Decision Support Systems (DSS) And Expert Systems

Decision support systems (DSS) and expert systems (ES) play critical role in solving various financial and business problems, where data processing for deriving new information, yielding possible solutions or their alternatives is a significant part of relevant computations. Section 3.1 gives a brief introduction to DSS and ES, discusses their goals and main differences from standard information systems (IS). Section 3.2 reviews main types and taxonomies of DSS, while relating them to financial risk oriented problems. Section 3.3 discusses recent developments in DSS for financial problems, related to credit risk, while Section 3.4 enlists a number of requirements for modern DSS dedicated to banking decisions. Further, we discuss the development of novel DSS based on AI techniques, described in

Although such systems may share similar architectural patterns and goals, they also possess some differences. While DSS definition may be more relevant for modern decision support and automation, it is reasonable to begin with the review of ES, which pioneered decision support at the beginning of artificial intelligence based analysis.
ES can be defined as an AI-driven computational system that uses a knowledge base of human expertise to aid in solving problems. Several types of expert systems can be identified according to their types – Liao (Liao, 2005) identified several types of expert systems, according to techniques, used for their development, such as rule-based systems, knowledge-based systems, neural networks, case-based reasoning, intelligent agents, modelling-based and others. According to these sources, such features of ES can be identified:
1. It uses human knowledge and expertise which is collected in knowledge base in various forms (general information, rules, formulas, models, restrictions,