CRIF Credit Bureau Score is a standardized, objective and transparent risk measure that allows to compare customers’ risk across different credit portfolios on the Uzbekistan Market. The score allows to support Banks and Financial Institutions in all the stages of the credit life cycle from acquisition to ongoing monitoring up to early risk detection.
The Score is a powerful tool to widely support risk and credit management activities: pricing, mitigate losses and provisioning, benchmarking, stress testing, credit fair value and risk appetite framework assessment.
CRIF Credit Bureau Score allows for a more refined and predictive credit risk estimation thanks to:
- Data quality: input data are deeply analyzed and “cleaned” to allow a more refined screening of customers and improve score performances
- Easy to understand: in a single number, the Credit Bureau Score resumes the credit history of the subject; moreover, with a value added service of a Score Profiling, it is possible to highlight the main score factors that impact on the Credit Bureau Score
- Deep credit history analysis, based on the last 24 months of credit bureau data availability, for a more exhaustive analysis of the type and use of credit products
- Excellent KPI (Key performance indicator): statistic performance measures (Gini index and K-S) give evidence of the effectiveness in predicting the risk of the subjects.
CBS automatically assigns and scores the subject according to the appropriate set of variables built from Credit Bureau data and selected after deep analysis: Data Analysis points out the most predictive and meaningful risk pattern variables for each Subject: Individuals and Business.
CRIF offers the possibility to use CBS through 10 standard tranches: these tranches are built in order to maximize the granularity in risk classification and, at same time, ensuring the monotonicity in risk rank ordering (i.e. tranche A has to be less riskier than tranche B, tranche B less risky than tranche C and so on).
With the support of CRIF team, Banks/FI can understand the impact of using the Credit Bureau Score and how to enhance their credit processes during Customer’s life cycle: Retrospective Analysis is the activity that allows to test the Credit Bureau Score inside the credit processes and how to maximize its value.
Furthermore, CBS can be used to assist the application phase in a wide variety of screening operations:
- Concurring in the approval/refusal decisioning (integrated with the application score of the Bank/FIs or incorporated into existing procedures)
- Providing benefits associated with scoring in operations where Banks/ FIs internal scoring is not available (new products, instant credit programs, no application score already in place)
- Assessing the application strategies (initial credit limit calculation, risk based pricing definition, etc.)
CBS, on the other hand, considers the positive indicators of future credit performance as well as the negative. In this comprehensive evaluation, the applicant with previous 60-day delinquencies may in fact prove to be a low risk if the established a track record of “clean” credit since the delinquencies occurred.
In conclusion, Credit Bureau Score can be also used in the Approve/Reject decisions combined with other score or set of rules, and Banks/FIs can effectively incorporate CBS into their collection strategies in order to take more targeted action on appropriate customers.
In the table below is represented an overall of the CBS benefits:
| Features CBS | Benefits |
| Rank-orders the likelihood of a future delinquency on the credit lines | Helps Banks / FIs to identify quickly and consistently the “good” and “bad” clients among existing and new clients. Separating low- and high-risk subjects leads to better management corporate policy decisions and allows Banks / FIs to focus on potential medium-risk clients that need to be evaluated separately, which maximize profitability. |
| Easy to understand | CBS is a simple and comprehensive risk measure. Each score is a proxy of the likelihood of a subject defaulting on a Credit Product by examining his entire credit portfolio. A higher score indicates lower risk of delinquency. |
| Exclusion rules | CBS provides the description of the reasons in case the Customers are excluded from the score computation: this is, anyway, an important information to be evaluated. |
| Monitoring and Tuning based on approx. 1.3mln Individuals and Business: representative sample of the Region multicountry Credit Market data | The large CB data sample considers all over the Central Asia Region with the types of credit products and payment histories. This array of information makes CBS a robust model that provides enhanced risk prediction for existing account holders as well as for new account prospects. |
| Expected good/bad odds and bad rate | Beside the score, CBS will also provide a measure of the expected creditworthiness of the counterparty, good/bad odds and bad rate in 12 months period from the time of scoring. |
| Data Quality activity | In order for the score to be most accurate and robust, there is a strong data quality activity on input data: this helps the Score in reduce the number of clients that require manual review and increasing the number of prospects. |
| CBS monitoring | CRIF will perform periodically all the necessary statistical analysis to check the models stability and performance over the years. |