{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T22:19:24Z","timestamp":1768256364655,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Artificial Intelligence is a disruptive technology that is revolutionizing the accounting sector, e.g., by reducing costs, detecting fraud, and generating reports. However, the manual maintenance of booking ledgers remains a significant challenge, particularly for small and medium-sized enterprises. The usage of AI technologies in this area is rarely considered in the literature depite a significant interest in using AI for other acounting-related activities. Our study, which was conducted during 2023\u20132024, utilizes natural language processing and machine learning to construct a predictive model that accurately matches bank transaction statements with accounting records. The study employs Feedforward Neural Networks and Support Vector Machines with various settings and compares their performance with that of previous models embedded in similar predictive tasks. Additionally, as a baseline model, a software called Contofox, a rule-based system capable of classifying accounting records by manually creating rules to match bank statements with accounting records, is used. Furthermore, this study evaluates the business value of the model through an interview with an accounting expert, highlighting the potential benefits of artifacts in enhancing accounting processes.<\/jats:p>","DOI":"10.3390\/computers14050193","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T06:59:16Z","timestamp":1747292356000},"page":"193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Accounting Support Using Artificial Intelligence for Bank Statement Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Marco","family":"Lecci","sequence":"first","affiliation":[{"name":"Department of Business, University of Applied Sciences and Arts Northwestern Switzerland, 4600 Olten, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5636-1660","authenticated-orcid":false,"given":"Thomas","family":"Hanne","sequence":"additional","affiliation":[{"name":"Department of Business, University of Applied Sciences and Arts Northwestern Switzerland, 4600 Olten, Switzerland"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"key":"ref_1","first-page":"381","article-title":"Machine learning algorithms\u2013A review","volume":"9","author":"Mahesh","year":"2020","journal-title":"Int. J. Sci. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/1742-6596\/1142\/1\/012012","article-title":"Machine learning from theory to algorithms: An overview","volume":"1142","author":"Alzubi","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1007\/s11142-020-09563-8","article-title":"Using machine learning to detect misstatements","volume":"26","author":"Bertomeu","year":"2020","journal-title":"Rev. Account. Stud."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, Z., and Zheng, L. (2018, January 28\u201330). The impact of artificial intelligence on accounting. Proceedings of the 2018 4th International Conference on Social Science and Higher Education (ICSSHE 2018), Sanya, China.","DOI":"10.2991\/icsshe-18.2018.203"},{"key":"ref_5","first-page":"126","article-title":"How artificial intelligence is challenging accounting profession","volume":"12","year":"2018","journal-title":"Econ. Bus. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Faccia, A., Al Naqbi, M.Y.K., and Lootah, S.A. (2019, January 28\u201330). Integrated cloud financial accounting cycle. How artificial intelligence, blockchain, and XBRL will change the accounting, fiscal and auditing practices. Proceedings of the 2019 3rd International Conference on Cloud and Big Data Computing, Oxford, UK.","DOI":"10.1145\/3358505.3358507"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110461","DOI":"10.1109\/ACCESS.2020.3000505","article-title":"The impact of artificial intelligence and blockchain on the accounting profession","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1007\/s11142-020-09546-9","article-title":"Machine learning improves accounting estimates: Evidence from insurance payments","volume":"25","author":"Ding","year":"2020","journal-title":"Rev. Account. Stud."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1287\/mnsc.1100.1174","article-title":"Detecting management fraud in public companies","volume":"56","author":"Cecchini","year":"2010","journal-title":"Manag. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1111\/1475-679X.12292","article-title":"Detecting accounting fraud in publicly traded U.S. firms using a machine learning approach","volume":"58","author":"Bao","year":"2020","journal-title":"J. Account. Res."},{"key":"ref_11","first-page":"1","article-title":"A back propagation artificial neural network based model for detecting and predicting fraudulent financial reporting","volume":"106","author":"Salama","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_12","first-page":"41","article-title":"Application of artificial neural network in accounting research","volume":"51","author":"Ordia","year":"2019","journal-title":"Indian J. Account."},{"key":"ref_13","unstructured":"Bunker, R.P., Zhang, W., and Naeem, M.A. (2016). Improving a credit scoring model by incorporating bank statement derived features. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Man, X., Luo, T., and Lin, J. (2019, January 6\u20139). Financial sentiment analysis (FSA): A survey. Proceedings of the IEEE International Conference on Industrial Cyber Physical Systems (ICPS), Taipei, Taiwan.","DOI":"10.1109\/ICPHYS.2019.8780312"},{"key":"ref_15","first-page":"19","article-title":"A brief survey of text mining","volume":"20","author":"Hotho","year":"2005","journal-title":"Ldv Forum"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hamisu, M., and Mansour, A. (2021, January 23\u201325). Detecting advance fee fraud using NLP bag of word model. Proceedings of the 2020 IEEE 2nd International Conference on Cyberspac (CYBER NIGERIA), Abuja, Nigeria.","DOI":"10.1109\/CYBERNIGERIA51635.2021.9428793"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Egozi, G., and Verma, R.M. (2018, January 7\u201312). Phishing email detection using robust NLP techniques. Proceedings of the IEEE International Conference on Data Mining Workshops (ICDMW), Singapore.","DOI":"10.1109\/ICDMW.2018.00009"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1016\/j.procs.2021.03.107","article-title":"Spam email detection using deep learning techniques","volume":"184","author":"AbdulNabi","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_19","unstructured":"Ojala, J. (2018). Machine learning in automating bank statement postings. [Master\u2019s Thesis, Helsinki Metropolia University of Applied Sciences]."},{"key":"ref_20","unstructured":"Wong, K. (2021). Support of Accounting Using Artificial Intelligence. [Master\u2019s Thesis, University of Applied Sciences and Arts Northwestern Switzerland]."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"137","DOI":"10.2308\/JETA-2022-073","article-title":"Support of Accounting by Bank Statement Classification Using Neural Networks","volume":"22","author":"Wong","year":"2025","journal-title":"J. Emerg. Technol. Account."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1007\/s12065-022-00795-y","article-title":"An analysis of weight initialization methods in connection with different activation functions for feedforward neural networks","volume":"17","author":"Wong","year":"2024","journal-title":"Evol. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Murthy, A., Rao, P.S., Pallavi, N.S., Kharvi, N., Neha, B.R., and Poojary, K. (2024, January 9\u201310). Optimizing Convolutional Neural Networks: A Comparative Study of Gradient-Descent, Adam, and RMSprop Optimizers for Accuracy and Loss in Apple Leaf Disease Detection. Proceedings of the 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON), Bengaluru, India.","DOI":"10.1109\/NMITCON62075.2024.10699138"},{"key":"ref_24","unstructured":"Goldman, E. (2024). Comparative Analysis of Traditional Machine Learning Models and BERT for Short Financial Text Classification, Vrije Universiteit Amsterdam. Master Internship Report."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, C., Hu, Z., Chen, W., Liu, Y., Wang, L., and Jiang, A. (2022, January 16\u201318). Research on Classification Method of Bank Statement in Low Resource and Cross-domain Scenarios. Proceedings of the 2022 Euro-Asia Conference on Frontiers of Computer Science and Information Technology (FCSIT), Beijing, China.","DOI":"10.1109\/FCSIT57414.2022.00015"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"617","DOI":"10.3390\/make2040033","article-title":"Automatic electronic invoice classification using machine learning models","volume":"2","author":"Bardelli","year":"2020","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_27","unstructured":"Bergdorf, J. (2018). Machine Learning and Rule Induction in Invoice Processing: Comparing Machine Learning Methods in their Ability to Assign Account Codes in the Bookkeeping Process. [Master\u2019s Thesis, KTH, School of Electrical Engineering and Computer Science]."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Esswein, M., Mayer, J.H., Sedneva, D., Pagels, D., and Albers, J.P. (2020). Improving Invoice Allocation in Accounting\u2014An Account Recommender Case Study Applying Machine Learning. Digital Business Transformation, Springer.","DOI":"10.1007\/978-3-030-47355-6_10"},{"key":"ref_29","unstructured":"Hedberg, N. (2020). Automated Invoice Processing with Machine Learning: Benefits, Risks and Technical Feasibility. [Master of Science Thesis, KTH, School of Industrial Engineering and Management (ITM)]. TRITA-ITM-EX 2020:326."},{"key":"ref_30","unstructured":"Holt, X., and Chisholm, A. (2018, January 10\u201312). Extracting structured data from invoices. Proceedings of the Australasian Language Technology Association Workshop 2018, Dunedin, New Zealand."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Vaishnavi, V., and Kuechler, W. (2007). Design Science Research Methods and Patterns. Innovating Information and Communication Technology, CRC Press.","DOI":"10.1201\/9781420059335"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dallachiesa, M., Ebaid, A., Eldawy, A., Elmagarmid, A., Ilyas, I.F., Ouzzani, M., and Tang, N. (2013, January 23\u201328). NADEEF: A commodity data cleaning system. Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, New York, NY, USA.","DOI":"10.1145\/2463676.2465327"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102323","DOI":"10.1016\/j.is.2023.102323","article-title":"On tuning parameters guiding similarity computations in a data deduplication pipeline for customers records: Experience from a R&D project","volume":"121","author":"Andrzejewski","year":"2024","journal-title":"Inf. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hl\u00e1dek, D., Sta\u0161, J., and Pleva, M. (2020). Survey of automatic spelling correction. Electronics, 9.","DOI":"10.3390\/electronics9101670"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1186\/s40537-024-00943-4","article-title":"Data oversampling and imbalanced datasets: An investigation of performance for machine learning and feature engineering","volume":"11","author":"Mujahid","year":"2024","journal-title":"J. Big Data"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1111\/j.1468-0394.2008.00449.x","article-title":"Financial decision support using neural networks and support vector machines","volume":"25","author":"Tsai","year":"2008","journal-title":"Expert Syst."},{"key":"ref_37","unstructured":"Heaton, J. (2008). Introduction to Neural Networks with Java, Heaton Research."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"151359","DOI":"10.1109\/ACCESS.2019.2948112","article-title":"Improved convolutional neural network based on fast exponentially linear unit activation function","volume":"7","author":"Qiumei","year":"2019","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fernandes de Mello, R., Antonelli Ponti, M., Fernandes de Mello, R., and Antonelli Ponti, M. (2018). A Brief Introduction on Kernels. Machine Learning: A Practical Approach on the Statistical Learning Theory, Springer.","DOI":"10.1007\/978-3-319-94989-5"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"136765","DOI":"10.1016\/j.scitotenv.2020.136765","article-title":"Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy","volume":"714","author":"Chen","year":"2020","journal-title":"Sci. Total Environ."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/5\/193\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:33:10Z","timestamp":1760031190000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/5\/193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,15]]},"references-count":40,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["computers14050193"],"URL":"https:\/\/doi.org\/10.3390\/computers14050193","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,15]]}}}