Signature Verification

The major advantage over other signature verification solutions on the market today is that our technology is able to compare a signature against a profile which is self-learning over time. Only this approach guarantees appropriate results for signature verification and authentication, respectively, because it is human nature never to sign twice in exactly the same way, and also to alter the signature constantly over a life-time.

A comparison with only one sample signature is mathematically much easier to handle, and thus some companies offer essentially inaccurate solutions. These “low level” solutions merely pick one random signature as a basis for the comparison. This approach only works for people who always sign in exactly the same way. Most human beings do not behave like that, and thus this approach is simply not feasible for a broader usage of that technology. Anyone can easily prove this by asking 10 random people to sign 6 times in a row, on a blank sheet of paper, in order to see how different most of these signatures are.

A more sophisticated but still not satisfactory approach is to build a solution which takes several signatures – a profile – into consideration at the time of real-time comparison, but still using a static profile which is not self-learning. This will deliver, at the start, better results than a comparison with just one signature, but the comparison will get less and less accurate over time. This will happen for nearly 100% of human subjects.

To summarize: only the xyzmo SIGNificant approach – based on self-learning profiles – really works in the long run with sufficient accuracy. The SIGNificant Biometric Server takes the ability of self-learning profiles one step further, by using a sophisticated algorithm when building a signature profile, initially by recognizing if such a profile is of sufficient quality or not. In particular, when people sign for the first time on a signature pad they change their signing behavior a little bit, and adjust it after they get used to this new technology, or new way of signing on a signature tablet, until it becomes the “usual” way. Thus it is business-critical to take more than 2 or 3 samples for each profile and, on top of that, to check by intelligent algorithms if these profiles are a robust base for the later verification. It is much better to reject improper signatures out of a profile at this stage, namely, at the time of creating such a profile, and ask a customer to sign one additional time, instead of generating a wrong profile and “try” to use this for later comparison.

financial_crime_prevention_workflowxyzmo is happy to announce a new partnership with SQN Banking Systems for bank fraud detection.

SQN Banking Systems fraud detection software products are a critical step towards overcoming the growing problem of payments for debits and credits including checks, deposits, ACH, mobile, and wire transactions for financial institutions worldwide. Using sophisticated image and transaction analysis algorithms, we help our clients effectively prevent fraud. Our products include a workflow application, automated signature verification, check image analysis, real-time fraud analysis, transaction fraud analysis, bank check verification, cloud applications and safe deposit management.

SigCheck™ reduces fraud losses by automatically searching on-us check images, extracting applicable signatures and comparing the current signature to those in a verified database. Signature™ instantly displays signatures or photo IDs electronically, making the verification process fast, seamless and more effective.

 

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