Financial service companies manage huge volumes of data which requires timely error identification and resolution. The associated tasks to resolve these errors frequently put financial analyst workforces under significant pressure leading to resourcing challenges and increased business risk. The purpose of this study is to address this challenge through introducing a formal task allocation model which considers both business orientated goals and analyst well-being.
They use a genetic algorithm (GA) to optimise our formal model to allocate and schedule tasks to analysts. The proposed solution is able to allocate tasks to analysts with appropriate skills and experience, while taking into account staff well-being objectives.
They demonstrate their GA model outperforms baseline heuristics, current working practice and is applicable to a range of single and multi-objective real-world scenarios. We discuss the potential for metaheuristics (such as GAs) to efficiently find sufficiently good allocations which can provide recommendations for financial service managers in-the-loop.
A 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 while still improving on current working practice for efficiency.
