all symbols with equal accuracy. It is possible to implement new neural-network based OCR systems into standard large optical systems. Those older systems, used for automated processing of forms and documents, are limited to reading typed block letters. When added to these systems, neural networks improve accuracy of reading not only typed letters but also handwritten characters. Along with automated form processing, neural networks will analyze signatures for possible forgeries. Conclusion Neural networks are still considered emerging technology and have a long way to go toward achieving their goals. This is certainly true for financial transaction security. But with the current capabilities, neural networks can certainly assist humans in complex tasks where large amounts of data need to be analyzed. For visual recognition of individual customers, neural networks are still in the simple pattern matching stages and will need more development before commercially acceptable products are available. Speech recognition, on the other hand, is already a huge industry with customers ranging from individual computer users to international telephone companies. For security, voice recognition could be an added link to the chain of pre-established systems. For example, automated account inquiry, by telephone, is a popular method for customers to determine the status of existing accounts. With voice identification of customers, an option could be added for a customer to request account transactions and payments to other institutions. For credit card fraud detection, banks have relied on computers to identify suspicious transactions. In fraud detection, these programs look for sudden changes in spending patterns such as large cash withdrawals or erratic spending. The drawback to t...
