One drawback was the experts almost never were able to put into words everything they knew. Conditions that affect loan performance, from varying interest rates and a changeable economy to sliding employment figures, are always in flux. In instances where you had dynamic or volatile environments, or very complex environments, expert systems tended to fail. At the fringes of expert systems' limitations is where neural networking comes into play. Neural networking, another form of artificial intelligence, got its biggest boost from attempts by military and university researchers to develop software mimicking the way the human mind works.
Neural networks are self-learning systems using data of past behavior to predict future behavior.
Using a neural network, a bank feeds the computer data, such as the characteristics of loan applicants' income, how many kids they have, and where they live. The computer is also given a description of the type of customers who defaulted on loans in the past. One approach to solving any problems in modeling is by overlaying sophisticated mathematical systems. Such modeling became an aspect of artificial intelligence separate from neural networks. As computer technology advances so too do opportunities to use this software in business.