Integrating Artificial Intelligence in organizational cybersecurity: Enhancing consumer data protection in the U.S. Fintech Sector

Ajakaye, Oluwabiyi Oluwawapelumi and Olanrewaju, Ayobami Gabriel and Fawehinmi, David and Afolabi, Rasheed and Pius-Kiate, Gold Mebari (2025) Integrating Artificial Intelligence in organizational cybersecurity: Enhancing consumer data protection in the U.S. Fintech Sector. World Journal of Advanced Research and Reviews, 26 (1). pp. 2802-2821. ISSN 2581-9615

Financial technology (fintech) companies face escalating cyber threats that jeopardize consumer data. This research investigates how integrating artificial intelligence (AI) into organizational cybersecurity can enhance consumer data protection in the U.S. fintech industry. We pose key questions on AI’s role in threat detection, its current use cases and challenges in fintech cybersecurity, and the effectiveness of deep learning models in preventing data breaches. A comprehensive literature review reveals that AI techniques – particularly deep learning models like Long Short-Term Memory (LSTM) networks and Transformers – are increasingly applied for intrusion detection, fraud mitigation, and threat intelligence in fintech cybersecurity​. However, challenges such as adversarial attacks, data bias, regulatory constraints, and implementation costs persist​. To address our research questions, we develop an AI-driven cybersecurity methodology applying LSTM and Transformer models to recent U.S. fintech breach datasets and a benchmark intrusion dataset. Real-world breach data from 2018–2023 (e.g., the Verizon VERIS breach database and public disclosures) and a modern intrusion detection dataset are used to train and evaluate the models. The LSTM-based model and Transformer-based model are assessed on their accuracy, detection speed, and impact on breach prevention. Results show that both models achieve high detection rates (over 98–99% accuracy) in identifying malicious activities, with the Transformer slightly outperforming the LSTM in precision and recall. These AI models dramatically reduce incident response times and flag threats that may otherwise go undetected, aligning with industry reports that organizations using security AI contain breaches significantly faster​. Discussion of the findings connects these performance gains to improved consumer data protection: earlier and more accurate detection of intrusions allows fintech firms to prevent or mitigate data breaches before sensitive customer information is compromised. We also explore how AI integration must be paired with governance, risk, and compliance (GRC) frameworks to address ethical and regulatory considerations. Conclusion: The study concludes that AI-driven cybersecurity holds great promise for strengthening data protection in fintech by augmenting threat detection capabilities and reducing breach impacts. We provide actionable insights for fintech organizations and researchers, highlighting that while AI can substantially enhance cybersecurity resilience and consumer data safety, a socio-technical approach addressing challenges of trust, transparency, and compliance is essential for successful implementation