We live in an era where technology is rapidly reshaping the financial landscape; the integration of artificial intelligence (AI) offers striking advances in innovation and efficiency. Survey results published in Nasdaq Verafin’s 2024 Global Financial Crime Report, indicate 70% of anti-financial professionals said their institutions are planning to increase investment in AI technology for financial crime management in the next one to two years.
A recent report from the Department of Treasury, Managing Artificial Intelligence-Specific Cybersecurity Risks in the Financial Services Sector, focuses on the current state of AI-related cybersecurity and fraud risks in financial services. Drawing on insights from 42 interviews conducted in late 2023, with various stakeholders including financial institutions of all sizes, financial sector trade associations, cybersecurity and anti-fraud service providers, regulatory advocacy groups, and other service providers, the report paints a picture of the current state of AI in financial services. It establishes that AI is no longer a futuristic concept but a present-day reality that is being used within the financial sector. Use cases span the spectrum of financial operations, from enhancing customer interactions to executing complex financial models, making it abundantly clear that AI is revolutionizing the way financial institutions operate.
Six Key Takeaways for the Adoption of AI
The financial sector has leveraged AI for many years to protect against cyber threats and fraud. However, recent advances, such as generative AI, have caused those in the financial sector to reflect on how it will be used moving forward. Based on the report, here are six key takeaways on how financial institutions can lead with responsible adoption.
1) Adopt Responsible AI Practices
As AI becomes widely used, responsible adoption is imperative to ensure it is used ethically while considering its impact on customers and society. The ethical implications and the potential societal impact of its incorporation into the financial sector could have major impacts. The pursuit of innovation should not overshadow the core values of trust and integrity that underpin the financial services industry.
2) Update Risk Management Frameworks
With the emergence of new AI models, existing risk management frameworks may not be adequate to cover emerging AI technologies. Current risk management frameworks need to evolve to include updating policies, procedures, and tools to address the unique challenges posed by AI. Key examples of risk management frameworks relevant to the financial sector include model, technology, data and third-party risk management as well as compliance.
3) Ensure Data Integrity
“Applying appropriate risk management principles to AI development is critical from a cybersecurity perspective, as data poisoning, data leakage, and data integrity attacks can take place at any stage of the AI development and supply chain.”
The quality and quantity of data used to train AI models underpins the eventual effectiveness of these models to achieve their use cases. With the increasing reliance on third-party data and technology providers, as well as criminal efforts to compromise training data, ensuring data authenticity and security is paramount to maintain trust and prevent fraud.
4) Improve Cross-Enterprise Collaboration
“In the case of cybersecurity and anti-fraud AI usage, interviewees largely agreed that effectively managing risks requires collaboration across the financial services sector.”
Cross-enterprise collaboration in the fight against cyber threats is key. Sharing knowledge, resources, and best practices can forge a formidable defense, turning individual vulnerabilities into collective resilience to develop robust security measures against cyber threats. Financial institutions must work together to standardize strategies for managing AI-related risks and realize more effective mitigation of emerging threats.
5) Employ Advanced Fraud Detection Systems
Though there are many opportunities to increase the efficiency of daily workflows and compliance processes, the report underscores that financial institutions also need to be ready for sophisticated AI-enabled attacks, such as phishing schemes, AI-powered fraud, and the unsettling realism of deepfake technologies. To mitigate these risks financial institutions must have advanced fraud detection systems in place that improve the effectiveness of anti-financial crime efforts, in order to stay ahead of technologically adept fraudsters who utilize AI for illicit purposes.
6) Foster a Culture of Continuous Learning
A culture of continuous learning at financial institutions is crucial to stay abreast of the rapid developments in AI. By encouraging curiosity and innovation, investing in training and development, leveraging industry partnerships, implementing knowledge sharing platforms, promoting cross-functional teams and adapting policies and procedures, the financial industry can keep pace with the latest AI developments and ensure that risk management practices are current and effective.
A Roadmap for the Future
“AI has the potential to spur innovation and drive efficiency, but its use in financial services requires thoughtful implementation and supervision to manage potential risks.”
The Treasury’s report provides an important roadmap for navigating the AI landscape to help financial institutions manage the risks. In today’s rapidly evolving AI landscape, the financial services industry can lead by example, setting the standard for responsible AI adoption and ensuring that the transition into this new era is as secure as it is transformative.
