撮要

In an era defined by digital transformation, the ability to verify customer identities and detect suspicious transactions in real time has become foundational to banking integrity. This responsibility extends beyond regulatory compliance; it is central to maintaining customer trust in an increasingly automated financial ecosystem. Yet, as financial institutions strengthen their defences, fraudsters are leveraging the same technological advancements, from generative artificial intelligence (AI) and deepfake synthesis to fraud-as-a-service platforms, to exploit systemic vulnerabilities with unprecedented speed and scale.

This case uses the Hong Kong and Shanghai Banking Corporation (HSBC) as an example to examine how a bank confronts a rapidly evolving, AI-enabled, deepfake threat landscape. The central challenge is no longer merely responding to attacks but anticipating them, a significant change from a reactive to a proactive, predictive security approach. The case explores whether traditional banking defences can keep pace with AI-empowered criminal innovation and how a bank can stay ahead of the evolving AI-enabled cybersecurity threats.

Using this case study, students are tasked with analysing the dual-edged nature of technological adoption: while HSBC employs AI for fraud detection, biometric authentication, and behavioural analysis, adversaries deploy similar tools to create synthetic identities, falsify documents, and impersonate legitimate users. The case also invites discussion on strategic trade-offs—balancing customer convenience with security rigor, innovation velocity with operational resilience, and human oversight with AI autonomy.

By examining HSBC’s partnerships with regulators, technology firms, and global law enforcement, students will evaluate the effectiveness of collaborative defence frameworks and the role of explainable AI in building auditable, trustworthy systems. Ultimately, the case challenges future leaders to design a fraud prevention strategy that is not only technologically sophisticated but also ethically grounded and adaptable to the unknown threats of tomorrow.

学习目标

1. To analyse the emerging risks and unique challenges that AI-generated deepfakes pose for fraud in the banking industry.

2. To analyse the motivations of fraudsters using deepfake technology.

3. To identify key external stakeholders and strategic partners essential for collaborative defence against cybersecurity threats like deepfakes.

4. To evaluate the strategic rationale for banks to change from reactive security measures to proactive, predictive security protocols.

5. To develop and assess actionable strategies for enhancing deepfake-detection capabilities to stay ahead of evolving threats.