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 types of credit risk management require data analytics, and increased data availability and processing tools will bring new credit risk management opportunities. Predictive analytics is the practice of deriving information from existing data in order 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.
Recognized by Gartner, CRIF's expertise in 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 rating model development from estimation, validation, and review to calibration and evaluation of economic groups.
Credit Risk Score Development
- Data management to extract value from data businesses require solutions to help them extract, align and distill what's essential and quickly determine analytical interpretations.
- Scoring models that permit optimisation of any financial institution process, which they are developed for. CRIF provides a full portfolio of modelling tools and expertise, empowering business analysts, from beginners to advanced modelers, to develop, build, test, deploy and manage predictive models. Types of scorecards we provide are:
- Application Scorecard:These are tools that allow organisations to predict the probability that an applicant will behave in a certain way, helping businesses to make effective automated decisions. Application scorecard for credit assesses the likelihood of default which means it predicts the risk of a customer paying a or not. In the credit risk application scorecard, the output is usually a numeric score provided for each applicant, with higher scores corresponding to lower levels of estimated risk. This supports lenders to make accurate and consistent decisions on whether to approve, review or decline applicants. Application scorecards can also help predict many other different metrics such as an applicant's affordability (ability to pay), potential future profitability and the likelihood to churn (attrition) etc.
- Behavioral Scorecard: Do you know who are your most profitable customers are? Are your customers defaulting in their payments with you or other lenders? Behavioral scorecards help in identifying, retaining and growing the right customers for our businesses. These quantify the customer behavior to improve credit portfolio management and customer management. With CRIF's behavioral 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 Scorecard: These scorecards facilitate debt management decisions. By considering the past behavior, it identifies the 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. Collection scorecard plays a significant role in the profitability of the business by minimizing the credit losses. This enables the business to minimize 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 can help organizations prioritize collection efforts, minimize defaults, maximize recoveries and reduce overhead costs by identifying customers who have a higher propensity to pay and targeting them with innovative and tailor-made collection strategies.
- Fraud scoring models aim at optimizing the fraud risk control concentrating the verification on a reduced number of cases both for supporting securitization with pool audit services and IFRS 9 evaluations;
- Model management including Trend, Stability and Migration Analysis and tracking, monitoring, refining, recalibrating and/or re-developing scorecards;
- Feasibility Studies such as Alternative Data Value Analysis and Custom Scoring Value Assessment.
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 including credit sustainability indexes which determine the ability of the customer to take on debt and incorporate Financial Stress Index, Credit Limit & Household budget.
- Pricing risk-based model, such as Risk-Based Pricing in order to calibrate each to the risk profile of the customer and optimise the cost structure of the institution;
- 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 to demonstrate how this component can help save costs and to have a faster response, and as well as allow consistent credit risk management.