data-driven anomaly detection and cashflow prediction for accountants

Just like many industries today, the accountancy sector is also confronted with disruptive digitalization. This digitization means that accountants are expected to provide more and more proactive services, where the focus used to be on executive and compliance-related work. With our project we want to help accountants to fulfill these new expectations. By applying advanced statistical methods and machine learning techniques, we want to focus strongly on following two research topics. First of all, we want to test and develop different methods to discover anomalies ​​in accounting data. This helps the accountant to automate standard checks, but also to discover potential opportunities. Secondly, we want to test and develop robust and interpretable cash flow forecasting models. In both areas we are convinced that there is still enormous potential to create added value for the accountant. The collaboration with Boltzmann provides the ideal context for this project due to the presence of a rich, ever-expanding dataset, combined with professional expertise in various areas within the framework project team.

Keywords:ACCOUNTING, STATISTICAL MODELLING, MACHINE LEARNING, STATISTICAL DATA ANALYSIS

Disciplines:Statistics, Data mining, Machine learning and decision making, Accounting and auditing

Project type:Collaboration project