AI Countering AI Fraud - Strategies for Financial Institutions to Combat Emerging Forms of Financial Deception by AI
In the digital age, the landscape of financial fraud is constantly evolving, with criminals using advanced techniques such as deepfakes and synthetic identities to bypass traditional security measures. This article explores the strategies financial institutions are adopting to combat these challenges while maintaining a positive customer experience.
Legacy systems, designed for a different era, struggle to handle AI workloads. Modernizing data platforms and migrating to the cloud is a crucial step towards accessing scalable storage, streaming, ML runtimes, and AI services that can help detect and prevent fraud.
One of the key benefits of these modern systems is their ability to provide a single, trustworthy view of customers, accounts, devices, and transactions. This unified perspective is essential for identifying patterns and inconsistencies that may indicate fraudulent activity.
Modern anti-fraud platforms employ a layered approach, combining various AI and machine learning techniques to address specific attack vectors. For instance, computer vision checks for inconsistent eye reflections, lighting, skin texture, or audio-video mismatch in videos. Graph neural networks (GNNs) are used to map networks of accounts, devices, and IPs to expose coordinated fraud groups that are invisible in isolation.
AI copilots assist analysis specialists in collecting and summarizing data for specific requests, enabling faster and more accurate fraud detection. Biometric verification, such as facial recognition with liveness detection, is used to defeat simple video replay and check for signs of deepfakes.
However, overly sensitive detection systems can produce false positives, which can negatively impact the customer experience. The metric of the median time for recovery from a false block should be tracked to ensure customer satisfaction. Quick recovery, ideally via a step-up authentication, is essential to avoid degrading the customer experience.
Financial institutions lost $12.5 billion to AI-augmented fraud in the US in 2024, a 25% increase over the prior year. A 10% reduction in false positives could result in significant cost savings, chargeback reduction, and customer retention. The importance of justifying future investment in fraud prevention to stakeholders by defining and tracking relevant metrics, such as the 10% reduction in false positives, cannot be overstated.
Innovative content provenance standards like the Coalition for Content Provenance and Authenticity (C2PA) help verify source authenticity, providing an additional layer of protection against fraud. A central "fraud label registry" should be set up to track 12 months of confirmed fraud cases across core products and channels.
The focus on preventing fraud should not happen at the expense of the customer experience. It is essential to plan the business process to prevent this trade-off. Synthetic data is used to supplement real data for training detection systems on rare or hypothetical fraud patterns, ensuring that the systems can adapt to emerging threats.
Financial institutions prioritize reliable AI practices, including bias checks, explainability of model decisions, immutable audit trails, and thorough model documentation. Deploying AI for fraud prevention struggles with fragmented data silos and quality issues, making these practices even more crucial.
In conclusion, the modernization of fraud prevention strategies requires a balanced approach that prioritizes both security and the customer experience. By adopting advanced technologies, implementing robust AI practices, and tracking relevant metrics, financial institutions can effectively combat fraud while maintaining a positive relationship with their customers.
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