40)	(-3.20)	(0.88)	0.857	2.122^**
DC	-0.195	-7.284^***	-10.516^**	-7.088^*	-10.320^**
DC	(-0.06)	(-2.87)	(-2.54)	-7.088^*	-10.320^**
CFB	-0.392	0.617	1.440^***	1.009^*	1.832^**
CFB	(-0.66)	(1.59)	(2.72)	1.009^*	1.832^**
控制变量	是	是	是
截距项	是	是	是
观测数	83	80	81
似然比检验	$X 2$ (11)=74.76 p=0.000	$X 2$ (11)=48.39 p=0.000	$X 2$ (11)=62.01 p=0.000
PseudoR²	0.696	0.487	0.610

变量	Logit回归		OLS回归
变量	FB_Yes	FB_High	FB
IFB Index	0.746^**	0.900^***	0.579^***
IFB Index	(2.29)	(2.98)	(3.03)
CFB	0.780^***	0.787^***	0.530^***
CFB	(3.44)	(3.01)	(4.39)
∆ Asset	0.226	0.402	0.111
∆ Asset	(0.38)	(0.85)	(0.36)
∆ EVA	-0.038	0.053	-0.021
∆ EVA	(-0.29)	(0.49)	(-0.29)
∆ GDP	-1.263	-12.895	-7.711
∆ GDP	(-0.06)	(-0.72)	(-0.68)
∆ Consume	0.332	-0.166	0.147
∆ Consume	(1.36)	(-0.72)	(0.97)
控制变量	是	是	是
截距项	是	是	是
观测数	244	244	244
似然比检验	$X 2$ (11)=31.56 p=0.001	$X 2$ (11)=28.55 p=0.003	$F$ =3.62 p=0.000
Pseudo/Adj. R²	0.113	0.085	0.106

References 48

1	李建平，丰吉闯，宋浩，等. 风险相关性下的信用风险、市场风险和操作风险集成度量［J］. 中国管理科学， 2010， 18（1）： 18-25.
	Li J P， Feng J C， Song H， et al. Integrating creditmarket and operational risk based on risk correlation［J］. Chinese Journal of Management Science， 2010， 18（1）： 18-25.
2	Tong B， Diao X D， Wu C F. Operational risk quantified with spectral risk measures： A refined closed-form approximation［J］. Quantitative Finance， 2019， 19（7）： 1221-1242.
3	Berg T， Burg V， Gombovii A， et al. On the rise of fintech-credit scoring using digital footprints［J］. Review of Financial Studies， 2019， 109（1）： 198-223．
4	莫建明，吕刚，高翔，等.Pareto分布下损失分布法度量误差变动规律［J］.中国管理科学，2017，25（11）：134-142.
	Mo J M， Lv G， Gao X， et al. Change rule of loss distribution approach’s measurement error under the pareto distribution［J］. Chinese Journal of Management Science，2017，25（11）：134-142.
5	陆静，唐小我.基于贝叶斯网络的操作风险预警机制研究［J］.管理工程学报，2008，22（4）：56-61.
	Lu J， Tang X W. Building up the pre-warning system of operational risk by means of Bayesian networks［J］. Journal of Industrial Engineering Management，2008，22（4）：56-61.
6	肖斌卿，李心丹，徐雨茜，等. 流程、合规与操作风险管理［J］. 管理科学学报， 2017， 20（12）： 117-123.
	Xiao B Q， Li X D， Xu Y Q， et al. Process，compliance and operational risk management［J］. Journal of Management Sciences in China， 2017， 20（12）： 117-123.
7	朱晓谦，李建平. 相关性下的银行风险集成研究综述［J］. 中国管理科学， 2020， 28（8）： 1-14.
	Zhu X Q， Li J P. A review of bank risk aggregation［J］. Chinese Journal of Management Science， 2020， 28（8）： 1-14.
8	汤凌霄，张艺霄. 基于网络分析法的我国商业银行操作风险影响因素实证分析［J］. 中国软科学， 2012（8）： 143-151.
	Tang L X， Zhang Y X. An empirical analysis of influencing factors of operational risks for commercial banks in China Based on ANP［J］. China Soft Science， 2012（8）： 143-151.
9	Xu Y Q， Pinedo M， Xue M. Operational risk in financial services： A review and new research opportunities［J］. Production & Operations Management， 2016， 26（3）： 426-445.
10	Reason J. Human error［M］. Cambridge， UK.： Cambridge University Press， 1990.
11	Thaler R H. Misbehaving： The making of behavioral economics［M］. New York： W.W.Norton & Company， 2015.
12	高丽君，丰吉闯.基于变位置参数贝叶斯预测银行内部欺诈研究［J］.中国管理科学，2012，20（2）：20-25.
	Gao L J， Feng J C. The Bayesian inference research on the internal fraud of Chinese commercial banks based on varying location parameter［J］. Chinese Journal of Management Science，2012，20（2）：20-25.
13	梁力军，李志祥. 我国商业银行操作风险管理能力影响因素及管理状况分析［J］. 管理评论， 2010， 22（5）： 11-19.
	Liang L J， Li Z X.Factors thataffect the operational risk management capacity of Chinese commercial banks and the management status of the banks［J］. Management Review， 2010， 22（5）： 11-19.
14	Cohn A， Fehr E， Maréchal M A. Business culture and dishonesty in the banking industry［J］. Nature， 2014， 516： 86-89.
15	Cohn A， Fehr E， Maréchal M A. Do professional norms in the banking industry favor risk-taking？［J］. The Review of Financial Studies， 2017， 30（11）： 3801-3823.
16	Sheedy E A， Zhang L， Tam K C H. Incentives and culture in risk compliance［J］. Journal of Banking & Finance， 2019， 107： 105611.
17	Buchan H F. Ethical decision making in the public accounting profession： An extension of Ajzen's theory of planned behavior［J］. Journal of Business Ethics， 2005， 61（2）： 165-181.
18	李心丹，宋素荣，卢斌，等. 证券市场内幕交易的行为动机研究［J］. 经济研究， 2008， 43（10）： 65-79+92.
	Li X D， Song S R， Lu B， et al. Research on the motivation of insider trading［J］. Economic Research Journal， 2008， 43（10）： 65-79+92.
19	Dimmock S G， William C G， Graham N P. Is fraud contagious？ Coworker influence on misconduct by financial advisors［J］. The Journal of Finance， 2018， 73（3）： 1417-1450.
20	Jones T M. Ethical Decision making by individuals in organizations： An issue-contingent model［J］. Academy of Management Review， 1991， 16（2）： 366-395.
21	Schwartz M. Ethical decision-making theory：An integrated approach［J］. Journal of Business Ethics， 2016， 139（4）：755–776.
22	Beck L， Ajzen I. Predicting dishonest actions using the theory of planned behavior［J］. Journal of Research in Personality， 1991， 25（3）： 285-301.
23	Adams P， Hunt S， Palmer C， et al. Testing the effectiveness of consumer financial disclosure： Experimental evidence from savings accounts［J］. Journal of Financial Economics， 2021， 141（1）： 122-147.
24	Kahneman D. Maps of bounded rationality： Psychology for behavioral economics［J］. American Economic Review， 2003， 93（5）： 1449-1475.
25	Bruner DM. Does decision error decrease with risk aversion？［J］.Experimental Economics，2017，20： 259-273.
26	Charness G， Gneezy U， Imas A. Experimental methods： Eliciting risk preferences［J］. Journal of Economic Behavior & Organization， 2013， 87（2）： 43-51.
27	Tan F Y， Netessine S. When does the devil make work？ An empirical study of the impact of workload on worker productivity［J］. Management Science， 2012， 60（6）：1574-1593.
28	陈仕华，李维安. 并购溢价决策中的锚定效应研究［J］. 经济研究， 2016， 51（6）： 114-127.
	Chen S H， Li W A. A study on anchoring effects of acquisition premiums decision［J］. Economic Research Journal， 2016， 51（6）： 114-127.
29	Lehnert K， Park Y H， Singh N. Research note and review of the empirical ethical decision-making literature： Boundary conditions and extensions［J］. Journal of Business Ethics， 2015， 129（1）： 195-219.
30	Rest J R. Moral development： Advances in research and theory［M］. New York： Praeger， 1986.
31	Ajzen I. The theory of planned behavior［J］. Organizational Behavior and Human Decision Processes， 1991， 50（2）： 179-211.
32	Lonsdale D. Intentions to cheat： Ajzen's planned behavior and goal-related personality facets［J］. The Journal of Psychology， 2017， 151（2）： 113-129.
33	Rousselet E， Brial B， Cadario R， et al. Moral Intensity， issue characteristics， and ethical issue recognition in sales situations［J］. Journal of Business Ethics， 2020， 163： 347-363.
34	Robin D P， Reidenbach R E， Forrest P J. The perceived importance of an ethical issue as an influence on the ethical decision-making of ad managers［J］. Journal of Business Research， 1996， 35（1）： 17-28.
35	Shneor R， Munim Z H. Reward crowdfunding contribution as planned behaviour： An extended framework［J］. Journal of Business Research， 2019， 103： 56-70.
36	Murphy P R. Attitude， Machiavellianism and the rationalization of misreporting［J］. Accounting Organizations and Society， 2012， 37（4）： 242-259.
37	Fichter R.Do the Right thing！ Developing ethical behavior in financial institutions［J］. Journal of Business Ethics， 2018， 151（1/2）：1-16.
38	Kohlberg L， Power C. Moral development， religious thinking， and the question of a seventh stage［J］. Zygon， 1981， 16（3）： 203-259.
39	Rest J， Narvaez D， Bebeau M， et al. A neo-Kohlbergian approach： The DIT and schema theory［J］. Educational Psychology Review， 1999， 11（4）： 291-324.
40	刘彧彧，张佳良，刘雨萌. 伦理气氛下道德强度对组织员工伦理决策行为的影响研究［J］. 管理学报， 2015， 12（8）： 1217-1223.
	Liu Y Y， Zhang J L， Liu Y M. An empirical study of moral intensity’s effects on organizational employees’ ethical decision-making behavior［J］. Chinese Journal of Management， 2015， 12（8）： 1217-1223.
41	Arthur Andersen & Co. Business ethics program［EB/OL］.（1992-09）［2024-10-09］.
42	Nelson A. Do small errors need to be reported？［EB/OL］.（2013-06-03）［2024-10-09］.
43	Banerjee D， Cronan T P， Jones T W. Modeling IT ethics： A study in situational ethics［J］. Mis Quarterly， 1998， 22（1）： 31-60.
44	Immerman D. Moral pickles， moral dilemmas， and the obligation preface paradox［J］. Philosophical Studies， 2019， 176： 2087-2101.
45	Gneezy U， Potters J. An experiment on risk taking and evaluation periods［J］. The Quarterly Journal of Economics， 1997， 112（2）： 631-645.
46	Kahneman D. Thinking， fast and slow［M］. New York： Farrar， Straus and Giroux， 2011.
47	Oechssler J， Roider A， Schmitz P W. Cognitive abilities and behavioral biases［J］. Journal of Economic Behavior & Organization， 2009， 72（1）： 147-152.
48	Mabwe K， Ring P J， Webb R. Operational risk and the three lines of defence in UK financial institutions： Is three really the magic number？［J］. Journal of Operational Risk， 2017， 12（1）： 53-69.

变量名称	变量符号	度量量表
道德强度	MI	李克特七分制双题量表^[34]
欺诈行为意图	IFB	李克特七分制单题量表^[31]
欺诈行为态度	AFB	二元单题量表^[31]
感知行为控制	PBC	李克特七分制多题量表^[31]
道德责任	MO	李克特七分制单题量表^[43]
道德发展阶段	MD	确定性问题测试(DIT)^[35]

分组	统计量	道德发展阶段 MD	岗位	性别	年龄	学历	职业技术资格	道德强度
分组	统计量	道德发展阶段 MD	SIT	SEX	AGE	EDU	TEC	MI
全样本 N=244	Mean	41.69	1.35	0.49	37.39	1.78	0.41	6.18
	Std.Dev.	14.43	0.63	0.50	9.96	0.46	0.49	1.15
	Min	13.33	1.00	0.00	0.00	1.00	0.00	3.00
	Max	53.33	3.00	1.00	59.00	3.00	1.00	7.00
情境一 N=83	Mean	42.33	1.36	0.49	35.99	1.86	0.33	5.90
	Std.Dev.	14.92	0.69	0.50	9.17	0.45	0.47	1.28
	Min	13.33	1.00	0.00	23.00	1.00	0.00	3.00
	Max	53.33	3.00	1.00	55.00	3.00	1.00	7.00
情境二 N=80	Mean	42.18	1.33	0.55	37.99	1.74	0.49	6.25
	Std.Dev.	13.09	0.59	0.50	9.79	0.47	0.50	1.11
	Min	13.33	1.00	0.00	23.00	2.00	0.00	3.50
	Max	53.33	2.00	1.00	59.00	2.00	1.00	7.00
情境三 N=81	Mean	40.57	1.37	0.43	38.25	1.74	0.42	6.40
	Std.Dev.	15.27	0.62	0.50	10.83	0.47	0.50	0.99
	Min	13.33	1.00	0.00	0.00	1.00	0.00	4.00
	Max	53.33	3.00	1.00	58.00	3.00	1.00	7.00
分布特征检验P值		0.425	0.837	0.328	0.166	0.191	0.107	0.024^**
均值检验P值		0.709	0.882	0.330	0.262	0.170	0.105	0.022^**

[1]	Kun Zhou,Xiaohui Gao,Lianshui Li. Integrated Carbon Emission Trading Price Prediction Based on EMD-XGB-ELM and FSGM from the Perspective of Dual Processing [J]. Chinese Journal of Management Science, 2024, 32(10): 325-334.
[2]	Zhiqiang Ye,Shunming Zhang,Xuechen Shen,Jiefei Zheng. Volatility Timing Effect of Private Placement for Chinese Listed Firms [J]. Chinese Journal of Management Science, 2024, 32(10): 30-40.
[3]	Feng He,Hanyu Du,Jing Hao. Is There an Internet Media Coverage Premium in the Chinese Stock Market? [J]. Chinese Journal of Management Science, 2024, 32(10): 20-29.
[4]	Xiaojun Shi,Jiakun Xing. Credit-insurance Covered Financing of the Retailer-dominated Supply Chain: A Newsvendor Model [J]. Chinese Journal of Management Science, 2024, 32(8): 194-203.
[5]	Changqing Luo,Lan Liu,Huiming Zhu,Min Zhang. An Empirical Study on the Dynamic Evolution of Crude Oil Market Pricing Power Based on Multi-scale Information Share Model [J]. Chinese Journal of Management Science, 2024, 32(6): 1-12.
[6]	Han He,Sicheng Li. Research on the Modeling of Default Risk Contagion of Guarantee Circle Based on Directed Complex Network [J]. Chinese Journal of Management Science, 2024, 32(6): 22-33.
[7]	Xinnan Lei,Lefan Lin,Binqing Xiao,Honghai Yu. Re-exploration of Small and Micro Enterprises' Default Characteristics Based on Machine Learning Models with SHAP [J]. Chinese Journal of Management Science, 2024, 32(5): 1-12.
[8]	Linli Xie,Aifan Ling. Equity Extreme Tail Risks,Leverage Ratio and Credit Spreads of the Corporate Bonds in China [J]. Chinese Journal of Management Science, 2024, 32(5): 47-60.
[9]	Lanbiao Liu,Liang Guo. Downside Risk in the Chinese A-Share Market: Based on the Perspective of Generalized Disappointment Aversion [J]. Chinese Journal of Management Science, 2024, 32(5): 61-72.
[10]	Meifeng Zou,Xindong Zhang,Weiqi Liu. Supplier Concentration and its Financialization: “Risk Effect” or “Synergic Effect” [J]. Chinese Journal of Management Science, 2024, 32(3): 116-124.
[11]	Qiang Fu,Zelong Shi. Research on Frequency of the Joint Network Connectedness of Systemic Financial Risks in China ——Based on the Locally Stationary Non-parametric Time-varying Vector HAR Model [J]. Chinese Journal of Management Science, 2024, 32(2): 1-10.
[12]	Jun Hu,Qiang Li,Jiacheng Dai,Yong Zeng. Textual Analysis-based Measurement of Fintech and tests of Enabling Effect for Commercial Banks [J]. Chinese Journal of Management Science, 2024, 32(1): 31-41.
[13]	Yikang Qi. Nonfinancial Corporate Debt and Resource Allocation Optimization——A Quantitative Analysis Based on the Dynamic General Equilibrium Model [J]. Chinese Journal of Management Science, 2024, 32(1): 65-74.
[14]	De-hua SHEN,Yue LI. Do Commodity Futures Improve the Performances of Traditional Portfolios? Evidence from the Chinese Market [J]. Chinese Journal of Management Science, 2023, 31(12): 34-45.
[15]	Yong-ji ZHANG,Tian-xiong LI,Zhi SU,Qiong HUANG. Fund Size, Investor Attention and Fund Performance Persistence [J]. Chinese Journal of Management Science, 2023, 31(12): 57-68.

The Embankment of a Thousand Miles Collapses in the Ant's Nest:An Experimental Study on the Formation Mechanism of Internal Fraud Risk in the Banking Industry

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 48

Related Articles 15

Metrics

Comments

Recommended 0