Someday, VCRs, home security systems, CD players, and word processors will simply be activated by voice. Touch screen and voice editing will replace the word processors of today while bringing spreadsheets and data bases to a level of usability pleasing to most everyone. But for now, neural networks are simply entering the marketplace in niches where their statistical accuracy is valuable as they await what will surely come. Many of these niches indeed involve applications where answers are nebulous. Loan approval is one.
Financial institutions make more money by having the lowest bad loan rate they can achieve.
Indeed, some banks have proven that the failure rate on loans approved by neural networks is lower than those approved by some of their best traditional methods. Also, some credit card companies are using neural networks in their application screening process. This newest method of seeking the future by analyzing past experiences has generated its own unique problems. One of those problems is to provide a reason behind the computer-generated answer, say as to why a particular loan application was denied. To explain how a network learned and why it recommends a particular decision has been difficult.