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Forecasting checking account balance : Using supervised machine learning

The introduction of open banking has made it possible for companies to build the next generation of applications based on transactional data. Enabling economic forecasts which private individuals can use to make responsible financial decisions. This project investigated forecasting account balances using supervised learning. 7 different regression models were run on transactional data from 377 anonymised checking accounts split into subgroups. The results concluded that multivariate XGBoost optimised with feature selection was the best performing forecasting model and the subgroup with recurring income transactions was easiest to forecast. Based on the result from this project it can be concluded that a viable option to forecast account balances is to split the transactional data into subgroups and forecast them separately. Minimising the errors given by certain random, infrequent and large types of transactions.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-477799
Date January 2022
CreatorsDannelind, Martin
PublisherUppsala universitet, Avdelningen för systemteknik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationUPTEC IT, 1401-5749 ; 22011

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