"AUTOMATED FINANCIAL SYSTEMS: LEVERAGING AI TO CONTROL AND RECOVER NON-PERFORMING ASSETS"

ABSTRACT: As the number of outstanding mortgages keeps growing, the financial services sector has witnessed a fundamental change in the way that the debt is paid off. Artificial intelligence (AI), an emerging tool that has an opportunity to revolutionise this industry, is currently being used to automate and streamline the debt gathering process. In order analyse enormous quantities of data, forecast the possibility of recovery, and streamline operational procedures, automated debt recovery systems use language processing techniques, algorithmic learning, and automated analytics. AI-powered debt collection procedures are anticipated to be reliable, effective, and legal. On the other hand, traditional strategies have been linked to operational weaknesses and increasing costs. These technologies minimise the need for interaction between people while providing effective utilisation of resources, customised communication, and quick data analysis. The implementation of artificial intelligence (AI) in loan recovery is resulting in substantial advances in statistical evaluation, simulation, and decision-making; this might significantly change the manner in which the banking industry handles managing its debts. The study's findings emphasise the need for a data-centric architecture that significantly changes collection techniques and the necessity for computational intelligence (AI) to attain accuracy and effectiveness. In conclusion, technological innovation has the potential to significantly alter the way debt currently is managed, maintaining the long-term viability and efficiency of financial institutions. The system's abilities can be seen by the actual-life use of machine learning approaches like logistical analysis and predictive modelling. KEYWORDS: Debt, Recovery Methods, Automation, Artificial Intelligence, And Productivity