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This study aims to investigate the intricate relationship between corruption and the credit costs faced by small and medium-sized enterprises (SMEs) in OECD countries, a critical yet underexplored area in financial crime research. The primary aim is to dissect and understand how corruption impacts SMEs’ access to credit, highlighting a significant yet overlooked aspect of financial crime. This research seeks to fill a gap in the literature by providing empirical insights into the economic consequences of corruption, specifically on SMEs financing.

This study used secondary panel data from the World Bank and OECD databases. The data covered the period 2007–2020 for 25 OECD countries. This study used interest rate for SMEs loans as a dependent variable and GDP per capita, inflation and corruption index as independent variables. This study used the panel autoregressive distributed lag (ARDL) model to examine the relationship between variables.

The empirical findings derived from Panel ARDL postulate an intriguing dichotomy in the effects of GDP per capita, inflation rate and corruption on interest rates in both the short and long run. It was discerned that an increase in GDP per capita and inflation rate correlates with a decrement in interest rates in the long run, suggesting a potential compromise by central banks between controlling inflation and fostering economic growth.

This paper makes a novel contribution to the field of financial crime by illuminating the often-overlooked economic dimensions of corruption in the context of SMEs financing. It provides a unique perspective on the ripple effects of corrupt practices in credit markets, enriching the academic discourse and informing practical approaches to combating financial crime.

This paper aims to determine the tax potential of G20 countries and estimate the tax revenue they could generate. The study evaluates the effectiveness of tax revenue collection for G20 nations from 2008 to 2020 and investigates the relationship between tax collection efficiency and tax evasion. The study also examines the link between tax collection efficiency and a proxy for tax evasion through anti-corruption efforts.

The study assumes that tax collection is a function of gross domestic product (GDP), population, imports and price level. The study uses a stochastic frontier analysis to calculate the efficiency of tax collection. It estimates the loss in total tax collection due to inefficiency by comparing actual and best-practice tax collection.

The findings indicate that anti-corruption measures and technological advancements positively impact tax collection efficiency. Great Britain is identified as the most efficient country in tax collection, whereas Saudi Arabia is the least efficient. Germany has the highest losses in tax collection due to inefficiency, while Australia experiences the lowest losses in tax collection.

This study suggests several practical implications. For example, legislators and policymakers should pay more attention to anti-corruption policies. Also, tax agenesis should focus on better understanding variations in tax collection efficiency between countries and how they relate to tax evasion.