Optimising task allocation to balance business goals and worker well-being for financial service workforces

Abstract

PurposeFinancial service companies manage huge volumes of data which requires timely error identification and resolution. The associated tasks to resolve these errors often put financial analyst workforces under significant pressure leading to resourcing challenges and increased business risk. To address this challenge, we introduce a formal task allocation model which considers both business orientated goals and analyst well-being. MethodologyWe use a Genetic Algorithm (GA) to find the optimal allocation and scheduling of tasks to analysts. The proposed solution is able to allocate tasks to analysts with appropriate skills and experience, while taking into account longer-term staff well-being objectives. FindingsWe demonstrate our GA model outperforms baseline algorithms, current working practice, and is applicable to a range of single and multi-objective real-world scenarios. We discuss the implementation of our AI powered model with workforce managers in-the-loop. OriginalityA key gap in existing allocation and scheduling models, is fully considering worker well-being. This paper presents an allocation model which explicitly optimises for well-being