MACHINE LEARNING IN FINANCIAL FORECASTING: OPPORTUNITIES AND CHALLENGES

AIM In contrast to conventional approaches, this study examines how AI-driven recruitment platforms affect organizational hiring expenses. The study uses a mixed-methods approach, integrating qualitative information from semi-structured interviews with quantitative datafromsurveys of recruiting managers and HR experts. In addition to examining the perceivedadvantages and difficulties of both recruitment strategies, this data is examined to evaluatecost parameters including time-to-hire, cost-per-hire, and administrative overhead.MATERIALSAND METHODS: To acquire thorough data on recruitment expenses and perceptions, thestudycombines surveys, interviews, and secondary data analysis. While qualitative data is analyzedusing thematic analysis to find important themes and patterns in perceptions and experiences, quantitative data analysis compares cost metrics between AI- driven and conventional approaches using t-tests and ANOVA.CONCLUSION According to the study's findings, implementing AI-driven hiring practices would not always result in significant cost savings, andmore investigation is required to completely comprehend how AI affects hiring results outsideoffinancial concerns. In order to validate and build upon these findings, future research withbiggerand more diverse samples may be necessary. The study's shortcomings, such as its dependenceon self-reported data and the possibility of sample size constraints, are noted.