A Hippocampal-inspired Self-Organising Learning Memory Model with Analogical Reasoning for Decision Support

Decision-making is innately human-centered; and a decision support system seeks to provide a systematic and consistent way to information processing by integrating the domain knowledge with a rational reasoning capability to support the human decision process. Traditionally, decision support systems are based on data-mining solutions, statistical models and conventional AI techniques. These systems have several deficiencies such as lacking in ability to explain the computed decisions (black-box nature) and are not dynamically adaptive to handle the emergence of new information. This paper presents a brain-inspired learning memory model with analogical reasoning as a tool to facilitate human decisionmaking. The proposed model is named GenSoFNN-AR and constitutes a neurocognitive approach to the science of knowledge discovery to support the human decision process. The GenSoFNN-AR model is subsequently evaluated with a bank failure classification and analysis problem using a set of historical financial records. The results are encouraging.