Personal loan fraud is an ever-increasing challenge for large companies.
Our model enables our clients to reduce fraud by 80% using an efficient scoring tool to minimize the impact of false positives. The longer it runs on your real-time and historical data, the more accurate the rules become to reduce fraud over time.

Auto loan fraud is an industry-wide problem. While most application fraud targets identity fraud, our solution uses data from our auto lender partners to predict and find all types of fraud in a single, integrated fraud score.

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Our model identifies fraud in a single, integrated score, unlike other applications, by finding discrepancies from loan applications previously submitted to other auto dealerships.
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The alerts sent allow lenders to prioritize the highest-risk transactions for approval to avoid:

*False identities and income
*Straw man borrowers
*Merchants
*Collateral fraud

Our data scientists use machine learning techniques combined with unique retail data to minimize application risk and ensure a great customer experience at the point of sale.
We have developed a highly predictive analytics model that helps retailers identify high-risk applicants who are most likely to default due to application fraud.

SME fraud loans are dramatically increasing. The challenge is to decide with the complete process information: limit the fraud exposure and restrain the impact on customer friction.

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Give your team a robust fraud detection system to make more informed decisions. Data enrichment and accurate fraud scoring mean every decision is based on facts, not assumptions. Integrate more legitimate users to gain traction and grow your business with peace of mind.

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