Also known as belief networks or causal probabilistic networks, use the techniques of probability theory to reason under conditions of uncertainty. They are useful for representing uncertain relationships where prior knowledge and statistical data are available. Analysis is based on cause and effect, confidence factors, probabilities, polynomial algorithms, heuristics and related controls or concepts. Bayesian networks have been used widely in statistics, decision analysis and artificial intelligence to model expert systems, diagnosis, dynamic reasoning, control and decision making. Validation is the process of confirming the correctness of a given model or assumptions by using measurable inputs to produce definable outputs, both of which can be confirmed and verified.
Verification is the process of confirming that an implemented model works as intended.
An expert system has no hard-coded path thus can reach new or different conclusions using deductive or inductive reasoning. While the foundation of AI began hundreds of years ago with work by mathematicians like Boole and others, practical applications of AI began to appear in the seventies and eighties. Beginning in the early sixties, AI research was conducted in research labs at Stanford, SRI, MIT and Carnegie-Mellon. By 1984, a number of financial institutions began using some sort of AI programming technique or tool. Most of the initial financial applications involved decision making systems, including commercial and individual loans, credit card applicant screening and fraud pattern detection.