Fraudsters are evolving rapidly, leveraging shared tools, tactics, and data to outpace traditional defenses. To combat this, financial institutions must shift from working in isolation to embracing collaboration. By sharing insights and leveraging advanced AI, the industry can outsmart even the most sophisticated fraud schemes.
At FiVerity, we’re at the forefront of this transformation, where collaboration meets cutting-edge technology. In our new blog series, Collaboration In Action, we’ll showcase how AI-enhanced information sharing and collaboration is reshaping fraud prevention. Through real-world examples, we’ll explore how institutions are using shared fraud intelligence to detect and prevent threats before they escalate.
Join us as we uncover how AI and industry collaboration are not just reacting to fraud but proactively building a more secure financial ecosystem.
What You Can Expect in the Collaboration In Action Blog Series
Fraud doesn’t operate in isolation—and neither should we. In this series, we’ll explore how AI-enhanced information sharing and collaboration are revolutionizing fraud prevention. By breaking down silos and harnessing collective intelligence, financial institutions can detect threats faster, prevent losses, and protect their customers.
Here’s a glimpse into some of the use cases we’ll cover:
1. Detecting Emerging Fraud Trends Early
Fraudsters evolve quickly, creating schemes that often go unnoticed until multiple organizations report losses. AI-powered systems analyze shared fraud signals, such as unusual loan application patterns or repeated identity mismatches, to detect emerging trends early. Institutions can proactively update policies and procedures to stay ahead of emerging threats, saving millions in potential losses.
2. Preventing Account Takeovers (ATO)
Fraudsters use AI, automation, and shared personal information to infiltrate customer accounts, often leaving ATO undetected until significant damage is done. By sharing login anomalies or failed authentication patterns across institutions, AI can identify takeover attempts and alert others to take preventative action. Real-time collaboration minimizes the window and scale for fraudsters, safeguarding accounts and reinforcing customer trust.
3. Filing Higher-Quality SARs and Super SARs
Suspicious Activity Reports (SARs) are often delayed due to incomplete information or manual processes, reducing their effectiveness. Securely shared data and cross-institutional fraud intelligence help institutions identify broader fraud schemes, enabling faster and more accurate SAR filings. Streamlined processes improve compliance and support law enforcement with richer intelligence for larger-scale investigations.
4. Stopping Synthetic Identity Fraud
Fraudsters combine real and fake data to create synthetic identities, aging them across multiple institutions before committing large-scale fraud. AI analyzes shared data to identify patterns of synthetic behavior, such as inconsistencies in identity usage across institutions. Collaboration shortens the lifespan of synthetic identities, preventing costly fraud and reducing financial exposure.
5. Stopping Fraud at Account Origination
Fraud often starts at account opening, where traditional KYC checks may miss risk signals in siloed data.Institutions use AI to compare identity data against shared fraud signals, identifying suspicious accounts before they gain access. Fraud is stopped at the source, protecting financial systems and reducing risk.
6. Preventing Fraudulent Loan Applications
Fraudsters submit false applications to multiple lenders, exploiting gaps in communication. AI detects duplicate or suspicious applications across institutions, enabling lenders to identify fraud before issuing loans. Shared intelligence prevents large-scale loan fraud, protecting both institutions and legitimate borrowers.
7. Accelerating Fraud Investigations
Manual processes delay fraud response, increasing financial and reputational risk. AI analyzes shared intelligence to provide actionable insights and cross-institutional context, expediting investigations. Faster investigations minimize damage and improve operational efficiency.
8. Intelligent 314(b) Collaboration
The 314(b) provision allows information sharing for fraud detection, but manual processes often hinder its effectiveness. Automated, secure sharing identifies connections across suspicious activities, uncovering fraud schemes in real-time. Institutions can detect complex schemes like money mule networks faster, enhancing compliance and fraud prevention.
The Power of Collaboration
Fraudsters exploit gaps between institutions, but AI-enhanced collaboration closes those gaps. By securely sharing fraud intelligence, financial institutions can detect threats earlier, respond faster, and fortify the entire ecosystem.
In this series, we’ll show how AI-driven collaboration is transforming fraud prevention, helping institutions protect their customers and reduce losses. Together, we can build a safer financial system.
Stay Tuned
In the coming weeks, we’ll dive deeper into each use case, exploring real-world examples and success stories.
At FiVerity, we’re proud to power collaboration with our Anti-Fraud Collaboration Platform, enabling secure information sharing and AI-enhanced fraud detection.
Have questions or want to learn more? Contact us today to schedule a demo or consultation!