Lending is becoming more future-oriented, and Predictive Analytics can help financial institutions be at the forefront of innovation. All credit risk management processes require data analytics, and increased data availability and processing tools will bring new credit risk analytics and management opportunities. Credit risk analytics uses data and statistical models to assess the probability of default and the potential loss if a default does occur. Credit risk management involves implementing strategies to minimise the potential for default and loss. Predictive analytics is the practice of deriving information from existing data to identify the likelihood of patterns and predict future outcomes and trends. It forecasts what might happen in the future with an acceptable level of reliability and incorporates what-if scenarios and risk assessment. Recognised by Gartner, CRIF's expertise in credit risk analytics and predictive analytics is shown by the development of various scoring projects in many, including Bureau scoring models, spanning over 18 countries which in total are used to make hundreds of millions of score calculations and decisions each year around the world. Rating systems are a core competency in CRIF; thanks to CRIF's Rating Agency experience, we provide a credit risk rating model from development, estimation, validation, and review to calibration and evaluation of economic groups.
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