Identity analytics Solution
Fraud Detection
Fraud is proliferating, and the tactics are becoming increasingly sophisticated. The financial impact on companies and governments—and ultimately taxpayers—is immense. When fraud is used to finance criminal networks, it can even threaten national security.
NetOwl delivers highly accurate, fast, and scalable entity resolution to help uncover obfuscated identities, expose non-obvious links among fraudsters, and reveal the criminal networks operating beneath the surface.
Featured Product
Entity Resolution Against Fraud
Fraud detection is an important concern for both commercial and public sector organizations. On the commercial side, financial organizations such as banks and insurers strive to avoid the financial losses and potential negative publicity associated with fraud. On the public side, government agencies must ensure that public services and programs (e.g., healthcare, immigration, social security) are not abused for personal gain or, worse, with the intent of inflicting harm on others.
NetOwl’s entity resolution, which leverages its machine learning-based fuzzy name matching, helps companies and government agencies match records against blacklists and detect previously unknown fraudster networks among large volumes of records.
Finding Bad Actors
Whether it is in banking, insurance, health care, or government programs, organizations must screen potential customers or applicants against blacklists of bad actors. These blacklists may include, for instance, former clients or vendors who have committed fraud or abuse.
Fraudsters are clever by using similar but not the exact same names and other personal information (e.g., DoB, address) to avoid being caught. To protect against them effectively, a fraud detection system must perform intelligent entity resolution with both high recall (few missing matches) and high precision (few extraneous matches). NetOwl, the winner of the MITRE Multicultural Name Matching Challenge, offers best-of-breed entity resolution through its innovative machine learning-based approach.
Detecting Non-obvious Connections
Many fraud schemes involve individuals that look benign by themselves but are actually connected in not so obvious ways. For instance, multiple similar insurance claims for services rendered by the same provider, asylum applications tied to the same phone number, or multiple rebate requests from essentially the same address.
NetOwl can reveal similarities among records beyond person names by discovering hidden connections through other common attributes such as addresses, companies, and phone numbers.
Featured Blog Posts
How Name Matching Helps Protect Against Credit Card Fraud
As e-commerce continues to grow, so does credit card fraud. Fuzzy Name Matching provides a secure layer of protection.
How Name Matching is Crucial for Automated Identity Verification
In today’s digital world, there is an increasing need for automated Identity Verification to prevent fraud.
Name Matching Helps Remittance Companies Curtail Fraud
A global economy with great ethnic and linguistic diversity poses a difficult challenge for remittance businesses as they must verify…
Fuzzy Name Matching Helps to Verify E-Signatures
E-signatures are effective in helping to ensure the authenticity of documents, but they need to be supplemented by Fuzzy Name…
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