Matei, Kononenko, CHALLENGES FOR SMES IN IMPLEMENTING AI IN FINANCIAL MANAGEMENT

V.V. Matei, PhD in Economics, Associate Professor
M.A. Kononenko, student
Taras Shevchenko National University of Kyiv

CHALLENGES FOR SMES IN IMPLEMENTING AI IN FINANCIAL MANAGEMENT

Effective financial management is vital for small and medium-sized enterprises (SMEs), with artificial intelligence (AI) driven forecasting and cash flow optimization offering transformative benefits. Comprehending feasible challenges government shall encounter through incentives and regulatory frameworks is essential for SMEs to harness AI's potential and ensure responsible adoption in financial processes.
Effective financial management for SMEs initiates with strategic financial planning. SMEs must forecast their financial needs, taking into account factors such as operating expenses, working capital requirements, and investment in growth. By crafting comprehensive financial plans, SMEs can better allocate resources, make informed decisions, and secure financing when needed. The latest AI advancements have revolutionized these procedures not only within multinational enterprises' operations but also within SMEs'. AI-powered financial software can analyze historical data, market trends, and economic indicators to create highly accurate financial forecasts. AI can assist SMEs in predicting sales trends and demand fluctuations, which is vital for resource allocation and investment decisions. According to a study by the management consultancy firm McKinsey & Company, enterprises that integrated AI into their planning managed to reduce administration costs (by 25-40 %), warehouse costs (up to 10 %), and errors in planning costs for the purchase of goods (by 20-50 %) [5].
Cash flow management is another critical for the survival of SMEs, with poor cash management accounting for 82 % of business failures in 2021 [1]. SMEs often have tight cash reserves, so it is crucial to monitor income and expenses carefully. Maintaining a positive cash flow ensures that they can meet their short-term obligations, pay suppliers on time, and seize opportunities for growth without relying on costly external financing. AI algorithms can monitor cash flow in real time, identifying patterns and anomalies. These algorithms offer suggestions for optimizing cash flow by adjusting payment schedules, reducing inventory, or providing incentives for early payments from customers. Additionally, AI plays a key role in aiding SMEs in making informed decisions about investment and growth. The technology assesses market conditions, analyzes customer behavior, and recommends optimal strategies. Thus, effective cash flow management and AI-driven solutions utilization are essential for the survival and success of SMEs, allowing them to maintain financial stability and make informed decisions for sustainable growth.
Nevertheless, AI deployment in financial management remains posing challenges for SMEs. One considerable issue is the availability and quality of data. AI relies heavily on historical data to make accurate predictions and forecasts. Many SMEs may not have access to the extensive datasets required for AI-driven predictions. Unlike large corporations with years of transactional data (often referred to as data-opolies), SMEs might have limited historical financial data, which can hinder the training of AI models. The monopoly of large corporations in the possession of big data can force SMEs to appeal to them and buy access to databases. In developing countries, notably in Latin America and Asia, where the legal framework for the digital markets is poorly developed, and the activities of data-opolies are not limited, it results in a violation of fair competition principles [2]. Within this context, the quality of the available data is another concern since inaccurate or incomplete data can lead to flawed predictions, which, in turn, can have detrimental effects on financial planning and decision-making.
Another major challenge is the financial cost associated with implementing AI systems. While AI becomes more accessible over time, it is not without its financial demands. SMEs, often operating on limited budgets, may consider the initial investment in AI technology and infrastructure daunting. This cost includes not only acquiring AI software but also integrating it into existing processes and infrastructure. Generally, businesses should spend at least 30,000-50,000 U.S. dollars on software, 320,000 U.S. dollars per year for maintenance, and pay IT specialists 25-50 U.S. dollars per hour [4]. SMEs might also need to invest in new hardware, software licenses, and staff training to make effective usage of AI. Such expenses may raise questions about the reasonability of implementing AI in financial management.
It is worth noting that SMEs face data security issues when integrating AI into financial data processes. Handling sensitive financial data requires robust security measures to safeguard against cyber threats and data breaches. As AI systems often require access to a vast amount of financial and operational data, the risk of a security breach becomes more pronounced. Inadequate security measures can lead to unauthorized access, data leaks, or the compromise of sensitive information. If, for multinational enterprises, this issue can lead to elevated financial expenses, for SMEs, the risks are much drastic. In 2021, 61 % of all cyber-attacks targeted SMEs [6]. Data breaches can not only result in financial losses and operations shutdowns but can also lead to a complete termination of business activity (according to statistics, 75 % of SMEs could not continue operating after a ransomware attack). Therefore, SMEs must adhere to stringent data protection and security standards that might entail higher initial financial investments in AI implementation.
In this light, governments shall be proactive and take responsibility for creating the circumstances for the successful implementation of AI by SMEs. First and foremost, governments should provide financial incentives, tax credits, or subsidies to SMEs looking to invest in AI technologies for their financial management processes. These incentives assist in offsetting the costs associated with AI implementation, making it more accessible to smaller enterprises. In Canada, the "Strategic Innovation Fund" offers financial support for SMEs to embrace advanced technologies. This initiative supports fund projects that drive innovation, such as the integration of AI into financial management systems. Following this model, governments worldwide can play a crucial role in fostering AI among SMEs, enhancing their competitiveness and contributing to economic growth.
Another strategy for governments is establishing clear regulatory frameworks for AI adoption, thus ensuring that ethical and legal considerations are addressed. Proper legislation provides SMEs with data privacy, security, and compliance guidelines, instilling confidence in AI technologies. In the European Union, where SMEs represent 99% of all businesses, the General Data Protection Regulation (GDPR) sets stringent data protection regulations, including those related to AI [3]. This regulation arranges guidelines for AI deployment within the EU, safeguarding consumers and instilling trust in AI-driven services. It underscores the imperative need for such policies to constitute a substantial part of the governments' agenda, fostering responsible AI adoption within SMEs in an increasingly digital landscape.
In conclusion, the integration of AI into SMEs' financial management processes is a transformative step towards increased accuracy, cost reduction, and operational resilience. AI-driven forecasting can significantly enhance the productivity of strategic financial planning, leading to reduced lost sales and product unavailability, lower warehousing costs, and streamlined administration processes. However, the challenges of data availability, implementation costs, and data security cannot be overlooked. Governments have a pivotal role in fostering this transition by offering financial incentives, providing access to data and infrastructure, and establishing regulatory frameworks.

References
1. AI Can Help Small Businesses Prepare for the Unexpected [Electronic resource]. URL: https://www.wired.com/brandlab/2020/05/ai-can-help-small-businesses-prepare-for-the-unexpected/
2. Filipe Da Silva, Georgina Núñez. Free competition in the post-pandemic digital era: the impact on SMEs. Project Documents (LC/TS.2021/15), Santiago, Economic Commission for Latin America and the Caribbean (ECLAC), 2021
3. Galia Mancheva. “European Union Regulatory Framework on Artificial Intelligence (SMEs)”. Journal of Development Studies. Volume 2, December 2021, Issue 1, pp 20-25.
4. How Much Does Artificial Intelligence Cost in 2023? | AI Development Cost [Electronic resource]. URL: https://www.suffescom.com/blog/how-much-does-artificial-intelligence-cost
5. Jorge Amar, Sohrab Rahimi, Zachary Surak, and Nicolai von Bismarck. AI-driven operations forecasting in data-light environments [Electronic resource]. URL: https://www.mckinsey.com/capabilities/operations/our-insights/ai-driven-operations-forecasting-in-data-light-environments
6. Komron Rahmonbek. 35 Alarming Small Business Cybersecurity Statistics for 2023 [Electronic resource]. URL: https://www.strongdm.com/blog/small-business-cyber-security-statistics