Short-term Liquidity Inflow Forecast Based on Big Data Technologies

Student: Ryazanov Igor

Educational Programme: Business Informatics (Bachelor)

Year of Graduation: 2018

The paper is devoted to the problem of predicting bank liquidity inflows using Big Data and Machine Learning techniques. Predictive analysis is extremely important part of handling liquidity risk and these technologies are to improve its quality. Main goal of the study is to create model that predicts cash inflows from corporate loans in short-term stress scenarios. The paper reviews Big Data, Machine Learning and Artificial Intelligence applications in contemporary banking sphere, describes tools and methods that are relevant to solving task of predicting liquidity. Practical part of the work describes main steps of the development of part of the model responsible for predicting corporate clients paying in advance: data selection and preparation, training and model evaluation. The model showed good quality of predictions and is expected to be used in liquidity flows forecast.

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