Trust as a neural network

Introduction. Trust can be considered as rational generalization of past experience, probabilities, and various contextual signals, which makes it possible to determine the similarity between the mechanisms of trust formation and the algorithms of neural network operation and training, which include similar mechanisms of input data processing, adaptability through learning, neuroplasticity, weighting of trust ratios, and interpretation of final decisions. Problem Statement. Trust is an important factor in economic interaction, as it helps to expedite decision-making processes in the face of uncertainty about the business qualities of financial counterparties. However, the nature of trust remains a complex and multidimensional phenomenon that includes cognitive, social, and emotional factors. Modeling the mechanisms of trust emergence based on a neural network algorithms should help to improve government approaches to building trust in the financial and banking system. Purpose. To lay the theoretical foundations for creating an innovative basis for modeling the mechanisms of trust formation in the banking system based on neural network algorithms. Methods. In the course of the study, the abstract-logical method and methods of analysis, synthesis, grouping, description, comparison and theoretical generalization were used. Results. On the basis of the analysis, a formula for determining the degree of trust as a weighing of information from various sources of information is proposed, taking into account the weight factor of trust in each source of information. It is determined that the application of the concept of trust as a neural network will increase the effectiveness of mechanisms for building trust at the level of institutions and the state. Conclusions. The study proves that the use of the concept of trust as a neural network allows creating an innovative basis for modeling dynamic and socially adaptive algorithms for the formation of trust relations in the banking system, for example, to determine the creditworthiness of borrowers, predict the probability of defaults, and build anti-fraud systems based on historical interaction data. Further empirical research could be devoted to determining the “activation thresholds” of trust, namely the level of trust at which a person gets a “state of trust” and the value below which distrust, or a cautious approach arises.