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

Chris Duckworth, Zlatko Zlatev, James Sciberras, Peter Hallett and Enrico Gerding

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Abstract: Purpose: 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. To address this challenge, we introduce a formal task allocation model which considers both business orientated goals and analyst well-being. Methodology: We 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. Findings: We demonstrate our 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. Originality: 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.

Date: 2025-06
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Published in ISSN 1746-5664 (2025)

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