Forecasting Identity Theft Victims: Analyzing Characteristics and Preventive Actions through Machine Learning Approaches

Hu, Xiaochen; Zhang, Xudong; Lovrich, Nicholas P.

doi:10.1080/15564886.2020.1806161

Cited by 13 publications

(5 citation statements)

References 43 publications

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“…Law Enforcement Agencies and fraud detection experts can focus on catching the offenders at their entry gateways, the same way as trappers would lay traps at the mouse paths. Previous research has shown a couple of machine learning algorithms that can be used to predict identity theft victimization, misuse of credit cards, misuse of other types of financial accounts and fraudulent opening of new accounts (Hu et al , 2020). At the Self-E-Filing vulnerability point, a profile of fraudulent transactions (mismatch between taxpayer’s residential address and filing address, multiple tax refunds without filing amended tax returns, etc.)…”

Section: Discussionmentioning

confidence: 99%

Reducing tax identity theft by identifying vulnerability points in the electronic tax filing process

Ngugi

Hung

2021

ICS

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Purpose Tax Identity Theft involves the illegal use of a potential taxpayer’s identity, usually the social security number, to fraudulently file a tax return and claim a refund. The victim is the real owner of the social security number who will have difficulties getting a tax refund, as the offender has already taken a refund for the year in question. This paper aims to investigate whether the increased use and adoption of electronic tax filing (i.e. E-Filing) technologies has inadvertently resulted in a corresponding growth in Tax Identity Theft. Design/methodology/approach Multiple regressions are used to analyze the data that is extracted from the Identity Theft complaint reports (maintained by the Federal Trade Commission) and the tax filing statistics (retrieved from the Internal Revenue Service). Findings The results indicate that E-Filing can indirectly but significantly increase Tax Identity Theft through the full mediation effects of individual Self-E-Filing and Direct Deposit adoption, after controlling for general Identity Theft, the number of Individual Tax Returns and Total Refunds. Originality/value The authors explore the association between the adoption of tax e-filing technologies and Tax Identity Theft. The findings suggest that the key loopholes in the Tax Identity Theft process are at the Self-E-Filing and the Direct Deposit points. Several practical recommendations for patching these loopholes are provided and discussed.

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Section: Discussionmentioning

confidence: 99%

Reducing tax identity theft by identifying vulnerability points in the electronic tax filing process

Ngugi

Hung

2021

ICS

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“…Studies of identity crime typically focus on demographic characteristics as predictors of victimization (Golladay, 2020;Golladay & Holtfreter, 2017;Hu et al, 2021). Within this body of literature, age is one of the most frequently tested predictors.…”

Section: Predictorsmentioning

confidence: 99%

Predictors of Identity Crime Victimization of Adolescent Youth in Foster Care

Gyourko,

Ridgeway

2024

Victims & Offenders

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Identity crimes pose serious risks and challenges for youth in foster care. As young people move through various out-of-home placements and schools, their personal data are often widely disseminated, difficult to secure, and thus vulnerable to theft and misuse. When they transition from foster care to adulthood, identity crime victims with compromised credit histories may be denied loans, employment, and/or housing during the already-precarious period of emerging adulthood. Even though federal law requires state child welfare agencies to conduct annual credit checks for adolescent youth in foster care, little is known about the factors contributing to risk of identity crime victimization among foster care-involved youth, either before or after their initial entry into care. To address this knowledge gap, we analyze linked child welfare and consumer credit records for a population-based cohort of 4,670 youth (age 14 to 17) in foster care in a southeastern state who received credit checks from November 2015 through December 2021.Using gradient boosted machine learning, we find that prior identity crime victimization before the youth first entered care is the strongest predictor of victimization while in foster care.Implications of these findings for policy and practice are discussed.

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“…Risk factors associated with financial fraud While a large body of literature exists that examines identity theft fraud (Anderson, 2006;Golladay and Holtfreter, 2016;Hu et al, 2021;Randa and Reyns, 2020;Reyns and Henson, 2016 for examples), fewer published empirical studies exist that examine financial fraud, its various forms, predictors of risk and consequences. Furthermore, some forms of financial Financial fraud victimization fraud, such as investment fraud, have received more attention in the literature, while others, such as relationship and trust fraud, are more rare and often only focus on older individuals (DeLiema et al, 2020;Li et al, 2022 for examples) as victims.…”

Section: Literature Reviewmentioning

confidence: 99%

Financial fraud victimization: an examination of distress and financial complications

Golladay,

Snyder

2022

JFC

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Purpose This study expands the empirical understanding of financial fraud victims and the consequences that emerge as a result of financial fraud victimization. In addition, this study aims to assess the impact of the unique role victims play in financial fraud and the impact self-identifying as a victim has on the negative consequences they experience. Design/methodology/approach Data from the Supplemental Fraud Survey to the National Crime Victimization Survey are used to assess the negative consequences of financial fraud victimization. Findings Results suggest that victims of financial fraud experience increased distress and financial complications following their victimization experience. In addition, self-reported victim status is found to significantly increase a respondent’s likelihood of reporting emotional distress and financial complications. Implications for research, theory and policy are discussed. Originality/value While empirical studies on the consequences of identity theft victimization have been increasing in recent years, financial fraud victimization remains understudied. Given the victim involvement in financial fraud, the consideration of financial fraud independent of identity theft fraud is vital.

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Forecasting Identity Theft Victims: Analyzing Characteristics and Preventive Actions through Machine Learning Approaches

Cited by 13 publications

References 43 publications

Reducing tax identity theft by identifying vulnerability points in the electronic tax filing process

Reducing tax identity theft by identifying vulnerability points in the electronic tax filing process

Predictors of Identity Crime Victimization of Adolescent Youth in Foster Care

Financial fraud victimization: an examination of distress and financial complications

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