舞弊是指透過欺騙他人來獲取金錢的犯罪行為，例如：加密貨幣交易所FTX的破產事件、德國支付業者 Wirecard的詐欺事件、中國河南省村鎮銀行存款消失事件、台灣台新銀行理財專員不當挪用客戶資產等，都再再顯示全球舞弊及詐騙行為層出不窮。是故政府機關或私人企業均持續投注資源於舞弊偵防。因此，本論文的目標旨在建立異常交易與投資舞弊行為的檢測模型，並試圖從數據中找出適當的解決方案。其中，本論文共包含了兩部分的研究，第一部分是以犯罪學的理性選擇理論為基礎，從海量交易數據中識別出異常交易的舞弊因子；第二部分則是透過情緒分析的方式，從聊天訊息內容中偵測出詐騙者的舞弊行為。兩項研究最後都透過機器學習的方式來評估檢測模型顯著的效能，進而提供研究人員及舞弊偵防從業人員有例可援。

Fraud refers to the crime of obtaining money by deceiving people, such as the bankruptcy of the FTX cryptocurrency exchange, the Wirecard fraud in Germany, the disappearance of deposits in rural banks in Henan Province, China, and the misappropriation of customer assets by a financial consultant at Taiwan's Taishin Bank that illustrate the persistent issue of fraud and scams are still prevalent worldwide. As a result, government agencies and enterprises have invested significant resources in fraud prevention and detection. Therefore, this dissertation aims to establish fraud detection models and identify appropriate solutions from the data. This dissertation consists of two parts of research. The first part of the study is grounded in the rational choice theory of criminology and identifies fraudulent variables in abnormal transactions from massive transaction data. The second part detects the fraudulent behavior of scammers through emotion analysis of chat room messages. Both parts of this study evaluate the effectiveness of the detection model through machine learning, providing researchers and anti-fraud practitioners with valuable references.

ACCC. (2016). The little black book of scams https://www.accc.gov.au/publications/the-little-black-book-of-scams
ACFE. (2019). 2020 Fraud Examiners Manual (International ed.). Association of Certified Fraud Examiners, Inc.
ACFE. (2021). 2022 Fraud Examiners Manual. Association of Certified Fraud Examiners, Inc.
ACFE. (2022). 2023 Fraud Examiners Manual. Association of Certified Fraud Examiners, Inc. https://fraudexaminersmanual.com/
ACFE, & SAS. (2022). Anti-Fraud Technology Benchmarking Report. SAS. https://www.sas.com/en/whitepapers/acfe-anti-fraud-technology-110652.html
Akers, R. L. (1990). Rational choice, deterrence, and social learning theory in criminology: The path not taken. The Journal of Criminal Law and Criminology, 81, 653.
Alghamdi, B., & Alharby, F. (2019). An intelligent model for online recruitment fraud detection. Journal of Information Security, 10(3), 155-176.
Arya, M., & Sastry G, H. (2020). DEAL – ‘Deep Ensemble ALgorithm’ Framework for Credit Card Fraud Detection in Real-Time Data Stream with Google TensorFlow. Smart Science, 8(2), 71-83. https://doi.org/10.1080/23080477.2020.1783491
Askari, S. M. S., & Hussain, M. A. (2020). IFDTC4.5: Intuitionistic fuzzy logic based decision tree for E-transactional fraud detection. Journal of Information Security and Applications, 52, 102469. https://doi.org/https://doi.org/10.1016/j.jisa.2020.102469
Bartoletti, M., Carta, S., Cimoli, T., & Saia, R. (2020). Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact. Future Generation Computer Systems, 102, 259-277. https://doi.org/10.1016/j.future.2019.08.014
Beals, M., DeLiema, M., & Deevy, M. (2015). Framework for a taxonomy of fraud. S. C. o. Longevity. https://longevity.stanford.edu/framework-for-a-taxonomy-of-fraud/
Birzhandi, P., Kim, K. T., Lee, B., & Youn, H. Y. (2019). Reduction of Training Data Using Parallel Hyperplane for Support Vector Machine. Applied Artificial Intelligence, 33(6), 497-516. https://doi.org/10.1080/08839514.2019.1583449
Blagus, R., & Lusa, L. (2013). SMOTE for high-dimensional class-imbalanced data. BMC Bioinformatics, 14(1), 106. https://doi.org/10.1186/1471-2105-14-106
Brantingham, P. J., & Brantingham, P. L. (1984). Patterns in crime. Macmillan New York.
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
Buchanan, T., & Whitty, M. T. (2014). The online dating romance scam: causes and consequences of victimhood. Psychology, Crime & Law, 20(3), 261-283. https://doi.org/10.1080/1068316X.2013.772180
Bullée, J. W. H., Montoya, L., Pieters, W., Junger, M., & Hartel, P. (2018). On the anatomy of social engineering attacks—A literature‐based dissection of successful attacks. Journal of investigative psychology and offender profiling, 15(1), 20-45. https://doi.org/10.1002/jip.1482
Butavicius, M., Taib, R., & Han, S. J. (2022). Why people keep falling for phishing scams: The effects of time pressure and deception cues on the detection of phishing emails. Computers & Security, 123, 102937. https://doi.org/https://doi.org/10.1016/j.cose.2022.102937
Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2021). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 557, 317-331. https://doi.org/https://doi.org/10.1016/j.ins.2019.05.042
Chan, F., & Gibbs, C. (2019). Integrated Theories of White-Collar and Corporate Crime. In The Handbook of White‐Collar Crime (pp. 191-207). https://doi.org/https://doi.org/10.1002/9781118775004.ch13
Chang, B., Kuo, C., Wu, C.-H., & Tzeng, G.-H. (2015). Using Fuzzy Analytic Network Process to assess the risks in enterprise resource planning system implementation. Applied Soft Computing, 28, 196-207. https://doi.org/10.1016/j.asoc.2014.11.025
Charm, T., Perrey, J., Poh, F., & Ruwadi, B. (2020). 2020 Holiday Season: Navigating shopper behaviors in the pandemic. P. b. Mckinsey.
Chaudhry, J. A., Chaudhry, S. A., & Rittenhouse, R. G. (2016). Phishing attacks and defenses. International Journal of Security Its Applications, 10(1), 247-256. https://doi.org/10.14257/ijsia.2016.10.1.23
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357. https://doi.org/10.1613/jair.953
Chen, C., & Li, X. (2020). The effect of online shopping festival promotion strategies on consumer participation intention. Industrial Management & Data Systems.
Chiew, K. L., Yong, K. S. C., & Tan, C. L. (2018). A survey of phishing attacks: Their types, vectors and technical approaches. Expert Systems with Applications, 106, 1-20. https://doi.org/https://doi.org/10.1016/j.eswa.2018.03.050
Chiluwa, I. M. (2019). “Truth,” Lies, and Deception in Ponzi and Pyramid Schemes. In Handbook of Research on Deception, Fake News, and Misinformation Online (pp. 439-458). IGI Global.
Choi, J., Kruis, N. E., & Choo, K.-S. (2021). Explaining Fear of Identity Theft Victimization Using a Routine Activity Approach. Journal of Contemporary Criminal Justice, 37(3), 406-426. https://doi.org/10.1177/10439862211001627
Cialdini, R. B., & James, L. (2009). Influence: Science and practice (Vol. 4). Pearson education Boston.
[Record #682 is using a reference type undefined in this output style.]
Clarke, R. V. (1983). Situational Crime Prevention: Its Theoretical Basis and Practical Scope. Crime and Justice, 4, 225-256. https://doi.org/10.1086/449090
Clarke, R. V., & Cornish, D. B. (1985). Modeling Offenders' Decisions: A Framework for Research and Policy. Crime and Justice, 6, 147-185. https://doi.org/10.1086/449106
Clarke, R. V., & Harris, P. M. (1992). A rational choice perspective on the targets of automobile theft. Criminal Behaviour and Mental Health, 2(1), 25-42. https://doi.org/https://doi.org/10.1002/cbm.1992.2.1.25
Cohen, L. E., & Felson, M. (1979). Social Change and Crime Rate Trends: A Routine Activity Approach. American sociological review, 44(4), 588-608. https://doi.org/10.2307/2094589
Cornish, D. B., & Clarke, R. V. (1987). Understanding crime displacement: an application of rational choice theory. Criminology, 25(4), 933-948. https://doi.org/https://doi.org/10.1111/j.1745-9125.1987.tb00826.x
Cornish, D. B., & Clarke, R. V. (1989). Crime Specialisation, Crime Displacement and Rational Choice Theory. In H. Wegener, F. Lösel, & J. Haisch (Eds.), Criminal Behavior and the Justice System: Psychological Perspectives (pp. 103-117). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-86017-1_7
Correa Bahnsen, A., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 51, 134-142. https://doi.org/10.1016/j.eswa.2015.12.030
Cressey, D. R. (1973). Other People's Money: A Study in the Social Psychology of Embezzlement. Patterson Smith.
Cui, B., & He, S. (2016). Anomaly detection model based on hadoop platform and weka interface. 2016 10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS),
Deliema, M., Shadel, D., & Pak, K. (2020). Profiling victims of investment fraud: Mindsets and risky behaviors. Journal of Consumer Research, 46(5), 904-914. https://doi.org/10.1093/jcr/ucz020
Deloitte. (2018). 2018 Taiwan Corporate Fraud Risk Management Survey and Future Outlook. D. A. P. S. Limited. https://www2.deloitte.com/tw/tc/pages/risk/articles/2018-fraud-report-press.html
Dilla, W. N., & Raschke, R. L. (2015). Data visualization for fraud detection: Practice implications and a call for future research. International Journal of Accounting Information Systems, 16, 1-22. https://doi.org/https://doi.org/10.1016/j.accinf.2015.01.001
Ding, N., & Zhai, Y. (2021). Crime prevention of bus pickpocketing in Beijing, China: does air quality affect crime? Security Journal, 34(2), 262-277. https://doi.org/10.1057/s41284-019-00226-1
Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87.
Dopson, L. R., & Hayes, D. K. (2015). Food and beverage cost control. John Wiley & Sons.
Ekman, P., & Friesen, W. V. (1971). Constants across cultures in the face and emotion. Journal of personality and social psychology, 17(2), 124. https://doi.org/https://doi.org/10.1037/h0030377
Esparza, J. M. (2019). Understanding the credential theft lifecycle. Computer Fraud & Security, 2019(2), 6-9. https://doi.org/https://doi.org/10.1016/S1361-3723(19)30018-1
Fang, Y., Xie, M., & Huang, C. (2021). PBDT: Python Backdoor Detection Model Based on Combined Features. Security and Communication Networks, 2021, 9923234. https://doi.org/10.1155/2021/9923234
Felson, M., & Clarke, R. V. (1998a). Opportunity makes the thief. Police research series, paper, 98(1-36), 10.
Felson, M., & Clarke, R. V. (1998b). Opportunity Makes the Thief. Police Research, 98.
Feng, Y., Akiyama, H., Lu, L., & Sakurai, K. (2018). Feature selection for machine learning-based early detection of distributed cyber attacks 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech),
Fernández, A., Garcia, S., Herrera, F., & Chawla, N. V. (2018). SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. Journal of artificial intelligence research, 61, 863-905. https://doi.org/10.1613/jair.1.11192
Fischer, P., Lea, S. E. G., & Evans, K. M. (2013). Why do individuals respond to fraudulent scam communications and lose money? The psychological determinants of scam compliance. Journal of Applied Social Psychology, 43, 2060-2072. https://doi.org/10.1111/jasp.12158
Fortinet. (2022). Cyber Threat Predictions for 2023. I. Fortinet. https://www.fortinet.com/content/dam/maindam/PUBLIC/02_MARKETING/02_Collateral/WhitePaper/WP-threat-prediction-2023.pdf
Fraud. In. Dictionary by Merriam-Webster. Retrieved March 1, 2023, from https://www.merriam-webster.com/dictionary/fraud
Garzia, F., Borghini, F., Makshanova, E., Lombardi, M., & Ramalingam, S. (2022). Emotional analysis of safeness and risk perception of cybersecurity attacks during the COVID-19 pandemic. 2022 IEEE International Carnahan Conference on Security Technology (ICCST),
Gee, S. (2014). Fraud and fraud detection: a data analytics approach. John Wiley & Sons.
Guedes, I., Martins, M., & Cardoso, C. S. (2022). Exploring the determinants of victimization and fear of online identity theft: an empirical study. Security Journal. https://doi.org/10.1057/s41284-022-00350-5
Guidotti, R., Coscia, M., Pedreschi, D., & Pennacchioli, D. (2015). Behavioral entropy and profitability in retail. 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA),
Guri, M., Puzis, R., Choo, K.-K. R., Rubinshtein, S., Kedma, G., & Elovici, Y. (2019). Using malware for the greater good: Mitigating data leakage. Journal of Network and Computer Applications, 145, 102405. https://doi.org/https://doi.org/10.1016/j.jnca.2019.07.006
Hadnagy, C. (2010). Social engineering: The art of human hacking. John Wiley & Sons.
Hajek, P., & Henriques, R. (2017). Mining corporate annual reports for intelligent detection of financial statement fraud–A comparative study of machine learning methods. Knowledge-Based Systems, 128, 139-152.
Hanus, B., Wu, Y. A., & Parrish, J. (2022). Phish Me, Phish Me Not. Journal of Computer Information Systems, 62(3), 516-526. https://doi.org/10.1080/08874417.2020.1858730
Hatfield, J. M. (2018). Social engineering in cybersecurity: The evolution of a concept. Computers & Security, 73, 102-113.
Henriques, D. B. (2011). The wizard of lies: Bernie Madoff and the death of trust. Macmillan.
Hogarth, R. M., & Makridakis, S. (1981). Forecasting and planning: An evaluation. Management science, 27(2), 115-138. https://doi.org/10.1287/mnsc.27.2.115
Holtfreter, K. (2005). Is occupational fraud “typical” white-collar crime? A comparison of individual and organizational characteristics. Journal of Criminal Justice, 33(4), 353-365. https://doi.org/10.1016/j.jcrimjus.2005.04.005
Hooda, N., Bawa, S., & Rana, P. S. (2018). Fraudulent firm classification: a case study of an external audit. Applied Artificial Intelligence, 32(1), 48-64.
Hooda, N., Bawa, S., & Rana, P. S. (2020). Optimizing fraudulent firm prediction using ensemble machine learning: a case study of an external audit. Applied Artificial Intelligence, 34(1), 20-30.
Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.
Hsia, T.-L., Wu, J.-H., Xu, X., Li, Q., Peng, L., & Robinson, S. (2020). Omnichannel retailing: The role of situational involvement in facilitating consumer experiences. Information & Management, 57(8), 103390.
Huang, F. L. (2019). MANOVA: A Procedure Whose Time Has Passed? Gifted Child Quarterly, 64(1), 56-60. https://doi.org/10.1177/0016986219887200
Huang, M.-H., & Rust, R. T. (2022). A Framework for Collaborative Artificial Intelligence in Marketing. Journal of Retailing, 98(2), 209-223. https://doi.org/https://doi.org/10.1016/j.jretai.2021.03.001
Huang, S. Y., Lin, C.-C., Chiu, A.-A., & Yen, D. C. (2017). Fraud detection using fraud triangle risk factors. Information Systems Frontiers, 19(6), 1343-1356.
Hudlicka, E. (2011). Guidelines for Designing Computational Models of Emotions. International Journal of Synthetic Emotions, 2(1), 26-79. https://doi.org/10.4018/jse.2011010103
IAASB. (2013). International Standard on Auditing 240: The Auditor’s Responsibilities Relating to Fraud in an Audit of Financial Statements.
Janjua, F., Masood, A., Abbas, H., Rashid, I., & Khan, M. M. Z. M. (2021). Textual analysis of traitor-based dataset through semi supervised machine learning. Future Generation Computer Systems, 125, 652-660. https://doi.org/https://doi.org/10.1016/j.future.2021.06.036
Jones, H. S., Towse, J. N., Race, N., & Harrison, T. (2019). Email fraud: The search for psychological predictors of susceptibility. PLOS ONE, 14(1), e0209684. https://doi.org/10.1371/journal.pone.0209684
Jones, K. S., Armstrong, M. E., Tornblad, M. K., & Siami Namin, A. (2021). How social engineers use persuasion principles during vishing attacks. Information & Computer Security, 29(2), 314-331. https://doi.org/10.1108/ICS-07-2020-0113
Junger, M., Wang, V., & Schlömer, M. (2020). Fraud against businesses both online and offline: crime scripts, business characteristics, efforts, and benefits. Crime Science, 9(1), 13. https://doi.org/10.1186/s40163-020-00119-4
Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234-245. https://doi.org/10.1016/j.eswa.2018.01.037
Karmy, J. P., & Maldonado, S. (2019). Hierarchical time series forecasting via Support Vector Regression in the European Travel Retail Industry. Expert Systems with Applications, 137, 59-73. https://doi.org/https://doi.org/10.1016/j.eswa.2019.06.060
Kashyap, A. (2019). How digital transformation increases consumer and retail fraud risks. https://www.ey.com/en_ro/how-digital-transformation-increases-consumer-and-retail-fraud-risks
Krombholz, K., Hobel, H., Huber, M., & Weippl, E. (2015). Advanced social engineering attacks. Journal of Information Security applications, 22, 113-122. https://doi.org/10.1016/j.jisa.2014.09.005
Kuo, C., & Tsang, S.-S. (2022). Detection of price manipulation fraud through rational choice theory: evidence for the retail industry in Taiwan. Security Journal. https://doi.org/10.1057/s41284-022-00360-3
Lacey, D., Goode, S., Pawada, J., & Gibson, D. (2020). The application of scam compliance models to investment fraud offending. Journal of Criminological Research, Policy and Practice, 6(1), 65-81. https://doi.org/10.1108/JCRPP-12-2019-0073
Langenderfer, J., & Shimp, T. A. (2001). Consumer vulnerability to scams, swindles, and fraud: A new theory of visceral influences on persuasion [https://doi.org/10.1002/mar.1029]. Psychology & Marketing, 18(7), 763-783. https://doi.org/https://doi.org/10.1002/mar.1029
Lavanya, P., Kouser, K., & Suresha, M. (2021). Effective feature representation using symbolic approach for classification and clustering of big data. Expert Systems with Applications, 173, 114658.
Lee, C. S. (2021). How Online Fraud Victims are Targeted in China: A Crime Script Analysis of Baidu Tieba C2C Fraud. Crime & Delinquency, 68(13-14), 2529-2553. https://doi.org/10.1177/00111287211029862
Levi, M. (2008). Organized fraud and organizing frauds: Unpacking research on networks and organization. Criminology & Criminal Justice, 8(4), 389-419.
Levy, D., Chen, H., Müller, G., Dutta, S., & Bergen, M. (2010). Holiday price rigidity and cost of price adjustment. Economica, 77(305), 172-198.
Li, C., Ding, N., Zhai, Y., & Dong, H. (2021). Comparative study on credit card fraud detection based on different support vector machines. Intelligent Data Analysis, 25, 105-119. https://doi.org/10.3233/IDA-195011
Li, D., & Santos Jr, E. (2020). Discriminating deception from truth and misinformation: an intent-level approach. Journal of Experimental & Theoretical Artificial Intelligence, 32(3), 373-407. https://doi.org/10.1080/0952813X.2019.1652354
Li, T., Wang, X., & Ni, Y. (2020). Aligning social concerns with information system security: A fundamental ontology for social engineering. Information Systems, 101699.
Liu, S. M., & Chen, J.-H. (2015). A multi-label classification based approach for sentiment classification. Expert Systems with Applications, 42(3), 1083-1093. https://doi.org/https://doi.org/10.1016/j.eswa.2014.08.036
Lokanan, M. E. (2015). Challenges to the fraud triangle: Questions on its usefulness. Accounting Forum, 39(3), 201-224. https://doi.org/https://doi.org/10.1016/j.accfor.2015.05.002
Lucas, Y., Portier, P.-E., Laporte, L., He-Guelton, L., Caelen, O., Granitzer, M., & Calabretto, S. (2020). Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs. Future Generation Computer Systems, 102, 393-402. https://doi.org/https://doi.org/10.1016/j.future.2019.08.029
Masmoudi, K., Abid, L., & Masmoudi, A. (2019). Credit risk modeling using Bayesian network with a latent variable. Expert Systems with Applications, 127, 157-166. https://doi.org/https://doi.org/10.1016/j.eswa.2019.03.014
Mavlanova, T., Benbunan-Fich, R., & Lang, G. (2016). The role of external and internal signals in E-commerce. Decision Support Systems, 87, 59-68.
Mehbodniya, A., Alam, I., Pande, S., Neware, R., Rane, K. P., Shabaz, M., & Madhavan, M. V. (2021). Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques. Security and Communication Networks, 2021, 9293877. https://doi.org/10.1155/2021/9293877
Mendl, M. (1999). Performing under pressure: stress and cognitive function. Applied Animal Behaviour Science, 65(3), 221-244. https://doi.org/https://doi.org/10.1016/S0168-1591(99)00088-X
Mercer, G. (2016, 01/14). 4 Ways to Be a Successful Amazon Seller. https://www.linkedin.com/pulse/4-ways-successful-amazon-seller-greg-mercer/
Meyer, S. (2012). Reducing harm from explosive attacks against railways. Security Journal, 25(4), 309-325. https://doi.org/10.1057/sj.2011.23
Micro, T. (2021, 2021/04/01). Top 3 Hot Sacms in 2021 investegated by Trend Micro, National Police Agency, and Consumers' Foundation https://www.trendmicro.com/zh_tw/about/newsroom/press-releases/2021/2021-04-01.html
Mohammad, R. M., Thabtah, F., & McCluskey, L. (2015). Tutorial and critical analysis of phishing websites methods. Computer Science Review, 17, 1-24. https://doi.org/https://doi.org/10.1016/j.cosrev.2015.04.001
MOI. (2023). 2022 Fraud techniques in Taiwan. https://www.moi.gov.tw/News_Content.aspx?n=4&s=275532
Mqadi, N. M., Naicker, N., & Adeliyi, T. (2021). Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss. Mathematical Problems in Engineering, 2021, 7194728. https://doi.org/10.1155/2021/7194728
Mueller, E. A., Wood, S. A., Hanoch, Y., Huang, Y., & Reed, C. L. (2020). Older and wiser: age differences in susceptibility to investment fraud: the protective role of emotional intelligence. Journal of Elder Abuse & Neglect, 32(2), 152-172. https://doi.org/10.1080/08946566.2020.1736704
NPA. (2021). Ministry of the Interior release the top scams in 2020 https://www.moi.gov.tw/News_Content.aspx?n=4&s=212607
NRF. (2020). NRF expects holiday sales will grow between 3.6 and 5.2 percent. National Retail Fedration. Retrieved 01/12 from https://nrf.com/media-center/press-releases/nrf-expects-holiday-sales-will-grow-between-36-and-52-percent
Oh, H., & Kwon, K. N. (2009). An exploratory study of sales promotions for multichannel holiday shopping. International Journal of Retail Distribution Management.
OIG. (2018). Alleged Split Purchases at the VA St. Louis Health Care System. O. o. I. G. Department of Veterans Affairs. https://www.va.gov/oig/pubs/VAOIG-16-02863-199.pdf
Otu, S. E., & Okon, O. N. (2019). Participation in Fraud/Cheat in the Buying and Selling of Meats Without Legal Metrology: A Theoretical and Empirical Investigations. Deviant Behavior, 40(2), 205-224. https://doi.org/10.1080/01639625.2017.1420458
Palmer, W. E., & Richardson, C. (2009). Organized Retail Crime: Assessing the Risk and Developing Effective Strategies (An ASIS Foundation Research Council CRISP Report, Issue. I. ASIS Foundation.
Park, S., Bae, B., & Cheong, Y. (2020, 19-22 Feb. 2020). Emotion Recognition from Text Stories Using an Emotion Embedding Model. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp),
Peng, L., & Lin, R. (2018, 2-7 July 2018). Fraud Phone Calls Analysis Based on Label Propagation Community Detection Algorithm. 2018 IEEE World Congress on Services (SERVICES),
Peng, S., Cao, L., Zhou, Y., Ouyang, Z., Yang, A., Li, X., Jia, W., & Yu, S. (2022). A survey on deep learning for textual emotion analysis in social networks. Digital Communications and Networks, 8(5), 745-762. https://doi.org/https://doi.org/10.1016/j.dcan.2021.10.003
Piza, E. L., Caplan, J. M., & Kennedy, L. W. (2017). CCTV as a tool for early police intervention: Preliminary lessons from nine case studies. Security Journal, 30(1), 247-265. https://doi.org/10.1057/sj.2014.17
Plutchik, R. (1958). Outlines of a new theory of emotion. Trans N Y Acad Sci, 20(5), 394-403. https://doi.org/10.1111/j.2164-0947.1958.tb00600.x
Plutchik, R. (2001). The Nature of Emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. American Scientist, 89(4), 344-350. http://www.jstor.org/stable/27857503
Poria, S., Majumder, N., Mihalcea, R., & Hovy, E. (2019). Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances. IEEE Access, 7, 100943-100953. https://doi.org/10.1109/ACCESS.2019.2929050
Qamar, S., Mujtaba, H., Majeed, H., & Beg, M. O. (2021). Relationship Identification Between Conversational Agents Using Emotion Analysis. Cognitive Computation, 13(3), 673-687. https://doi.org/10.1007/s12559-020-09806-5
Rajesh, P., & Karthikeyan, M. (2017). A comparative study of data mining algorithms for decision tree approaches using weka tool. Advances in Natural Applied Sciences, 11(9), 230-243.
Reurink, A. (2018). Financial fraud: a literature review. Journal of Economic Surveys, 32(5), 1292-1325. https://doi.org/10.1111/joes.12294
Robinson, W. N., & Aria, A. (2018). Sequential fraud detection for prepaid cards using hidden Markov model divergence. Expert Systems with Applications, 91, 235-251. https://doi.org/10.1016/j.eswa.2017.08.043
Romney, M. B., Albrecht, W. S., & Cherrington, D. J. (1980). Auditors and the detection of fraud. Journal of Accountancy, 149(5), 63-69.
Salahdine, F., & Kaabouch, N. (2019). Social Engineering Attacks: A Survey. Future Internet, 11(4).
Schmalleger, F. (1999). Criminology today: an integrative introduction. Prentice Hall Second edition. Upper Saddle River, NJ.
Schmalleger, F. (2021). Criminology Today: An Integrative Introduction (10th ed.). Pearson.
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia & Analgesia, 126(5). https://journals.lww.com/anesthesia-analgesia/Fulltext/2018/05000/Correlation_Coefficients__Appropriate_Use_and.50.aspx
Schuchter, A., & Levi, M. (2016). The fraud triangle revisited. Security Journal, 29(2), 107-121.
Shadel, D., & Pak, K. (2017). AARP Investment Fraud Vulnerability Study. AARP. https://www.aarp.org/research/topics/economics/info-2017/investment-fraud-survey.html
Shalke, C. J., & Achary, R. (2022, 28-30 April 2022). Social Engineering Attack and Scam Detection using Advanced Natural Langugae Processing Algorithm. 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI),
Shrivastava, G., & Kumar, P. (2021). Android application behavioural analysis for data leakage [https://doi.org/10.1111/exsy.12468]. Expert Systems, 38(1), e12468. https://doi.org/https://doi.org/10.1111/exsy.12468
Siering, M., Koch, J.-A., & Deokar, A. V. (2016). Detecting fraudulent behavior on crowdfunding platforms: The role of linguistic and content-based cues in static and dynamic contexts. Journal of Management Information Systems, 33(2), 421-455. https://doi.org/10.1080/0952813X.2019.1652354
Singh, K., & Best, P. (2019). Anti-money laundering: using data visualization to identify suspicious activity. International Journal of Accounting Information Systems, 34, 100418. https://doi.org/10.1016/j.accinf.2019.06.001
Singh, M., & Valtorta, M. (1995). Construction of Bayesian network structures from data: A brief survey and an efficient algorithm. International Journal of Approximate Reasoning, 12(2), 111-131. https://doi.org/https://doi.org/10.1016/0888-613X(94)00016-V
Springer, M. (2020). The Politics of Ponzi Schemes: History, Theory and Policy. Routledge.
Stamler, R. T., Marschdorf, H. J., & Possamai, M. (2014). Fraud prevention and detection: warning signs and the red flag system. CRC Press.
Steinmetz, K. F. (2020). The Identification of a Model Victim for Social Engineering: A Qualitative Analysis. Victims & Offenders, 1-25.
Tang, T., & Hu, P. (2019). Quantitative standard of promotion strategy and analysis on the influence of consumer purchase behavior. Cluster Computing, 22(2), 4949-4955.
Thornburgh, T. (2004). Social engineering: the "Dark Art" Proceedings of the 1st annual conference on Information security curriculum development, Kennesaw, Georgia. https://doi.org/10.1145/1059524.1059554
Torres, C. F., Baden, M., & State, R. (2020). Towards Usable Protection Against Honeypots. 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC),
Trahan, A., Marquart, J. W., & Mullings, J. (2005). Fraud and the American Dream: Toward an Understanding of Fraud Victimization. Deviant Behavior, 26(6), 601-620. https://doi.org/10.1080/01639620500218294
Trivedi, N. K., Simaiya, S., Lilhore, U. K., & Sharma, S. K. (2020). An efficient credit card fraud detection model based on machine learning methods. International Journal of Advanced Science and Technology, 29(5), 3414-3424.
Tsai, T.-M., Chou, W.-Y., & Chen, Y.-S. (2009). A Study on the Characteristics of Fraud Victims. R. O. C. T. Ministry of the Interior. https://www.grb.gov.tw/search/planDetail?id=1895111&docId=313819
Tsang, S.-S., Kuo, C., Hu, T.-K., & Wang, W.-C. (2022). Exploring impacts of AR on group package tours: Destination image, perceived certainty, and experiential value. Journal of Vacation Marketing, 13567667221078244. https://doi.org/10.1177/13567667221078244
Tseng, S. C. (2018). An analysis of first sale rule in the Trademarks Act. Taiwan Bar Journal, 12, 24-44.
Tsoumakas, G. (2019). A survey of machine learning techniques for food sales prediction. Artificial Intelligence Review, 52(1), 441-447.
Ullah, I., Ahmad, W., & Ali, A. (2022). Determinants of investment decision in a Ponzi scheme: Investors’ perspective on the Modaraba scam. Journal of Financial Crime, 29(4), 1172-1190. https://doi.org/10.1108/JFC-02-2020-0027
Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2015). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75, 38-48.
Wang, L., Cheng, H., Zheng, Z., Yang, A., & Zhu, X. (2021). Ponzi scheme detection via oversampling-based Long Short-Term Memory for smart contracts. Knowledge-Based Systems, 228, 107312. https://doi.org/https://doi.org/10.1016/j.knosys.2021.107312
Wei, Z., Liu, W., Zhu, G., Zhang, S., & Hsieh, M.-Y. (2022). Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments. Connection Science, 34(1), 409-428. https://doi.org/10.1080/09540091.2021.2006146
Wen, X., Xu, L., Wang, J., Gao, Y., Shi, J., Zhao, K., Tao, F., & Qian, X. (2022). Mental States: A Key Point in Scam Compliance and Warning Compliance in Real Life. International Journal of Environmental Research and Public Health, 19(14).
Whitty, M. T. (2013). The Scammers Persuasive Techniques Model: Development of a Stage Model to Explain the Online Dating Romance Scam. The British Journal of Criminology, 53(4), 665-684. https://doi.org/10.1093/bjc/azt009
Whitty, M. T. (2015). Anatomy of the online dating romance scam. Security Journal, 28(4), 443-455. https://doi.org/10.1057/sj.2012.57
Whitty, M. T. (2017). Do You Love Me? Psychological Characteristics of Romance Scam Victims. Cyberpsychology, Behavior, and Social Networking, 21(2), 105-109. https://doi.org/10.1089/cyber.2016.0729
Whitty, M. T. (2019a). Predicting susceptibility to cyber-fraud victimhood. Journal of Financial Crime, 26(1), 277-292. https://doi.org/10.1108/JFC-10-2017-0095
Whitty, M. T. (2019b). Who can spot an online romance scam? Journal of Financial Crime, 26(2), 623-633. https://doi.org/10.1108/JFC-06-2018-0053
Whitty, M. T. (2020a). Is there a scam for everyone? Psychologically profiling cyberscam victims. European Journal on Criminal Policy, 26(3), 399-409. https://doi.org/10.1007/s10610-020-09458-z
Whitty, M. T. (2020b). Is There a Scam for Everyone? Psychologically Profiling Cyberscam Victims. European Journal on Criminal Policy and Research, 26(3), 399-409. https://doi.org/10.1007/s10610-020-09458-z
Wilcox, P. (2015). Routine Activities, Criminal Opportunities, Crime and Crime Prevention. In J. D. Wright (Ed.), International Encyclopedia of the Social & Behavioral Sciences (Second Edition) (pp. 772-779). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-08-097086-8.45080-4
Williams, E. J., Hinds, J., & Joinson, A. N. (2018). Exploring susceptibility to phishing in the workplace. International Journal of Human-Computer Studies, 120, 1-13. https://doi.org/https://doi.org/10.1016/j.ijhcs.2018.06.004
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. (2017). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers. https://doi.org/10.1016/C2015-0-02071-8
Xu, D., Tian, Z., Lai, R., Kong, X., Tan, Z., & Shi, W. (2020). Deep learning based emotion analysis of microblog texts. Information Fusion, 64, 1-11. https://doi.org/https://doi.org/10.1016/j.inffus.2020.06.002
Xu, L., Lin, H., Pan, Y., Ren, H., & Chen, J. (2008). Constructing the Affective Lexicon Ontology. Journal of the China Society for Scientific and Technical Information(2), 180-185.
Yan, Q. (2022). Real-Time Analysis of Youth Emotion Based on Python Language and Smart Sensor Network. Mobile Information Systems, 2022, 8635787. https://doi.org/10.1155/2022/8635787
Yee, O. S., Sagadevan, S., & Malim, N. H. A. H. (2018). Credit card fraud detection using machine learning as data mining technique. Journal of Telecommunication, Electronic Computer Engineering, 10(1-4), 23-27.
Zhang, S., Wei, Z., Wang, Y., & Liao, T. (2018). Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary. Future Generation Computer Systems, 81, 395-403. https://doi.org/https://doi.org/10.1016/j.future.2017.09.048
Zhang, X., Han, Y., Xu, W., & Wang, Q. (2021). HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture. Information Sciences, 557, 302-316. https://doi.org/10.1016/j.ins.2019.05.023
Zhang, Z., Zhou, X., Zhang, X., Wang, L., & Wang, P. (2018). A model based on convolutional neural network for online transaction fraud detection. Security Communication Networks, 2018. https://doi.org/10.1155/2018/5680264
Zhao, Q., Chen, K., Li, T., Yang, Y., & Wang, X. (2018). Detecting telecommunication fraud by understanding the contents of a call. Cybersecurity, 1(1), 8. https://doi.org/10.1186/s42400-018-0008-5
Zheng, L., Liu, G., Yan, C., & Jiang, C. (2018). Transaction fraud detection based on total order relation and behavior diversity. IEEE Transactions on Computational Social Systems, 5(3), 796-806.
Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National science review, 5(1), 44-53. https://doi.org/10.1093/nsr/nwx106