Forecasting and improving fiscal performance of ULBs using K-SMART data
Local bodies play a vital role in managing community resources and services, but effective financial oversight remains a significant challenge. Many local governments lack a cohesive system to track and analyze their financial data, leading to inefficiencies in revenue collection, expenditure management, and the ability to forecast potential financial crises. This often results in missed opportunities for improvement and inadequate preparedness for financial shortfalls.
The goal is to develop an advanced system that utilizes data from K-SMART to comprehensively track local body finances, analyzing revenue sources including property tax, profession tax, license fees, rent revenue, and own asset revenue as well as various expenditures such as establishment costs, salaries, pensions, utility bills, welfare expenses, and statutory obligations. This system must leverage artificial intelligence (AI) and machine learning (ML) to:
Aggregate and analyze financial data from K-SMART to provide a clear overview of monthly revenue and expenditure patterns.
Identify trends and anomalies through advanced data analysis, helping local bodies understand historical financial performance and anticipate future challenges.
Forecast potential financial crises by employing AI and ML algorithms to predict fiscal shortfalls based on historical data and current spending patterns.
Generate actionable recommendations for improving revenue collection, reducing unnecessary expenditures, and enhancing overall financial health.
Provide user-friendly visualization and reporting tools to facilitate transparent communication with stakeholders and support informed decision-making.
By addressing these challenges, the proposed system will enhance the financial management capabilities of local bodies, promote fiscal responsibility, and ultimately contribute to improved service delivery and community well-being.
