Identity analytics Solution

Fraud Detection

Fraud is rampant. The tactics are evolving. The financial cost to companies and governments (and ultimately tax payers) are staggering. Fraud could even harm national security. The challenge is how to detect ever increasing and morphing fraud incidents effectively.

Request a Demo

Photo illustration of a person typing on a laptop with a screen that is revealing an issue and a lock icon.

photo illustration of connected people nodes

AI 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 the 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 to inflict harm on others.

On both sides, organizations must not be content with using yesteryear’s solutions but explore the most effective technologies available today. NetOwl’s AI-based identity analytics can help.

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 same information to avoid being caught. To protect against them effectively, a fraud detection system must perform intelligent identity matching with both high recall (few or no missing matches) and high precision (few or no extraneous matches). NetOwl, the winner of the MITRE Multicultural Name Matching Challenge, offers best-of-breed identity matching through its innovative machine learning-based approach.

photo illustration of person flagged as a bad actor

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 attributes that are common, such as addresses, companies, and phone numbers among others.