Abstract
This study examines the deployment and effectiveness of AI-based threat detection systems across 25 financial institutions in Hong Kong. We measure detection rates, false positive reduction, and operational impact over a 12-month observation period (January 2025 – December 2025).
1. Introduction
The financial sector in Hong Kong faces an evolving threat landscape characterised by increasingly sophisticated attack techniques. Traditional signature-based detection methods are increasingly insufficient against novel threats, prompting organisations to adopt AI-powered security solutions.
2. Methodology
We conducted a mixed-methods study combining quantitative analysis of security event logs with qualitative interviews of security operations centre (SOC) managers. Our participant pool comprised 25 financial institutions representing banks, insurance companies, and asset managers.
3. Key Findings
- AI-powered detection reduced false positives by an average of 42% compared to rule-based systems
- Mean time to detection (MTTD) decreased from 4.2 hours to 1.1 hours
- 87% of participants reported improved analyst productivity
- However, 32% experienced initial implementation challenges related to data quality and model tuning
4. Recommendations
Organisations should invest in data quality improvement programmes before deploying AI detection systems. A phased rollout approach, starting with well-defined use cases, yields better results than enterprise-wide deployments.