UTILIZING MACHINE LEARNING FOR CASH FLOW FORECASTING AND ITS INFLUENCE ON STARTUP BUSINESS MODEL ADAPTATION
DOI:
https://doi.org/10.1234/aira.v2i1.59Keywords:
artificial intelligence, business model innovation, cash flow forecasting, machine learning, startup managementAbstract
This systematic literature review examines the application of machine learning technologies in cash flow prediction and their transformative impact on business model adaptation within startup companies. Following PRISMA guidelines, a comprehensive analysis of 48 high-quality studies published between 2015-2024 was conducted across multiple databases including Scopus, Web of Science, IEEE Xplore, and ACM Digital Library. The research reveals that machine learning algorithms, particularly Long Short-Term Memory networks and deep neural networks, achieve 15-25% improvements in cash flow prediction accuracy compared to traditional statistical methods. Four primary adaptation mechanisms were identified: enhanced financial visibility with 6-8 week lead times for cash shortfall anticipation, improved risk assessment capabilities, strengthened investor relations resulting in 23% higher fundraising success rates, and operational optimization achieving 10-15% working capital efficiency improvements. Sector-specific analysis demonstrates varying adoption patterns, with technology startups showing 91% implementation rates, followed by e-commerce at 78%, service-based at 64%, and manufacturing at 52%. Implementation challenges include data quality issues affecting 84% of deployments, technical expertise gaps in 71% of startup teams, and computational resource constraints. The research establishes that gradual implementation approaches achieve 38% lower failure rates compared to wholesale replacement strategies. Bibliometric analysis reveals evolving research focus from technical algorithm development toward practical business applications and strategic impact assessment. The findings demonstrate that machine learning-enhanced cash flow prediction creates sustainable competitive advantages through improved operational efficiency, enhanced strategic agility, and superior risk management capabilities, positioning these technologies as critical strategic investments for startup competitiveness and long-term sustainability.
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