Risk Management & Predictive Analytics
CRIF's predictive analytics tools and software generate hundreds of millions of score calculations and risk decisions every year around the world
Credit Risk Management & Predictive Analytics
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.
Credit Risk Analytics Rating and Score Development
- Data management to extract value from data businesses require solutions to help them extract, align, and distil what's essential and quickly determine analytical interpretations.
- Scoring models permit the optimisation of any financial institution process which they are developed for. CRIF provides a complete portfolio of credit risk analytics modelling tools and expertise, empowering business analysts, from beginners to advanced modellers, to design, build, test, deploy and manage predictive models. The types of scorecards we provide are:
- Application Scorecards: These tools allow organisations to predict the probability that an applicant will behave in a certain way, helping businesses make effective automated decisions. Application scorecard for credit assesses the likelihood of default, predicting the risk of a customer paying or not. The output of the credit risk application scorecard is usually a numeric score provided for each applicant, with higher scores corresponding to lower estimated risk levels. This supports lenders in making accurate and consistent decisions on whether to approve, review or decline applicants. Application scorecards can also help predict many other credit risk metrics, such as an applicant's affordability (ability to pay), potential future profitability and the likelihood to churn (attrition) etc.
- Behavioural Scorecards: Do you know who your most profitable customers are? Are your customers defaulting on their payments with you or other lenders? Behavioural scorecards help identify, retain, and grow the right customers for our businesses. These quantify customer behaviour to improve credit portfolio management and customer management. With CRIF's behavioural scorecards, lenders can make more customer-centric decisions, respond effectively to their individual needs, enhance control of risk exposure, create a more effective pricing program, and accurately target current and prospective customers for cross-selling programs.
- Collections Scorecards: These scorecards facilitate debt management decisions. By considering past behaviour, it identifies risky customers. Appropriate treatments can then be initiated at the earliest on the customers based on their risk levels to protect the business assets with the most cost-effective mechanism applicable. A Collection scorecard plays a significant role in the business's profitability by minimising credit losses. This enables the organisation to minimise the provisions taken against the credit. The provisions directly impact the capital allocations that could otherwise be invested in the growth of the business. CRIF's collections scorecard and credit risk management analytics process and tools can help organisations prioritise collection efforts, minimise defaults, maximise recoveries and reduce overhead costs by identifying customers with a higher propensity to pay and targeting them with innovative and tailor-made collection strategies.
- Fraud scoring models aim to optimise fraud risk control concentrating the verification on a reduced number of cases for supporting securitisation with pool audit services and IFRS 9 evaluations.
- Model management including Trend, Stability and Migration Analysis and tracking, monitoring, refining, recalibrating, and re-developing scorecards.
- Feasibility Studies such as Alternative Data Value Analysis and Custom Scoring Value Assessment.
Credit Risk Management Advanced Model
- Internal Rating System (Basel Compliant) Advanced Rating systems (Both Corporate and Retail) model development from estimation, validation and review to calibration and evaluation of economic groups, including Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD) models and encompassing both quantitative and judgmental approaches.
- Financial models include credit sustainability indexes which determine the ability of the customer to take on debt and incorporate the Financial Stress Index, Credit Limit & Household budget.
- Pricing risk-based models, such as risk-based pricing, to calibrate each to the customer's risk profile and optimise the institution's cost structure.
- Fair value for assessing the lifetime value of a retail portfolio, leveraging CRIF data.
Predictive analytics is a key component and integrated part of many of our offerings, including our credit management platform products (like StrategyOne) and services, with many success stories demonstrating how this component can help save costs and have a faster response, and allow consistent credit risk management.