AI-Powered Risk Discovery in the Data Age
DOI:
https://doi.org/10.5281/zenodo.21509280Keywords:
Big Data And AI In Risk Management, Risk Assessment Analytics, Financial Risk Typologies, Credit Risk Modeling, Market Risk Analysis, Operational Risk Management, Liquidity Risk Evaluation, Reputational Risk Assessment, Risk Data Requirements, Internal And External Data Sources, Risk Analytics Pipelines, AI-Driven Decision Support, Fraud Detection Systems, Customer Profiling Analytics, Model Calibration And Discrimination, Risk Model Performance Evaluation, Social Media And Sentiment Data, Investment Risk Analytics, Enterprise Risk Management, Advanced Risk AnalyticsAbstract
Integration of Big Data Analytics and Artificial Intelligence (AI) technologies is transforming risk assessment practices across industries, including banking, insurance, and investment. Yet, a comprehensive typology of risks considered, corresponding data requirements, and pipeline designs remains elusive. The synthesis offered here supports risk assessment through the structured integration of various Big Data and AI methods throughout the risk management cycle. Current literature is examined to identify risk models spanning credit, market, operational, liquidity, and reputational risk. For each, the critical data sources required to train and evaluate the models are documented. These data can be harvested from within the institution or supplemented with external sources. Emerging developments in risk model performance assessment are also discussed, including the importance of distinguishing between model calibration and discrimination. Finally, state-of-the-art Big Data and AI technologies for risk evaluation are mapped to the corresponding risk classes.
Integration of Big Data Analytics and AI technologies is transforming decision-making processes across industries, including banking, insurance, and investment. Such transformation holds potential for long-standing data-hungry tasks, such as fraud detection and customer profiling, which have been shrouded in the secrecy of proprietary models for years. Moreover, support for these daunting processes is becoming increasingly critical given the rising prevalence of new data sources such as social media and market sentiments. Yet, a comprehensive typology of risks considered, corresponding data requirements, and pipeline designs remains elusive. The synthesis offered here supports risk assessment through the structured integration of various Big Data and AI methods throughout the risk management cycle.
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