Artificial Intelligence in Combating Synthetic Identity Fraud: A Comparative Case Study of Amazon and Shopify E-Commerce

References

[1] The Federal Reserve, “Synthetic Identity Fraud Defined | FedPayments Improvement,” The Federal Reserve. Accessed: Jul. 16, 2025. [Online]. Available: https://fedpaymentsimprovement.org/strategic- initiatives/payments-security/synthetic-identity- payments-fraud/synthetic-identity-fraud- defined/

[2] M. Timoney, “Gen AI is ramping up the threat of synthetic identity fraud - Federal Reserve Bank of Boston,” Federal Reserve Bank of Boston. Accessed: Jul. 16, 2025. [Online]. Available: https://www.bostonfed.org/news- and-events/news/2025/04/synthetic-identity- fraud-financial-fraud-expanding-because-of- generative-artificial-intelligence.aspx

[3] E. Udinmwen, “Europe and North America are drowning in deepfake fraud as scammers break ID systems faster than ever | TechRadar,” TechRadar. Accessed: Jul. 16, 2025. [Online]. Available: https://www.techradar.com/pro/security/syntheti c-id-document-fraud-is-exploding-worldwide- thanks-entirely-to-generative-ai-heres-how-to- stay-safe

[4] L.-H. Liang, “Sumsub reveals 300% increase in identity document fraud | Biometric Update,” BioMetric. Accessed: Jul. 16, 2025. [Online]. Available: https://www.biometricupdate.com/202506/sums ub-reveals-300-increase-in-identity-document- fraud

[5] Experian, “‘Synthetic fraud’ reaches record levels,” Experian. Accessed: Jul. 16, 2025. [Online]. Available: https://www.experianplc.com/newsroom/press- releases/2025/-synthetic-fraud--reaches-record- levels

[6] C. J. Zhang, A. Q. Gill, B. Liu, and M. J. Anwar, “AI-based Identity Fraud Detection: A Systematic Review,” Jan. 2025, [Online]. Available: http://arxiv.org/abs/2501.09239

[7] R. Gupta, “Cybersecurity Threats in E- Commerce: Trends and Mitigation Strategies,” J. Adv. Manag. Stud., vol. 1, no. 3, pp. 1–10, Sep. 2024,

[8] E. Ortanez, “E-Commerce Fraud Will More Than Double by 2029,” Chargeblast. Accessed: Jul. 16, 2025. [Online]. Available: https://www.chargeblast.com/blog/ecommerce- fraud-will-more-than-double-by-2029/

[9] P. Rawat, M. R, H. P. Josyula, A. Kataria, and S. R. Landge, “Consumer Perception And Adoption Of Digital Payment Methods: A Study On Trust And Security Concerns,” Educ. Adm.. Theory Pract., pp. 6022–6029, Apr. 2024, 10.53555/kuey.v30i4.2334.

[10] J. M. Sánchez, “Synthetic Identity Fraud: What It Is & How to Prevent It | Veridas,” VeriDas. Accessed: Jul. 16, 2025. [Online]. Available: https://veridas.com/en/synthetic-identity-fraud/

[11] ACI Worldwide, “Synthetic Identify Fraud: What It Is & How to Prevent It,” ACI Worldwide. Accessed: Jul. 16, 2025. [Online]. Available: https://www.aciworldwide.com/synthetic- identity-fraud

[12] J. Yang, K. Chen, K. Ding, C. Na, and M. Wang, “Auto Insurance Fraud Detection with Multimodal Learning,” Data Intell., vol. 5, no. 2, pp. 388–412, May 2023, 10.1162/dint_a_00191.

[13] S. M. Devaraj, “Next-Generation Fraud Detection: A Technical Analysis of AI Implementation in Financial Services Security,” Int. J. Multidiscip. Res. …, vol. 6, no. 6, pp. 1– 10, 2024, [Online]. Available: https://www.researchgate.net/profile/Surendra- Mohan-Devaraj- 2/publication/390271109_Next- Generation_Fraud_Detection_A_Technical_An alysis_of_AI_Implementation_in_Financial_Ser vices_Security/links/67e6a62f9b1c6c48775fde9 7/Next-Generation-Fraud-Detection-A-T

[15] R. T. . Krishna, K. Akshaya, K. Deepika, R. Vanaja, and S. Ganesh, “Design and Analysis of mmWave Patch Antenna for 5G and 6G Applications,” Int. J. Sci. Res. Sci. Technol., vol. 12, no. 2, pp. 683–692, Apr. 2025, 10.32628/IJSRST.

[16] S. Lalchand, J. Gregorie, and V. Srinivas, “Biometrics in banking | Deloitte Insights,” Deloitte Insights. Accessed: Jul. 18, 2025. [Online]. Available: https://www.deloitte.com/us/en/insights/industr y/financial-services/financial-institutions- synthetic-identity-fraud.html

[17] S. Xi Rao, J. Jiang, Z. Han, and H. Yin, “Fraud Detection in E-Commerce: A Systematic Review of Transaction Risk Prevention,” in Anomaly Detection - Methods, Complexities and Applications [Working Title], IntechOpen, 2025.

[18] P. Baxter and S. Jack, “Qualitative Case Study Methodology: Study Design and Implementation for Novice Researchers,” Qual. Rep., Jan. 2015, 3715/2008.1573.

[19] A. Biswas, “Prevent fake account sign-ups in real time with AI using Amazon Fraud Detector | Artificial Intelligence,” AWS. Accessed: Jul. 16, 2025. [Online]. Available: https://aws.amazon.com/blogs/machine- learning/prevent-fake-account-sign-ups-in-real- time-with-ai-using-amazon-fraud-detector/

[20] G. Praspaliauskas and V. Raman, “Real-time fraud detection using AWS serverless and machine learning services | Artificial Intelligence,” AWS. Accessed: Jul. 18, 2025. [Online]. Available: https://aws.amazon.com/blogs/machine- learning/real-time-fraud-detection-using-aws- serverless-and-machine-learning-services/

[21] V. Soni and S. Gupta, “Account Takeover Prevention and Identity Verification with AI Models,” Int. Joural Intell. Syst. Appl. Eng., vol. 11, no. 11, pp. 632–643, 2023.

[22] AWS, “Amazon Fraud Detector Customers - Amazon Web Services,” Amazon. Accessed: Jul. 18, 2025. [Online]. Available: https://aws.amazon.com/fraud- detector/customers/

[23] F. van Coller, “Shopify Protecting Millions of Merchants From Fraud - Shopify,” Shopify. Accessed: Jul. 16, 2025. [Online]. Available: https://www.shopify.com/blog/shopify-best-in- class-technology-protects-millions-of- merchants-from-fraud

[24] Shopify, “Protect your business with Shopify’s fraud tools - Shopify,” Shopify. Accessed: Jul. 18, 2025. [Online]. Available: https://www.shopify.com/fraud-solutions

[25] Shopify Help Centre, “Shopify Help Center | Fraud analysis,” Shopify. Accessed: Jul. 18, 2025. [Online]. Available: https://help.shopify.com/en/manual/fulfillment/ managing-orders/protecting-orders/fraud- analysis

[26] F. van Coller, “How Shopify Payments Uses Machine Learning to Boost Payment Success Rates by 0.26% and Cut Fraud Chargebacks by 20% (2025) - Shopify,” Shopify. Accessed: Jul. 18, 2025. [Online]. Available: https://www.shopify.com/enterprise/blog/shopif y-payments-pre-authorization

[27] Shopify Help Centre, “Shopify Help Center | Shop sales channel performance analytics,” Shopify. Accessed: Jul. 18, 2025. [Online]. Available: https://help.shopify.com/en/manual/online- sales-channels/shop/analytics

[28] R. Wang, J. Chan, K. Khurmi, and M. Xu, “Fraud detection empowered by federated learning with the Flower framework on Amazon SageMaker AI | Artificial Intelligence,” AWS Amazon. Accessed: Jul. 16, 2025. [Online]. Available: https://aws.amazon.com/blogs/machine- learning/fraud-detection-empowered-by- federated-learning-with-the-flower-framework- on-amazon-sagemaker-ai/

[29] D. Mehta, “How Amazon uses AI innovations to stop fraud and counterfeits,” Amazon. Accessed: Jul. 16, 2025. [Online]. Available: https://www.aboutamazon.com/news/policy- new-views/amazon-brand-protection-report- 2024-counterfeit-products

[30] Brandefense, “Fraud Fighters: Merging AI And Human Expertise To Stop Cybercrime - Brandefense,” Brandefense. Accessed: Jul. 18, 2025. [Online]. Available: https://brandefense.io/blog/drps/fraud-fighters- merging-ai-and-human-expertise-to-stop- cybercrime/

[31] C. Morales, “Human + AI Collaborative: Efforts in Fraud Detection - FraudLabs Pro Articles & Tutorials,” FraudLabs. Accessed: Jul. 18, 2025. [Online]. Available: https://www.fraudlabspro.com/resources/tutorial s/human-ai-collaborative-efforts-in-fraud- detection/

[32] P. Santilli, “The Integration of AI and Human Intelligence in Fraud Detection - Strategic Consortium of Intelligence Professionals (SCIP),” Scip. Accessed: Jul. 18, 2025. [Online]. Available: https://www.scip.org/news/653018/The- Integration-of-AI-and-Human-Intelligence-in- Fraud-Detection-.htm

[33] Federal Reserve, “Federal Reserve White Paper on Synthetic Identity Fraud Mitigation - FedPayments Improvement,” Federal Reserve. Accessed: Jul. 18, 2025. [Online]. Available: https://fedpaymentsimprovement.org/news/press -releases/federal-reserve-system-white-paper- examines-mitigation-of-synthetic-identity- payments-fraud/

[34] Splunk, “Creating a Fraud Risk Scoring Model Leveraging Data Pipelines and Machine Learning with Splunk | Splunk,” Splunk. Accessed: Jul. 18, 2025. [Online]. Available: https://www.splunk.com/en_us/blog/platform/cr eating-a-fraud-risk-scoring-model-leveraging- data-pipelines-and-machine-learning-with- splunk.html

[35] Market Screener, “Splunk : Creating a Fraud Risk Scoring Model Leveraging Data Pipelines and Machine Learning with Splunk | MarketScreener,” Market Screener. Accessed: Jul. 18, 2025. [Online]. Available: https://www.marketscreener.com/quote/stock/S PLUNK-INC-10454129/news/Splunk-Creating- a-Fraud-Risk-Scoring-Model-Leveraging-Data- Pipelines-and-Machine-Learning-with-Spl- 32449009/

[36] J. B. Simon, D. Karkada, N. Ghosh, and M. Belkin, “More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory,” May 2024, [Online]. Available: http://arxiv.org/abs/2311.14646

[37] A. Parihar, M. Domb, and S. Joshi, “Fraud Detection in E-commerce Platforms Using Data Mining Algorithms,” 2025, pp. 51–62. 10.1007/978-981-97-9559-8_6.

[38] R. Pahuja, “New Challenges in Fraud Risk and Prevention for Retail and eCommerce - with Leaders from Riskified, eBay, and Comcast - Emerj Artificial Intelligence Research,” Emerj. Accessed: Jul. 18, 2025. [Online]. Available: https://emerj.com/new-challenges-in-fraud-risk- and-prevention-for-retail-and-ecommerce- leaders-from-riskified-ebay-comcast/

[39] G. Rama, “AWS Helps ML Devs Streamline Human Reviews with Amazon A2I -- AWSInsider,” AWS Insider. Accessed: Jul. 18, 2025. [Online]. Available: https://awsinsider.net/articles/2020/04/27/aws- human-reviews-amazon-a2i.aspx

[40] S. Godavarthi, M. Mona, and M. Pranusha, “Reviewing online fraud using Amazon Fraud Detector and Amazon A2I | Artificial Intelligence,” AWS. Accessed: Jul. 18, 2025. [Online]. Available: https://aws.amazon.com/blogs/machine- learning/reviewing-online-fraud-using-amazon- fraud-detector-and-amazon-a2i/

@article{1710309,
      author = {Adepeju Deborah Bello, Oluwaseyi Babatunde Oguntola, John Achidok, Ayodeji Temitope Ajibade; Oluwatosin Omotoriogun, Florence Olabisi Ogunleye; Oluwadamilola Ayoola},
      title = {Artificial Intelligence in Combating Synthetic Identity Fraud: A Comparative Case Study of Amazon and Shopify E-Commerce},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {2},
      pages = {921-932},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710309.pdf},
      abstract = {Synthetic identity fraud is a rapidly growing threat, accounting for over $12?billion in losses (?25% of global identity fraud) in 2024 and is projected to rise further. This study evaluates how AI-driven methods detect and prevent synthetic identity fraud on e-commerce platforms. Using a qualitative multiple-case approach (Amazon, Shopify). This study reviews secondary sources including technical documentation, industry reports, and academic literature. The findings of the study show that advanced AI techniques such as anomaly detection, behavioural analytics, and hybrid supervised models have proven to substantially reduce fraudulent activity in the selected cases. The cases selected in this study report fraud reductions of about 30?40% on orders, along with dramatic drops in false positives. However, there are a number of difficulties still experienced in the use of AI, often in terms of false-positives, data availability to train models, and also, the pace of advancement in fraudulent patterns like synthetic identities generated by generative AI.  This study recommends continuous refinement of models, oversight of AI decisions by humans (hybrid reviews), and the cooperation of industry stakeholders, through threat sharing intelligence, to aid in adjusting to changing threats.},
      keywords = {Synthetic Identity Fraud; Artificial Intelligence; E-Commerce; Fraud Detection},
      month = {August},
      doi = {https://doi.org/10.64388/IREV9I2-1710309-4729}
  }