1. S. Goonatilake, P. Treleaven: I. S. for Finance and Business 10 years ago substantial increase in I.s. Killer applications - breakthrough Visa, 6 G trans. ann., 550G$, security; American Express, 15$ > 1.4$ book = a list of case real-life examples typical: lots of data, new AI and HW cap. quality improvement, lower costs,

2. I.s. in American Express, Visa Authorizer’s Assistant - an expert system before: simple rigid rules, majority left to human supervisors, many people with different performance now: an expert / intelligent system with many rules, copies expert supervisors, faster, cheaper, more equilibrated 15% > 1.4$ per one transaction (Visa - an neural network)

3. I.s. in American Express, Visa Before a centralized application on a mainframe, then lots of preprocessing on PCs and only some centralized Before every transaction (except simplest) examined by humans, then only complicated ones Before much slower (humans are slow, are not working at night …), then faster (most by computers only 24 hours a day) - fraud causes loses of money, faster also costs less, users more satisfied, also safer

4. I.s. in American Express, Visa The key question – trust – can IS be trusted, obviously good enough (actually as good as average humans) Intelligent systems enabled organizational changes in terms of HW, SW and humans Less employed, more work done by computers Work done better and faster, more profits, cheaper transactions

5. Techniques, Properties Neural networks Genetic algorithms SVM Fuzzy logic Expert systems Hybrid systems ML, Data mining, Text mining Ontologies, Semantic WEB Intelligent agents Learning Flexibility Adaptation Explanation Discovery