A IA e o paradoxo do seu uso dual na fraude financeira: navegando entre a ofensiva e a defesa na era digital
DOI:
https://doi.org/10.51247/st.v8i4.666Palavras-chave:
Inteligência Artificial (IA), Paradoxo do uso dual, Fraude financeira, Deteção de fraude, IA defensiva, IA ofensivaResumo
Este estudo aborda o paradoxo do uso dual da inteligência artificial (IA) na fraude financeira digital, mostrando como as mesmas tecnologias podem tanto fortalecer os sistemas defensivos quanto possibilitar ataques cada vez mais adaptativos. A pesquisa adota uma metodologia qualitativa–conceitual, voltada para o desenvolvimento de um arcabouço teórico que capture esse paradoxo e destaque a lacuna de assimetria da IA entre as capacidades ofensivas e defensivas. As evidências indicam que essa lacuna está se ampliando devido a modelos de detecção reativos, restrições de acesso a dados e fragmentação no compartilhamento de inteligência. Embora soluções aplicadas (por exemplo, Mastercard Decision Intelligence Pro; SymphonyAI Sensa Copilot) relatem melhorias na precisão da detecção e na eficiência investigativa, sua efetividade parece estar limitada por restrições regulatórias, institucionais e de dados. O estudo propõe um arcabouço que integre arquiteturas híbridas de IA, redes de inteligência interinstitucionais, pesquisa em IA adversária e medidas regulatórias específicas para a IA.
Downloads
Referências
Alay, N., & Al-Baity, H. H. (2020). Deep learning approach for a multimodal biometric recognition system based on fusion of iris, face, and finger vein traits. Sensors, 20(19), 5523. https://doi.org/10.3390/s20195523
Apache Flink. (n.d.). Apache Flink: Stateful computations over data streams. The Apache Software Foundation. Retrieved from https://flink.apache.org/
Apache Kafka. (n.d.). Apache Kafka: A distributed streaming platform. The Apache Soft-ware Foundation. Retrieved from https://kafka.apache.org/
Aros, C., Astudillo, P., Riffo, V., Vásquez, R., & Díaz, J. (2024). Financial fraud detection through the application of machine learning techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 312. https://doi.org /10.1057/s41599-024-03606-0
Assefa, S. A., Dervovic, D., Mahfouz, M., Tillman, R. E., Reddy, P., & Veloso, M. (2020). Generating synthetic data in finance: Opportunities, challenges, and pitfalls. Proceedings of the First ACM International Conference on AI in Finance, 1–8. https://doi.org/10.1145/3383455.3422559
Association of Certified Fraud Examiners. (2025). What is fraud? Retrieved May 15, 2025, from https://www.acfe. com/fraud-resources/-what-is-fraud
Bello, O. A., & Komolafe, O. (2024). Artificial intelligence in fraud prevention: Exploring techniques and applications, challenges, and opportunities. Computer Science & IT Research Journal, 5(6), 1505–1520. Retrieved from https://fepbl.com/index .php/csitrj/article/view/1252
Boden, M. A. (Ed.). (1996). Artificial intelligence. Elsevier.
De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135. https://doi.org/10.1108/LR-06-2015-0061
Directive (EU) 2017/1371 of the European Parliament and of the Council of 5 July 2017 on the fight against fraud to the Union’s financial interests by means of criminal law, Article 3(2), Journal officiel de l'Union européenne, p. 34.
Falade, P. V. (2023). Decoding the threat landscape: ChatGPT, FraudGPT, and WormGPT in social engineering attacks. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 87–190. arXiv:2310.05595. https://doi.org/10.48550/arXiv.2310.05595
Federal Bureau of Investigation, Internet Crime Complaint Center. (2022). Internet Crime Report 2022. https://www.ic3.gov/AnnualReport/Reports/2022_ic3report.pdf.
Federal Bureau of Investigation. (2024). Internet Crime Report 2023. Internet Crime Complaint Center (IC3). Retrieved from https://www.ic3.gov/Media/PDF/Annual Report/2023_IC3Report.pdf
Federal Bureau of Investigation. (2025). Internet Crime Report 2024. Internet Crime Complaint Center (IC3). Retrieved from https://www.ic3.gov/Media/PDF/Annual Report/2024_IC3Report.pdf
Hilpisch, Y. (2020). Artificial intelligence in finance. O’Reilly Media.
https://doi.org/10.22215/timreview/1282
Igba, E., Salam Olarinoye, H., Ezeh Nwakaego, V., Batur Sehemba, D., Shade Oluhaiyero, Y., & Okika, N. (2025). Synthetic Data Generation Using Generative AI to Combat Identity Fraud and Enhance Global Financial Cybersecurity Frameworks. International Journal of Scientific Research and Modern Technology, 4(2), 1–19. https://doi.org/10 .5281/zenodo.14928919
International Federation of Accountants. (n.d.). Auditor's responsibility to consider fraud in an audit of financial statements: ISA (240) (p. 179). Retrieved from http://www.ifac .org
Javaheri, A., Alimohammadi, S., Yazdinejad, A., Parizi, R. M., Dehghantanha, A., & Karimipour, H. (2023). Cybersecurity threats in FinTech: A systematic review. arXiv.https://doi.org/10.48550/arXiv.2312.01752
Kaushik, P., Garg, V., Priya, A., & Kant, S. (2025). Financial fraud and manipulation: The malicious use of deepfakes in business. In Deepfakes and their impact on business (pp. 173–196). IGI Global. https://doi.org/10.4018/979-8-3693-6890-9.ch008
Khan, M. I., Arif, A., & Khan, A. R. A. (2024). AI Driven Threat Detection: A Brief Overview of AI Techniques in Cybersecurity. BIN: Bulletin of Informatics, 2(2), 248–261. Retrieved from https://ojs.jurnalmahasiswa.com/ojs/index.php/bin/article/view/357
Kuraku, C., Gollangi, H. K., & Sunkara, J. R. (2020). Biometric Authentication In Digital Payments: Utilizing AI And Big Data For Real-Time Security And Efficiency. Educational Administration: Theory and Practice, 26(4), 954–964. https://doi.org/10.53555 /kuey.v26i4.759
Lemonnie, J. (2025, May 12). The truth about OnlyFake and generative AI fraud. Resistant.AI. https://resistant.ai/blog/onlyfake-generative-ai-fraud
Liu, Z., Chan, K. C., & Chimhundu, R. (2024). Mapping the landscape of financial technology (FinTech) research: A systematic review and bibliometric analysis. Financial Innovation, 10(1), 1–26. https://doi.org/10.1186/s40854-023-00524-z
Masood, M., Nawaz, M., Javed, A., Irtaza, A., & Mahmood, M. T. (2023). Deepfakes generation and detection: State-of-the-art, open challenges, countermeasures, and way forward. Applied Intelligence, 53(4), 3740–3768. https://doi.org/10.1007/ s10489-022-03832-x
Mastercard. (2024a, February 13). Mastercard supercharges consumer protection with Gen AI.https://www.mastercard.com/news/press/2024/february/mastercard-supercharges-consumer-protection-with-gen-ai/
Mohitkar, C., Kharat, A., Nashirkar, N., Pardeshi, T., & Gaikwad, P. S. (2025). Passive Captcha: AI-driven bot detection for seamless user experience. International Journal of Environmental Sciences, 11(1), 362–370. Retrieved from https://www.cibtech.org/ J-ENV-SCI/PUBLICATIONS /2025/Vol_11_No_1/50-JES-014-CHIRAG-MOHITKAR.pdf
Ng, A. W., & Kwok, B. K. B. (2017). Emergence of Fintech and cybersecurity in a global financial centre: Strategic approach by a regulator. Journal of Financial Regulation and Compliance, 25(4), 422–434. https://doi.org/10.1108/JFRC-01-2017-0001
Ogunjide, J. O. (2025). Biometric Authentication and Fraud Detection in Fintech. International Journal of Economics, Business and Management, 11(4), 183–189.https://doi.org/10.56201/ijebm. vol.11.no4.2025 .
Olanrewaju, A. G. (2025). Artificial intelligence in financial markets: Optimizing risk management, portfolio allocation, and algorithmic trading. International Journal of Research Publication and Reviews, 6, 8855–8870.https://doi.org/10.55248/gengpi .2025.6.4.2710
Onesi-Ozigagun, O., Ololade, Y. J., Eyo-Udo, N. L., & Ogundipe, D. O. (2024). AI-driven biometrics for secure fintech: Pioneering safety and trust. International Journal of Engineering Research Updates, 6(2), 1–12.https://doi.org/10.53430/ijeru.2024.6.2 .0023
Pan, E. (2024). Machine learning in financial transaction fraud detection and prevention. Transactions on Economics, Business and Management Research, 5, 243–249.https://doi.org/10.62051/ 16r3aa10
Pazouki, S., Jamshidi, M. B., Jalali, M., & Tafreshi, A. (2025). The integration of big data in FinTech: Review of enhancing financial services through advanced technologies. World Journal of Advanced Research and Reviews, 25(1), 546–556. https://doi.org/10 .30574/wjarr.2025.25.1.0060
Pearson, T. A., & Singleton, T. W. (2008). Fraud and forensic accounting in the digital environment. Issues in Accounting Education, 23(4), 545–559. https://doi.org/10. 2308/iace.2008.23.4.545
R Project. (n.d.). An introduction to isolation forests (isotree package vignette). The R Project for Statistical Computing. Retrieved from https://cloud.r-project.org/web/ packages/isotree/vignettes/An_Introduction_to_Isolation_Forests.html
Russell, S. J., & Norvig, P. (1995). Artificial intelligence: A modern approach (3rd ed.). Pearson.
Schiavone, E., Ceccarelli, A., Carvalho, A., & Bondavalli, A. (2019). Design, implementation, and assessment of a usable multi-biometric continuous authentication system. International Journal of Critical Computer-Based Systems, 9(3), 215–247. https://doi.org/10.1504/IJCCBS.2019.104490
Schmitt, M., & Flechais, I. (2024). Digital deception: Generative artificial intelligence in social engineering and phishing. Artificial Intelligence Review, 57(12), 324. https://doi.org/10.1007/s 10462-024-10735-5
Sharma, V., Pandey, B., & Kumar, V. (2016). Importance of big data in financial fraud detection. International Journal of Automation and Logistics, 2(4), 332–348. https://doi.org/10.1504/IJAL. 2016.080339
Simplilearn. (n.d.). K-means clustering algorithm. Simplilearn. Retrieved from https:// www.simplilearn.com/tutorials/machine-learning-tutorial/k-means-clustering-
SymphonyAI. (2024a). Sensa Copilot. https://www.symphonyai.com/glossary/financial-services/sensa-copilot/
Timoney, M. (2025, April 17). Synthetic identity fraud continues to expand—and Gen AI is making it worse. Federal Reserve Bank of Boston. Retrieved from https://www .bostonfed.org/news-and-events/news/2025/04/synthetic-identity-fraud-financial-fraud-expanding-because-of-generative-artificial-intelligence. aspx
Udeh, E. O., Amajuoyi, P., Adeusi, K. B., & Scott, A. O. (2024). The role of big data in detecting and preventing financial fraud in digital transactions. World Journal of Advanced Research and Reviews, 22(2), 1746–1760. https://doi.org/10.30574 /wjarr.2024.22.2.1575
Vacca, John R. Biometric technologies and verification systems. Elsevier, 2007.
Wenbo Guo, Yujin Potter, Tianneng Shi, Zhun Wang, Andy Zhang, and Dawn Song, SoK: Frontier AI’s Impact on the Cybersecurity Landscape, arXiv preprint arXiv:2504.05408 (2025), https://arxiv.org/abs/2504.05408.
Westerlund, M. (2019). The emergence of deepfake technology: A review. Technology Innovation Management Review, 9(11), 39–52.
Zhang, X. (2024). Machine Learning Insights into Digital Payment Behaviors and Fraud Prediction. Applied and Computational Engineering, 77(1), 203–209. https://doi.org/ 10.54254/2755-2721/77/2024MA0066
Downloads
Publicado
Edição
Secção
Licença
Direitos de Autor (c) 2025 Siham Rahmani
Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição-NãoComercial-CompartilhaIgual 4.0.
