Forensic-Auditing Capabilities and Self-Reported Recognition of Suspicious Money-Laundering Indicators Among Accounting Professionals in Peru: A Structural and Explainable Machine-Learning Study
This study examines how accounting professionals’ self-reported forensic-auditing capabilities relate to their attitudinal readiness to recognize suspicious anti-money-laundering (AML) indicators, using a transparent hybrid design that combines classical psychometrics, structural equation modeling (...
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| Lenguaje: | inglés |
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2026
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| author | Jessica Karina Saavedra-Vasconez Alexander Fernando Haro Sarango Eymmy Jimena Grados Lazaro Estrella Divina Lopez Pantoja Monica Jhanyra Gamarra Pacaya Silvia Cachay-Salcedo Thelma Madian Lazo Pilco |
| author_browse | Alexander Fernando Haro Sarango Estrella Divina Lopez Pantoja Eymmy Jimena Grados Lazaro Jessica Karina Saavedra-Vasconez Monica Jhanyra Gamarra Pacaya Silvia Cachay-Salcedo Thelma Madian Lazo Pilco |
| author_facet | Jessica Karina Saavedra-Vasconez Alexander Fernando Haro Sarango Eymmy Jimena Grados Lazaro Estrella Divina Lopez Pantoja Monica Jhanyra Gamarra Pacaya Silvia Cachay-Salcedo Thelma Madian Lazo Pilco |
| author_sort | Jessica Karina Saavedra-Vasconez |
| collection | Producción científica |
| description | This study examines how accounting professionals’ self-reported forensic-auditing capabilities relate to their attitudinal readiness to recognize suspicious anti-money-laundering (AML) indicators, using a transparent hybrid design that combines classical psychometrics, structural equation modeling (SEM), and leakage-controlled explainable machine learning (ML). A cross-sectional survey collected 700 valid responses from public accountants in Lima, Peru, with a 30-item, five-point Likert instrument (19 forensic-auditing items in three dimensions and 11 AML-recognition items). Reliability was high (Cronbach’s α = 0.935; McDonald’s ω = 0.935), but average variance extracted was below 0.50 in every block (0.329–0.449), and Fornell–Larcker testing showed that skills-and-knowledge and AML recognition were not discriminantly distinct (r = 0.673 > √AVE = 0.651/0.649). Responses showed a pronounced ceiling (51% of answers were the maximum), and 86 respondents (12.3%) answered all 30 items identically; removing them lowered the forensic-auditing–AML association from r = 0.731 to 0.630 and explained variance from 53.8% to 40.3%. Skills-and-knowledge remained the strongest predictor in SEM and HC3-robust regression (β = 0.478 and 0.425). Under a leakage-free protocol, ensemble models reached ROC-AUC ≈ 0.86 on held-out data, but threshold tuning did not improve F1 test, and item-level attributions were unstable (Spearman ρ = 0.28). Forensic-auditing capabilities are positively associated with declared AML-recognition readiness, driven by applied skills and knowledge; the evidence is attitudinal and correlational, and should not be read as real detection capability. Because professional experience, seniority, sector, and prior AML training were not measured, the reported associations may be partly confounded by unobserved professional background, and the dominance of skills-and-knowledge is therefore advanced as tentative, pending resolution of the skills-and-knowledge/AML-recognition discriminant-validity overlap. A second, procedural contribution is that the study reports the data-quality screening, the failed validity tests, and the explanation-stability diagnostics that survey-based forensic-accounting research rarely makes visible. |
| format | Article |
| id | openalex:W7213348033 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | openalex:W7213348033Forensic-Auditing Capabilities and Self-Reported Recognition of Suspicious Money-Laundering Indicators Among Accounting Professionals in Peru: A Structural and Explainable Machine-Learning StudyJessica Karina Saavedra-VasconezAlexander Fernando Haro SarangoEymmy Jimena Grados LazaroEstrella Divina Lopez PantojaMonica Jhanyra Gamarra PacayaSilvia Cachay-SalcedoThelma Madian Lazo Pilcoforensic auditinganti-money launderingaccountantspublic accountantsknowledge and skillsexplainable machine learningstructural equation modelingpsychometric validityceiling effectdata quality screeningPeruLimaAccounting and Financial ManagementCrime, Illicit Activities, and GovernanceAuditing, Earnings Management, GovernanceThis study examines how accounting professionals’ self-reported forensic-auditing capabilities relate to their attitudinal readiness to recognize suspicious anti-money-laundering (AML) indicators, using a transparent hybrid design that combines classical psychometrics, structural equation modeling (SEM), and leakage-controlled explainable machine learning (ML). A cross-sectional survey collected 700 valid responses from public accountants in Lima, Peru, with a 30-item, five-point Likert instrument (19 forensic-auditing items in three dimensions and 11 AML-recognition items). Reliability was high (Cronbach’s α = 0.935; McDonald’s ω = 0.935), but average variance extracted was below 0.50 in every block (0.329–0.449), and Fornell–Larcker testing showed that skills-and-knowledge and AML recognition were not discriminantly distinct (r = 0.673 > √AVE = 0.651/0.649). Responses showed a pronounced ceiling (51% of answers were the maximum), and 86 respondents (12.3%) answered all 30 items identically; removing them lowered the forensic-auditing–AML association from r = 0.731 to 0.630 and explained variance from 53.8% to 40.3%. Skills-and-knowledge remained the strongest predictor in SEM and HC3-robust regression (β = 0.478 and 0.425). Under a leakage-free protocol, ensemble models reached ROC-AUC ≈ 0.86 on held-out data, but threshold tuning did not improve F1 test, and item-level attributions were unstable (Spearman ρ = 0.28). Forensic-auditing capabilities are positively associated with declared AML-recognition readiness, driven by applied skills and knowledge; the evidence is attitudinal and correlational, and should not be read as real detection capability. Because professional experience, seniority, sector, and prior AML training were not measured, the reported associations may be partly confounded by unobserved professional background, and the dominance of skills-and-knowledge is therefore advanced as tentative, pending resolution of the skills-and-knowledge/AML-recognition discriminant-validity overlap. A second, procedural contribution is that the study reports the data-quality screening, the failed validity tests, and the explanation-stability diagnostics that survey-based forensic-accounting research rarely makes visible.Articlehttps://doi.org/10.3390/jrfm19090735W7213348033enJournal of risk and financial management1911-80661911-80742026-09-16 |
| spellingShingle | forensic auditing anti-money laundering accountants public accountants knowledge and skills explainable machine learning structural equation modeling psychometric validity ceiling effect data quality screening Peru Lima Accounting and Financial Management Crime, Illicit Activities, and Governance Auditing, Earnings Management, Governance Jessica Karina Saavedra-Vasconez Alexander Fernando Haro Sarango Eymmy Jimena Grados Lazaro Estrella Divina Lopez Pantoja Monica Jhanyra Gamarra Pacaya Silvia Cachay-Salcedo Thelma Madian Lazo Pilco Forensic-Auditing Capabilities and Self-Reported Recognition of Suspicious Money-Laundering Indicators Among Accounting Professionals in Peru: A Structural and Explainable Machine-Learning Study |
| title | Forensic-Auditing Capabilities and Self-Reported Recognition of Suspicious Money-Laundering Indicators Among Accounting Professionals in Peru: A Structural and Explainable Machine-Learning Study |
| title_full | Forensic-Auditing Capabilities and Self-Reported Recognition of Suspicious Money-Laundering Indicators Among Accounting Professionals in Peru: A Structural and Explainable Machine-Learning Study |
| title_fullStr | Forensic-Auditing Capabilities and Self-Reported Recognition of Suspicious Money-Laundering Indicators Among Accounting Professionals in Peru: A Structural and Explainable Machine-Learning Study |
| title_full_unstemmed | Forensic-Auditing Capabilities and Self-Reported Recognition of Suspicious Money-Laundering Indicators Among Accounting Professionals in Peru: A Structural and Explainable Machine-Learning Study |
| title_short | Forensic-Auditing Capabilities and Self-Reported Recognition of Suspicious Money-Laundering Indicators Among Accounting Professionals in Peru: A Structural and Explainable Machine-Learning Study |
| title_sort | forensic auditing capabilities and self reported recognition of suspicious money laundering indicators among accounting professionals in peru a structural and explainable machine learning study |
| topic | forensic auditing anti-money laundering accountants public accountants knowledge and skills explainable machine learning structural equation modeling psychometric validity ceiling effect data quality screening Peru Lima Accounting and Financial Management Crime, Illicit Activities, and Governance Auditing, Earnings Management, Governance |
| topic_facet | forensic auditing anti-money laundering accountants public accountants knowledge and skills explainable machine learning structural equation modeling psychometric validity ceiling effect data quality screening Peru Lima Accounting and Financial Management Crime, Illicit Activities, and Governance Auditing, Earnings Management, Governance |
| url | https://doi.org/10.3390/jrfm19090735 |
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