SQN Banking Systems introduces Linux versions of fraud products

Source: SQN Banking Systems

Rancocas, N.J.-based SQN Banking Systems, a leading provider of integrated fraud detection and process improvement products for the financial industry, has responded to client demand for open source enabled solutions by releasing Linux versions of its Sentry: SigCheck and Sentry: Inspect products.

Open source systems are easier to customize, more secure and offer a lower cost of ownership to users. Banks and credit unions using open source operating systems and database management systems can now use SQN's solutions to detect suspected forged and counterfeit checks. SQN's products will run on traditional server platforms such as Microsoft Server 2008 and Microsoft SQL Server 2008 as well as open source platforms Debian Linux and PostGres SQL.

"As more of our customers are installing open source operating systems and database management systems in their IT departments, we decided to write Linux versions of our flagship products," said Joe Uhland, president of SQN. "By listening to our clients' needs, we are able to continue to provide them with the most advanced and flexible fraud detection solutions."

SENTRY: SigCheck compares images of signatures electronically to detect suspected forgeries by searching "on-us" check images, extracting signatures and comparing them to reference signatures from signature databases (such as SQN's SENTRY: SignatureTM). SigCheck determines how well they match, spotting potential check fraud automatically, and allows bank personnel to review them further before making payment decisions.

SENTRY: Inspect software automatically reviews targeted on-us check images and compares them to a data template in the customer's profile. Inspect then flags items with discrepancies for further review. By reading the legal amount and payee line information, Inspect complements existing image based positive pay systems. Check fraud schemes are identified faster and productivity is significantly improved over manual or semi-automated research methods.

 

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