The era of big data revolutionizes operational management in enterprises, amplifying the challenges for auditors in managing vast corporate information and escalating fraud risks. This study explores machine learning's role in identifying financial fraud, constructing models based on fraud triangle theory and empirical data. The model, particularly LightGBM, achieves a 73.21% accuracy, showcasing its effectiveness in predicting fraud risks in publicly traded companies.
The critical role of risk management in organizational success is highlighted, particularly for small and medium-sized enterprises (SMEs) facing unique challenges due to limited resources. "Risk Management by Design" advocates integrating risk management into all facets of SME operations, aiding early identification, cost efficiency, and a competitive edge. Despite benefits, SMEs must navigate resource constraints to effectively balance risk and innovation.
This study explores how AI affects auditors' judgments on complex estimates. It finds that when clients use AI for estimates, auditors' planned responses don't match their risk assessments. Auditors tend to plan less (more) work if AI-generated estimates were more (less) accurate previously, potentially posing concerns about audit effectiveness due to automation bias.
Determining liability for AI-caused harm lacks clear answers. In cases involving Tesla autopilot accidents, unfair discrimination by AI in HR, or medical procedures, responsibility is blurry. The EU's proposed AI liability regime and AI Act aim to address these complexities, bridging gaps in risk regulation and liability for AI-human interactions.
"An adverse development cover (ADC) is a form of an excess of loss reinsurance contract that provides coverage for future loss payments relating to claims incurred prior to a specified date… A framework for assessing the value of an ADC from the perspective of the ceding insurer is developed. This value assists in making decisions regarding the acquisition of an ADC, comparing available options on offer and accounting for the ADC under the IFRS17 accounting standard."
Attack graphs visually map potential attack paths in systems, aiding systematic vulnerability exploration. Enhancing them with countermeasures and consequences streamlines risk assessment, accommodating system or environment changes. Demonstrated through a case study, this method integrates with existing standards, addressing evolving threats for better risk management in computer systems.
"Since the global financial crisis of 2007–9, legal risk has become increasingly important for the banking sector. In Poland, the growth in importance is predominantly associated with the so-called regulatory tsunami, which has seen a constantly changing legal framework for bank operations..."
We explore the challenge of measuring causal effects in empirical analyses, particularly in areas like asymmetric information and risk management. It emphasizes the importance of causal analysis in policy evaluation and discusses various frameworks such as instrumental variable, difference-in-differences, and generalized method of moments. The analysis addresses questions related to risk management's impact on firm value, moral hazard in insurance data, separating moral hazard from adverse selection, and the causal relationship between liquidity creation and reinsurance demand. The findings suggest that appropriate methodologies can enhance the value of risk management in firms despite residual information problems in various markets.
Accurate insurance claims forecasting is vital for financial planning and risk management. This study introduces innovative variables, such as weather conditions and car sales, and employs Machine Learning algorithms to predict average insurance claims per quarter. Key influential variables include new car sales and minimum temperature with specific lags. The findings aid insurers in enhancing claims forecasting by considering additional parameters like weather and sales data.
We study risk processes with level dependent premium rate. Assuming that the premium rate converges, as the risk reserve increases, to the critical value in the net-profit condition, we obtain upper and lower bounds for the ruin probability. In contrast to existing in the literature results, our approach is purely probabilistic and based on the analysis of Markov chains with asymptotically zero drift.