AI-Driven Financial Management for MSMEs: Transforming Decision-Making and Access to Finance in Developing Economies
Amey Adinath Choudhari et al.
What the paper says
Micro, Small, and Medium Enterprise (MSME) is the core of the employment creation and stability of the developing economies, but is characterized by inability to make financial decisions, cash-flow management, and access to formal finance on a consistent basis. This research paper suggests a financial management framework, based on AI in which the financial intelligence and credit accessibility of MSMEs are improved by applying the algorithms of the Random Forest, Long Short-Memory (LSTM), and Gradient Boosting (XGBoost). Random Forest models are used to classify credit risks that are robust on transactional and alternative financial data whereas LSTM networks are used to identify time trends in cash-flow behaviour so as to properly forecast revenues, and plan liquidity. XGBoost is used to optimize the prediction of loan approval and risk of default by means of nonlinear interaction of features. The suggested framework automates the expense classification, forecasts the short-term and future cash-flow and creates real-time financial advice, which minimizes information asymmetry between MSMEs and financial institutions. According to the empirical data, AI-based financial management allows to enhance the accuracy of cash-flow predictions by more than 25 %, enhance credit approvals by nearly 18 % and shorten the average time of loan processing by nearly 40 % compared to the traditional rule-based systems. Also, MSMEs that embrace AI-driven tools exhibit better financial transparency, reduce the cost of operations and better resistance to fluctuations in the market. The study arrives at the conclusion that the implementation of AI in financial management systems can considerably transform the way MSME decision-making is carried out, as data-driven strategies become feasible, risk mitigation strategies are proactive, and finance is available to all. These results highlight the opportunities of smart fintech solution to enhance the sustainability of MSMEs, promote financial inclusion, and stimulate the economic growth in resource-limited and emerging market conditions.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.