Automated Reasoning and Learning for Automated Payroll Management
Keywords:Payroll Management, Constraint Satisfaction, Constraint Reasoning, Satisfiability Modulo Theory, Constraint Optimisation, Knowledge-Based Systems, Machine Learning
AbstractWhile payroll management is a crucial aspect of any business venture, anticipating the future financial impact of changes to the payroll policy is a challenging task due to the complexity of tax legislature. The goal of this work is to automatically explore potential payroll policies and find the optimal set of policies that satisfies the user's needs. To achieve this goal, we overcome two major challenges. First, we translate the tax legislative knowledge into a formal representation flexible enough to support a variety of scenarios in payroll calculations. Second, the legal knowledge is further compiled into a set of constraints from which a constraint solver can find the optimal policy. Furthermore, payroll computation is performed on the individual basis which might be expensive for companies with a large number of employees. To make the optimisation more efficient, we integrate it with a machine learning model that learns from the previous optimisation runs and speeds up the optimisation engine. The results of this work have been deployed by a social insurance fund.
How to Cite
Dumancic, S., Meert, W., Goethals, S., Stuyckens, T., Huygen, J., & Denies, K. (2021). Automated Reasoning and Learning for Automated Payroll Management. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15107-15116. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17774
IAAI Technical Track on Highly Innovative Applications of AI