Development of an Analytical Solution to Support the Credit Process in a Bank Based on a BI Platform Using Machine Learning Methods

In the modern banking sector, characterized by a high level of competition, big data analytics is becoming not only a key element of strategic planning, but also an important tool for optimizing individual processes and making specific decisions. The use of business intelligence and machine learning methods allows financial organizations to adapt to changes in market conditions, anticipating trends in demand for credit products. Thus, taken together, these instruments are able to provide significant support to the lending process that is fundamental for banks. At the same time, companies that still do not use these technologies risk losing out to more progressive competitors, missing out on existing business development opportunities. The purpose of this study is to develop an analytical solution to support the lending process in the bank, thanks to which it will be possible to analyze not only key business metrics, but also projected loan issuance volumes, which in turn can help managers make more informed management decisions, effectively allocate available resources and carry out strategic planning. The thesis is divided into three main sections. The first chapter is devoted to a comprehensive review of modern methods and tools of analytics. The second chapter is aimed at describing and analyzing the bank's activities in the context of the process under consideration, and also includes the formulation of key business metrics and requirements for the system being created. The final part of the diploma focuses on the practical implementation of an analytical solution: from the preparation of data and the construction of forecasting models, to the visualization of results in interactive reporting and recommendations for further development of the project.