Compliance through Automation: Expert Systems – Part V

To incorporate human expert knowledge into a technology-based expert system, the right individuals must be identified and selected. Specialists tend to be trained in rather narrow domains and are best at solving problems within their defined domains. Assuming experts do exist and are willing to participate; good experts are those who are able to solve particular types of problem scenarios that most others cannot solve with the same efficiency and/or effectiveness. Additionally, considerable time can be saved in developing an expert compliance system if the knowledge engineer has experience in the area being modeled.

“View Part I of the Compliance through Automation: Expert Systems series here“

Compliance through Automation: Expert Systems – Part IV

Several methods exist for a knowledge engineer to obtain knowledge. One option is to go through textbooks and professional journals with the intent to extract definitions, axioms, and rules that apply to the issue. This type of knowledge acquisition is especially useful for teaching and reference situations because question-response paths are direct. However, how the question is posed to the expert system can lead to misleading results. Another method of acquiring knowledge is to ask human experts to explain their thought process and method for solving problem scenarios, sometimes referred to as verbal protocol analysis. Lastly, a human expert can enhance the information obtained from literary resources and often bring unpublished knowledge, gained through experience, to the decision process paths. As a result, this combinational knowledge makes human-based expert systems a valuable technology.

“View Part I of the Compliance through Automation: Expert Systems series here“

Compliance through Automation: Expert Systems – Part III

Expert system development is usually a four step process. It starts with the knowledge engineer obtaining an understanding of a particular judgment issue. It is followed by the acquisition of thought processes of experts in solving the issue. Next, a computer model is programmed to reproduce the adopted thought processes of defined situations; if a shell program is unavailable. Lastly, the system is tested and certified to ensure appropriate resulting decisions and usability. These steps are commonly known as: knowledge representation, knowledge acquisition, computational modeling, and model validation.

“View Part I of the Compliance through Automation: Expert Systems series here“

Compliance through Automation: Expert Systems – Part II

IT usually pervades all organizational formations pursuing effective and efficient processing in response to compliance requirements, thus facilitating better decision-making through various information delivery mechanisms and offering opportunities for business model development that may lead to value creation as well as competitive advantages. To construct an expert compliance system, a knowledge engineer, performing a function similar to a system or business analyst, is typically needed. A designated knowledge engineer is responsible for defining issues in manageable terms, soliciting the knowledge, skills and abilities of experts, and translating these talents into electronically encoded formats.

“View Part I of the Compliance through Automation: Expert Systems series here“

Compliance through Automation: Expert Systems – Part I

Technology is an ever changing tool driven by compliance requirements as well as entity-centric needs to satisfy market demands. For compliance requirements, IT deployments tend to be reactionary rather than a continuous, proactive process. Consequently, IT compliance efforts are typically lacking constancy and conformity. To combat this tendency, IT planners should focus design and transition efforts on three time frames for meeting entity needs: the current state, the near-term state, and the long-term state of compliance requirements. Within this context, expert systems can be an invaluable tool to implement mandates that satisfy immediate needs and simultaneously position the entity to effectively meet the next potential compliance issue.

Compliance through Automation: Decision Support Systems – Part VIII

Decision-making is the process of evaluating alternatives and choosing from among them. Information may drive leadership; however, data accuracy and completeness are prerequisites to ensuring appropriate decisions are made. A DSS commonly assists middle-level and upper-level managers in long-term, non-routine, and often unstructured decision-making. Typically, the deployed system contains a least one decision model, is usually interactive, dedicated, and time-shared; but need not be real-time. Thus, a DSS should be viewed as an aid in decision-making rather than simply the automation of decision processes. Managers should concentrate upon making professional compliance decisions based on a DSS that reduces the risk of inappropriate responses to the entity’s environment.

“View Part I of the Compliance through Automation: Decision Support Systems series here“

Compliance through Automation: Decision Support Systems – Part VII

Information asset value is continuously increasing in this information age due to integration into decision-making processes. Compliance decisions are of high visibility, often offer immediate results, tend to be goal focused, and are directive. Although there are various techniques that can be applied to compliance decision types, the final disposition is judgmental. Normally, IT processes can be adapted to support judgmental decisions through the utilization of engineered processes. In order to ensure robustness for the intended application, DSS models must pass three tests: relevance, accuracy, and aggregation. Relevance is measured by the alignment of the condition to the problem. Yet, depending on the decision being made, accuracy can vary. While, aggregation permits grouping of a number of individual quantities into a larger quantity.

“View Part I of the Compliance through Automation: Decision Support Systems series here“

Compliance through Automation: Decision Support Systems – Part VI

DSS models are abstractions that operate as substitutes for actual circumstances under evaluation. A model-driven DSS emphasizes access to and manipulation of statistical, financial, optimization, or simulation archetypes. Consequently, a model-driven DSS utilizes datum and parameters provided by users to assist decision makers in analyzing a situation; however, they are not necessarily data-intensive. Compliance DSS model construction includes: making a large number of assumptions about the nature of the environment in which the entity’s programs, systems, processes, activities and/or tasks operate; the operating characteristics of components; and about the way animate and/or inanimate objects are likely to behave. Managerial application is established through knowing when the model matches the set of objectives and attributes requiring analytical consideration.

“View Part I of the Compliance through Automation: Decision Support Systems series here“

Compliance through Automation: Decision Support Systems – Part V

There are many types of detail variables that may be associated with a mathematical model. Binary variables are employed for “go” and “no-go” decisions. Furthermore, discrete variables are utilized for any of a finite number of values. Questions of “which” and “when” are represented as specific discrete values. Such datum need not be continuous, however, continuous variables present an infinite number of possible values, and all the values will lie within a specific range. Among the other characteristics of variables, they can be random variables that model uncertainty and are expressed as probabilities. They can also be exogenous variables, ones that are external to the model and cannot be influenced by decision makers.

“View Part I of the Compliance through Automation: Decision Support Systems series here“

Compliance through Automation: Decision Support Systems – Part IV

At a minimum, compliance decision support systems should include word processing, database, spreadsheet, and modeling capabilities. Of these capabilities, modeling is crucial to reducing response uncertainty regarding circumstances that require a compliance decision. Rudimentarily, a model is comprised of variables and objectives; where the structure must reflect the purpose for construction. The variables in a quantitative model constitute a mathematical description of the relation between elements that can be classified as: decision, intermediate, or output variables. Decision variables are controlled by the decision maker and vary in accordance with the alternative selected. Whereas, intermediate variables link decisions to outcomes; thus functioning as consolidation variables. Lastly, output variables measure decision performance, and are referred to as ‘attributes’.

“View Part I of the Compliance through Automation: Decision Support Systems series here“