Top 3 considerations lenders need for better fraud protection
Recently Tom Algie, IDology’s National Sales Manager for Consumer Finance, joined GDS Link’s Lending Podcast to share how lenders can better protect themselves against trending fraud with better identity verification. Highlights from the conversation include best practices for three must-haves to look for when considering an identity verification (IDV) solution.
1. Tech that relies on multiple layers of data
Lenders must be able to access enough data to identify if and when a user’s onboarding information or behavior is unusual. A consumer’s SSN is often the ‘linchpin’ for financial services providers when they root out potential fraud threats. However, going solely off a single data point can’t and shouldn’t be a deciding factor.
With data diversity from several authoritative sources, lenders can build an accurate picture of legitimate customers before taking them through the credit underwriting process and origination. Tech that can look across multiple data sets is critical for fraud deference and revenue growth.
2. Cross-industry consortia data
Fraud, especially synthetic identity fraud (SIF), moves between industries indiscriminately, making fighting fraud effectively a group effort. Aite-Novarica Group projects losses due to SIF to reach more than $4.1 billion this year, which means fighting this threat needs to be a top priority.
This is where data shared between an extensive network of cross-industry parties becomes critical/imperative. A consortium fraud network enables different institutions to benefit from fraud data and learnings elsewhere in the ecosystem, securing the whole network more effectively. Utilizing consortia data amplifies real-time fraud intelligence between companies in the network anonymously, giving lenders insight into fraud threats trending in other industries.
3. Combining AI-powered tech with human experts
Machine learning is a powerful tool that can sort through massive amounts of information at a scale that humans physically can’t. However, regulators have taken notice of the growing use of AI and its lack of visibility and transparency into decisioning. So, if lenders can’t demonstrate to an auditor or regulators how an outcome occurred, they put their compliance department in a real risk situation.
Data must be sourced and explained, as a critical requirement for ongoing regulatory compliance, and justify decisions to customers. Using machine learning within the lending space requires a balanced approach that leverages rules-based systems combined with human intelligence.
With smarter identity verification with layered attributes, business can gain a holistic view of a consumer’s identity with speed and ease. The more trust a financial service provider has in a consumer’s identity, the more seamless the customer onboarding and origination process will flow—automating decisioning and accelerating approvals.
Listen to the entire podcast for more insights.