Automation in financial reporting, processes, and risk mitigation
Hajjar, Elias (2024)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024051713001
https://urn.fi/URN:NBN:fi:amk-2024051713001
Tiivistelmä
The evolution of financial reporting and process management has been significantly shaped by integrating automation technologies such as Robotic Process Automation (RPA) and Artificial Intelligence (AI). Historically burdened by inefficiencies and susceptibilities to error, these processes have been transformed through automation, enhancing accuracy, efficiency, and compliance with regulatory standards. This thesis investigates the multifaceted impact of automation within financial processes, focusing on its application in current practices and its potential for future advancements.
The study was structured around a mixed-methods approach, combining qualitative insights from case studies with quantitative data from performance metrics and compliance reports. This methodology facilitated a comprehensive analysis of automation’s role before and after its implementation in financial settings. The research underscored automation’s critical role in enhancing operational efficiencies, mitigating risks, and adhering to regulatory compliances, aligning with theoretical perspectives such as systems theory and technological determinism.
Findings from the research indicate that automation technologies streamline processes and foster strategic decision-making and risk management, positioning them as pivotal elements within financial operations. The study suggests that advancements in AI and machine learning will continue to influence the scope and complexity of financial process automation, advocating for continuous adaptation and strategic integration within the sector.
In conclusion, this thesis highlights the transformative effects of automation technologies on financial processes, suggesting that their continued evolution will necessitate dynamic adaptations in financial practices. Future research is recommended to explore the broader implications of automation across different scales of financial institutions and to address the socio-technical challenges associated with implementing these technologies.
The study was structured around a mixed-methods approach, combining qualitative insights from case studies with quantitative data from performance metrics and compliance reports. This methodology facilitated a comprehensive analysis of automation’s role before and after its implementation in financial settings. The research underscored automation’s critical role in enhancing operational efficiencies, mitigating risks, and adhering to regulatory compliances, aligning with theoretical perspectives such as systems theory and technological determinism.
Findings from the research indicate that automation technologies streamline processes and foster strategic decision-making and risk management, positioning them as pivotal elements within financial operations. The study suggests that advancements in AI and machine learning will continue to influence the scope and complexity of financial process automation, advocating for continuous adaptation and strategic integration within the sector.
In conclusion, this thesis highlights the transformative effects of automation technologies on financial processes, suggesting that their continued evolution will necessitate dynamic adaptations in financial practices. Future research is recommended to explore the broader implications of automation across different scales of financial institutions and to address the socio-technical challenges associated with implementing these technologies.