Credit Risk Management Solutions & Predictive Analytics
CRIF's predictive analytics tools and software generate hundreds of millions of score calculations and risk decisions every year around the world, enhancing decision-making processes
Data Management
To extract value from data, businesses require credit risk management software solutions to help them extract, align, and distil what's essential, enabling real-time analytical interpretations and streamlined workflows.
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.
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, aiding in credit decisions. 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 and how they influence your credit management decisions? 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, contributing to financial stability. 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 and improve compliance with regulatory standards. By considering past behaviour, it identifies risky customers and enhances the risk assessment framework. 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, Custom Scoring Value Assessment, and integrating real-time decision-making tools.
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 for better decisioning.
Pricing Risk-Based Models
Personalized communication strategies foster better customer relationships and improve repayment rates.
Fair Value
For assessing the lifetime value of a retail portfolio, leveraging CRIF data.
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