Developing a cybersecurity maturity model for fintech firms using predictive analytics
1 Department of Marketing and Business Analytics, East Texas A&M University, Texas, USA.
2 Reeks Corporate Services, Lagos, Nigeria.
3 H and R Block Tax Group Inc, Hammond, Indiana USA.
4 Deloitte and Touche LLP, Dallas, TX, USA.
5 Rome Business School, Estonia, Italy.
Review
International Journal of Science and Technology Research Archive, 2025, 08(01), 023-049.
Article DOI: 10.53771/ijstra.2025.8.1.0021
Publication history:
Received on 12 December 2024; revised on 18 January 2025; accepted on 21 January 2025
Abstract:
As the fintech industry expands, so does the sophistication of cybersecurity threats, making it critical for firms to adopt proactive and resilient security measures. This abstract proposes a cybersecurity maturity model specifically designed for fintech firms, incorporating predictive analytics to assess and enhance their cybersecurity posture. By leveraging predictive analytics, this model enables fintech companies to anticipate potential vulnerabilities, detect emerging threats, and strengthen their security strategies before incidents occur. The proposed cybersecurity maturity model is structured into distinct stages, ranging from basic security measures to advanced predictive capabilities. Each stage represents the evolution of a fintech firm's cybersecurity maturity, with predictive analytics playing a central role in moving from reactive to proactive defense mechanisms. Through the integration of machine learning algorithms and data-driven insights, the model can predict future risks based on historical attack data, threat patterns, and internal security metrics. This predictive capability allows fintech companies to identify vulnerabilities in real-time, prioritize security resources, and implement mitigation strategies ahead of potential attacks. The model also emphasizes continuous monitoring and data collection from various sources, such as transaction logs, network traffic, and user behavior, to build a comprehensive security profile. Predictive analytics can then process this data to provide forecasts on potential threats, attack vectors, and security gaps. The application of predictive analytics enhances decision-making, allowing cybersecurity teams to allocate resources more effectively and implement targeted interventions. Furthermore, this cybersecurity maturity model provides a framework for fintech companies to measure their progress, ensuring a systematic approach to enhancing security. It also fosters a culture of continuous improvement, aligning with the dynamic and evolving nature of cybersecurity in the fintech sector. Ultimately, by adopting predictive analytics, fintech firms can enhance their ability to protect digital financial operations, build customer trust, and comply with regulatory standards.
Keywords:
Cybersecurity Maturity Model; Predictive Analytics; Fintech; Risk Assessment; Cybersecurity Posture; Machine Learning; Threat Detection; Digital Financial Operations; Security Resilience; Continuous Monitoring
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