“Layer Up” to Prevent Loan Losses
Fraudsters go from lender to lender, exploiting even the smallest gaps in controls. But unfortunately, there’s no single point of entry. It could be a weak underwriter, a poor income validation process, a rogue broker, or poor identity checks. Any of these can result in budget-blowing loan losses, dreadful customer experiences, and reputational risk. This is true across the spectrum of mortgage, auto, and home equity loans and credit cards.
With so many opportunities for fraud, how can lenders reduce the risk of loan losses? The same way a robust water filtration system removes contaminants from drinking water: With multiple membranes or layers. In a filtration system, these layers work independently, yet together, to target various undesirable elements to provide clean water for the consumer.
Similarly, lenders have learned that a layered risk management approach is key to managing and removing the risk of loan losses due to fraud.
The Evolution of Fraud Prevention Layers
Historically, there was only one layer: Lenders relied on people and reports for their primary risk controls. It worked fairly well…until it didn’t.
Then came more automated tools, such as AI, big data, and machine learning. Well received by chief risk officers, auditors and regulators, these solutions have exploded in the lending space. They are game-changers. Also, they offer risk protection in multiple layers: People, Processes and Systems. Finally, they empower lenders to better identify and control losses while, in many cases, improving efficiencies.
Automated fraud detection is now an expected and necessary best practice. But is it enough? Is there a single tool to eliminate all the risk and fraud for all products at every phase of the lending process?
A One-and-Done Solution for Loan Losses?
Despite what some will tell you, a single layer of automation can’t effectively target the many different types of risk. Fraudsters are creative and their tactics morph quickly. Lenders need different tools and solutions to stay ahead of them.
For example, all lenders must answer the basic question: “Is the applicant truly who they say they are?”
The rise in identity theft generated new regulatory requirements. So most lenders appropriately implement specific controls that focus on identity validation. However, a recent auto industry report revealed that identity theft accounts for only about 20% of loan loss risk. Income and employment misrepresentation are a much larger risk, accounting for 60% of loan losses! Therefore, lenders may need another targeted layer to address this risk segment.
Here’s another potential threat: Is the intermediary in the transaction (e.g., broker, dealer, etc.) a trusted source? The more parties involved in a transaction, the more opportunity for fraud. Here again, lenders should consider another layer of protection that identifies the risk associated with each entity involved.
These are just two examples. The point is…lenders that implement layered risk controls can greatly reduce fraud and improve their odds of loan repayment.
Collective Benefits (Our Proprietary Data Repository Approach)
Each lender’s data eventually identifies a list of bad actors or fraudulent elements that have targeted their individual organizations. This provides one layer of protection. However, these types of negative lists become really powerful when a collection of lenders share their data. Point Predictive’s proprietary data repository has proven that collaboration provides a significant lift in fraud detection.
For example, you could build personal experiences with new brokers or dealers and perhaps find out only after many loan losses that they are bad. Or you could take the data repository approach, which provides insight into their performance before you make your first decision. Similarly, you could wait until your own data identifies the fraudulent employers behind higher early payment default rates. Or you could use a proprietary data repository approach to root out false employers as a powerful fraud risk management layer.
Loan Loss Prevention Requires Continuous Maintenance
Water filtration systems require ongoing maintenance and alertness. Over time, some filters lose their effectiveness and need to be replaced. Additional harmful elements may become present in the water and require new layers of protection.
The same is true in the fraud risk world. To avoid loan losses, lenders must continuously scrutinize their current systems to ensure they are effectively mitigating risk. Some well-performing tools may gradually, and indiscernibly, become less effective as new fraud schemes evolve. Additionally, new emerging tools should continue to be evaluated in light of the ever-changing risk landscape.
Minimizing loan losses when the economy is expanding and robust can be a challenge. However, without proper risk management layers in place, lenders will find it much more difficult to manage loan losses when economic times turn softer. Risk Officers, auditors, and regulators widely support a layered risk management approach to minimizing fraud risk.
Point Predictive’s Artificial + Natural Intelligence™ approach to fraud detection enlists multiple layers of automation and machine learning across a growing group of lenders, along with the invaluable layer of fraud experts who monitor alerts and investigate patterns.
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