This research addresses the critical need for intelligent optimization mechanisms in financial process modules by developing a machine learning-enhanced collaborative system designed for digital finance platforms, aiming to bridge theoretical advances in human-machine collaboration with practical applications in financial process optimization. A sophisticated multi-layered architecture integrating machine learning capabilities with human decision-making processes was developed, incorporating advanced ensemble algorithms, multi-objective optimization techniques, and adaptive learning mechanisms. The system was validated across three real-world scenarios. These included credit risk assessment using 2.26 million Lending Club records, anti-money laundering with 6.3 million FinCEN transactions, and customer service optimization with 1.8 million banking interactions. The collaborative system achieved significant improvements. Cost reduced by 28.4% and accuracy increased by 15.3% in credit risk assessment. AML efficiency improved by 256%, and AUC-ROC increased from 0.847 to 0.923. Processing time was reduced from 4.2 days to 1.8 days while maintaining regulatory compliance, resulting in a 44.8% return on investment in the first operational year. The learning collaborative approach efficiently combines human knowledge and AI, outperforming regular computerized methods as well as purely human strategies and maintaining long-term system improvement through its adaptive learning capability. This study provides practical toolkits for financial institutions to further explore AI in process optimization, aiming to achieve sustainable competitive advantages and compliance, while also ensuring operational efficiencies.
Wang et al. (Sat,) studied this question.
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