Core Concepts of ACCOUNTING INFORMATION SYSTEMS Moscove, Simkin & Bagranoff John Wiley & Sons, Inc. Developed by: Marianne Bradford, Ph.D. Bryant College

Chapter 14 Information and Knowledge Processing Systems in Accounting Introduction Information and Knowledge Processing Information Processing Systems in Accounting Solving Accounting Problems with Artificial Intelligence Comparing Information and Knowledge Processing Systems

Introduction Accounting systems have evolved from systems that primarily process transaction data to systems that process information and impart knowledge. Transaction processing systems are often incapable of helping managers in their planning functions. This chapter discusses different types of evolving accounting systems and provides a comparison of characteristics and problem domains.

Information and Knowledge Processing One of the earliest applications of computerized processing was transaction processing. Transaction processing involves processing data in volume, and it usually requires computers to do simple, repetitive tasks. These systems do not lend themselves to the types of analyses required for upper-level management decisions.

Information and Knowledge Processing Advances in information technology allow managers to develop complex processing systems that use information as a competitive tool. It is important to understand the various information and knowledge processing systems available to help accountants and managers in their work.

Strategic, Management Planning, and Operational Decision Making Top management makes strategic decisions, which involve long-range planning horizons and commitments of large amounts of resources. Middle management makes management planning decisions, which involve translating a company’s long-range plans into specific plans and activities. Operating management makes operational decisions, which operationalize management planning decisions into specific, meaningful tasks.

Types of Processing Systems Transaction Processing involves converting transaction data into useful information. Management Information Systems involve processing nonroutine data and information for management planning and control. Knowledge Processing is the highest level of processing and is made possible by recent advancements in technology.

Information Processing Systems in Accounting Decision Support Systems Spreadsheets Executive Information Systems Group Support Systems

Decision Support Systems Decision support systems (DSS) are information processing systems that assist in the decision-making process of professionals. Characteristics of DSSs: –Support management decision- making –Solve unstructured problems –Allow for “what-if” questions –Flexible and handle many different problems –User-friendly interface

Components of Decision Support Systems The user - usually manager One or more databases - contain routine and nonroutine data from external and internal sources A planning language - performs the communication function The model base - performs data manipulations and computations on data

Examples of DSS in Accounting A Cost Accounting System A Capital Budgeting System A Budget Variance Analysis System A General Decision Support System

Spreadsheet Programs Using spreadsheet software, a user can quickly develop a spreadsheet model to support decision-making. Examples: –Excel –Lotus 123 –Quattro Pro

Other Information Processing Systems Executive information systems (EIS) provide support for strategic decisions made by an organization’s top management. –The Internet is becoming an important source of external data in EISs. Group decision support systems are DSSs developed for use by management groups or teams.

Solving Accounting Problems with Artificial Intelligence Artificial intelligence (AI) is the branch of computer science that concerns itself with computer “thinking.” –Computers replace humans in doing certain tasks. Expert systems is the branch of AI that accomplishes decision making or problem solving and has the most application to accounting.

Expert Systems The AI software most used today in businesses for accounting applications is expert systems. Expert systems are software programs that uses facts, knowledge, and reasoning techniques to solve problems that typically require human abilities.

Characteristics of Expert Systems Make expert decisions Reason by inference Explain the reasoning process Learn Allow for uncertainty

Components of Expert Systems 1.The people who interact with an expert system - include user, expert and knowledge engineer. The knowledge engineer mines the knowledge of domain experts. 2.The domain database contains all of the facts about a particular domain or subject.

3.The knowledge database contains procedural knowledge or rules that dictate which actions to follow. 4.The inference engine “drives” the expert system by deciding when to apply rules and the order in which to apply them. 5.The user interface Components of Expert Systems

Examples of Expert Systems in Accounting Risk Assessment Systems Technical Support Systems Internal Audit Subsystems A Tax Preparation System A Tax Planning System Expert Systems at the Internal Revenue System

Benefits of Expert Systems Consistent application of rules Easy to modify by adding and deleting rules Incorporates knowledge of many experts Retention of expert knowledge when employed experts leave an organization Capable of training novices Efficient because can be used over and over by inexperienced users

Risks and Limitations of Expert Systems Legal liability of system developers Less than expert performance without validation Can be expensive to develop and maintain Consensus among experts sometimes difficult to obtain, making development also difficult Lack common sense and ability to think critically Staff members using expert systems will fail to develop expert knowledge

Neural Networks Neural networks solve problems by learning from experience, by observing patterns in data, and then by using this learning for prediction purposes. System developers train neural networks with sample data, rather than programming them.

Case-Based Reasoning Systems Case-based reasoning systems reason by analogy. Suitable for problems that require humans to search through historical data to find similar problems with successful solutions. One application of case-based reasoning systems is fraud detection.

Advantages of Case-Based Reasoning Systems Systems developers do not require expert problem understanding to build case- based reasoning systems. They have a learning capacity. They have the ability to incorporate explanations in their database to accompany solutions

Intelligent Agents Intelligent agents are artificial intelligence- based information systems that act on behalf of a user. Tasks performed by intelligent agents: –Prioritizing information –Locating and retrieving information from databases Search engines are a type of intelligent agent.

Search Engines The Internet has spurred the need for agents (search engines) to perform information retrieval and filtering. Bots are a type of intelligent agent that derives their name from the robotic branch of artificial intelligence. Spiders crawl the web in search of data. Intelligent agents have value to accountants in the form of data mining.

Comparing Information and Knowledge Processing Systems

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Chapter 14