How much has lending fraud has cost you in the last fiscal year? According to the Corelogic 2019 Mortgage Fraud Report, during Q2 2019, 1 in 123 mortgage applications contained fraudulent information. That’s better than Q2 2018, when 1 in 109 applications was fraudulent. Meanwhile, auto lending fraud has continued to rise. According to PointPredicitve, in the last seven years, auto lending fraud has risen by at least 38%. Fraud origination risk is expected to reach $7 billion in 2019.
Regardless of the ratios, the volume and size of fraudulent loans issued by lenders are the drivers of actual losses. The ease of committing fraud using online resources has made banking fraud detection necessary for loan origination.
Frequently-Encountered Fraud Types
Between individuals who have poor financial standing and criminal cartels, there’s no limit to the variations in fraudulent loan applications. However, the most frequently encountered types of fraud include:
- Identity: Uses stolen identity or synthetic identity composed of seemingly-legitimate personal information and credit data.
- Straw borrower: An applicant with poor credit convinces a friend or relative with better credit to front the deal for the purpose of buying the vehicle. Also used by fraud rings to acquire a vehicle to be sold in a foreign market.
- Income: Intentionally misrepresent the existence, continuance, source, or amount of income. False paystubs are easily created online for a small fee.
- Employment: Uses fee-based online services to confirm false employment by phone or letter.
- Undisclosed debt: Failure to disclose all current real estate debt or past foreclosures.
- Property value: Information is intentionally misrepresented with the goal of improving terms of the deal.
- Collateral inflation: Auto dealers claim a higher value for a used vehicle than the actual sales price to boost profits.
Banking Fraud Detection Requires the Latest Fintech Innovations
Banks can counter any damage done by fraudulent loan applications by using the latest fintech innovations:
- Secure, on-demand access to consumer data for better lending decisions.
- Machine learning algorithms for automated banking fraud detection.
- Cloud-based verification services to confirm or refute loan applicant claims.
You need the latest machine learning capabilities to identify fraud. Learn how in PointPredictive’s Hidden Patterns of Auto Lending Fraud webinar.
Consumer Data Provides a More Accurate Assessment of Ability to Pay
Fostered by the internet, ever-growing volumes of consumer data are available to banks to help better assess the financial standing of loan applicants. Automated calls to alternative credit data sources can be used along with traditional credit scores to provide a more detailed and current assessment of applicants’ ability to pay. Rental, utility, cell phone payment records, payday loans, frequent changes of address, and discrepancies between educational records and employment can correlate with higher risk and call for a thorough vetting of applicant attributes.
Machine Learning Uncovers Multiple Fraud Schemes
Analysis of tens of millions of historical auto loan applications reveals subtle as well as obvious schemes for misrepresenting information on loan applications. Machine learning algorithms applied to the thousands of loan applications are one of the most innovative means of banking fraud detection. The automated process can help detect false and synthetic identities, straw buyers, income and employment misrepresentation, and collateral inflation far more efficiently than could ever be accomplished via review by even the most experienced of underwriters.
The Convenience of Verification Services
When machine learning algorithms detect potential fraud, automated calls to income, employment, and vehicle valuation services can help confirm or refute the suspected fraud. Confirm eliminates these problematic applications. Refutation improves lending decision confidence in structuring the appropriate lending terms.
Fintech Innovations Address a Wide Range of Fraud Schemes
The chart below isn’t comprehensive, but it should give you an idea of which types of technology can counter which types of fraud.
|Type of Fraud||Alternative Credit Data||Machine Learning Algorithms||Verification Services|
The Vital Role of Automation in Banking Fraud Detection
Each of the fintech capabilities and services described above uses automation to make banking fraud detection as efficient and effective as possible. The cloud-based services can be automatically invoked by a modern loan origination system as part of the underwriting process to immediately call attention to suspect loan applications. Automated comparisons of applicant financial attributes and verification of application information can either classify the applicant as legitimate or highly-suspect of attempted fraud.
Proactively Get Ahead of Fraud
Although fraud statistics may show conflicting trends, it’s clear that bad actors will continue to use technology to their benefit. How much have fraudulent loan applications cost you? If you’re not using the latest fintech capabilities and innovations to fight back, expect a rise in losses from fraud. Proactive deployment of technologies for banking fraud detection will have immediate and long-term benefits for your loan portfolios.
defi SOLUTIONS provides configurable loan origination systems, loan management and servicing, analytics and reporting, and a wide range of technology-enabled BPO services. If your current solutions for fighting fraud aren’t getting the job done, take a look at modern technology. Contact our team today or register for a demo.
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