AUTOMATING DECISION-MAKING PROCESSES IN BANKING FINANCIAL INFORMATION SYSTEMS USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Аннотация

This paper analyzes the opportunities and advantages of automating decision-making processes in banking financial information systems using Artificial Intelligence and Machine Learning. AI and ML technologies have been successfully applied in banking for risk management, credit scoring, fraud detection, and optimizing investment strategies. By automating decision-making with AI/ML, banks can achieve faster and more reliable results, while reducing operational costs. The paper also explores system architecture, integration, and the impact of these technologies on security concerns. Keywords: artificial intelligence, machine learning, decision-making, automation, banking information system, credit scoring, fraud detection, investment strategy, risk management, security Login Financial information systems today's of banking activities per day to the center turned from lending pull investment up to the management was decision acceptance to do processes done increases. Digital transformation, transaction size sharp growth and customer requirements diversity - to banks decision acceptance to do processes automation mandatory Artificial intellect and car study technologies to banks not only real - time in mode information analysis to do, maybe predictive from models using risk reduce and service quality improve opportunity This is giving. article automation theoretical the basics of banking decision acceptance to do in the process solutions integration to do methods and also, this of the process economic and security aspects analysis does. Financial information systems of banking operations heart They are the customers' account -books management, credit and deposit operations management, money flow control to do such as directly financial security and stability providing functions does. With this together, digital transformation under the circumstances transactions number and their complexity noticeable at the level increased. In such circumstances decision acceptance to do processes automation to banks following priority advantages gives: Operation efficiency – human factor reduction and processes acceleration through service show speed increase Accuracy and consistency – standardized algorithmic approach with decision quality stable at the level hold to stand 135 ACADEMIC RESEARCH IN MODERN SCIENCE International scientific-online conference Risks management – big in size real time data in mode analysis to do through credit and cyber -threat risks in advance to determine. To regulators compliance – decision acceptance to do processes automated protocols based on conducted due to audit and accountable requirements easy to do. this purpose, banks financial information to systems artificial intelligence and car learning components integration to do today's both practical and theoretical at the level current research to the topic has become. Home part 1. Artificial intellect and car study Theory: Artificial to intelligent banking systems in advance unwritten rules based on study, decision acceptance to do and independent conclusion release ability gives. The machine study and enlarged transaction information on forecast creative models basis organization With the help of supervised learning credit scoring and customer scores clear designation possible, unsupervised approaches through and anomalous transactions fraud detection and customer segmentation done increase It gets easier. Deep deep learning networks and complicated time rows and graphic structured network information analysis to do possible. In a banking environment this approaches together used, decided quality noticeable at the level increases. Artificial the term intelligence computer to systems human to the mind typical analysis, decision acceptance to do and study ability provider technologies means. The machine learning is the study of SI concepts one syllable from the data independent conclusion release and forecasting done increases. -In the field of financial decision making the following MO approaches wide used: - Supervised Supervised Learning: in advance designated using a sample (label) the model to teach method. Credit used in scoring (logistic regression, random forest), fraud detection ( gradient boosting, support vector machine) . Model evaluation for such as accuracy, ROC - AUC, F1score indicators is used. - Uncontrolled learning (Unsupervised Learning ): in the information hidden discovers structures. Clustering (K means, DBSCAN) credit portfolio segmentation, anomalous detection (isolation forest, autoencoder) fraud in determining service does. - Deep Deep Learning: one how many neuron layers using complicated patterns learns. LSTM/ RNN time rows through market prices forecasting, GNN (Graph Neural Networks ) between banks financial networks in analysis, convolutional networks and documents automatic again at work is used. 136 ACADEMIC RESEARCH IN MODERN SCIENCE International scientific-online conference - Reinforcement Reinforcement Lear