Effective coordination between fiscal and monetary policy is crucial for macroeconomic stability, yet achieving it presents significant challenges due to differing objectives and institutional setups. This study evaluates the strategic interaction between fiscal and monetary authorities in Hungary from 2013 to 2023, employing the Nash equilibrium framework under the assumption of non-cooperative behavior. By modeling the authorities as independent players optimizing distinct payoff functions based on key economic indicators (interest rates, government spending, inflation, output gap, fiscal deficit, and public debt), the analysis estimates the best response strategies and computes the resulting Nash equilibrium. The key findings reveal persistent deviations between actual policies and the computed equilibrium strategies. Specifically, actual fiscal policy was consistently more expansionary (average actual deficit −2.6% to 7.6% GDP vs. equilibrium recommendations ranging from 8.5% surplus to −3.0% deficit) than the Nash equilibrium indicated, particularly during periods of economic growth. Monetary policy often lagged in equilibrium recommendations, maintaining low interest rates (e.g., 0.9% actual vs. 11.5% equilibrium in 2019) before implementing sharp increases (13% actual vs. approx. 3.5–3.8% equilibrium in 2022–2023) that significantly overshot the equilibrium. These misalignments underscore potential suboptimal outcomes arising from independent policymaking, contributing to increased public debt and heightened inflationary pressures in the Hungarian context. This study highlights the potential benefits of aligning policies closer to mutually consistent strategies, suggesting that improved coordination frameworks could enhance macroeconomic stability, offering insights relevant to Hungary and similar economies.

The increasing data scales in today’s business sectors coupled with the necessity of risk management raise the importance of business intelligence tools as an integrated solution for the insurance industry. These tools have mostly been used to achieve effective risk management. Although methods of risk management in the insurance industry have been proposed many years ago, the research effort has primarily been focused on predictive analyses. This study aimed to investigate the role of business intelligence as a solution to illustrate its potential in risk management particularly for decision-makers in agricultural insurance. We hypothesized that this would make a preferable decision in uncertain conditions. Sample data from the online transaction process system of Iran agricultural insurance fund were preprocessed in SQL server. Multidimensional online analytical processing architecture was analyzed using Targit business intelligence tool. Our results identified financial risks that lead to a framework of controlling risk based on business intelligence in the agricultural insurance fund.