Good credit score
The scoring of a customer’s credit score in credit processes is usually based on a scorecard, which is fundamentally informed by the data found in the customer’s credit report. This report is the primary source of information for calculating credit scores, as it contains detailed records of credit habits and behaviors that significantly impact the score. It takes the form of a set of customer characteristics and corresponding attributes with assigned points. After analyzing all the characteristics, the individual points are summed up and constitute the final score. Within each scoring model, a cut-off point is also defined, which illustrates the border between “good” and “bad” borrowers. Achieving a higher credit score is pivotal as it opens the door to a plethora of benefits, including access to a wider array of credit products at more favorable interest rates. A higher credit score can significantly enhance loan terms, offering lower interest rates and even the possibility of 0% financing on cars and credit cards, which underscores the importance of striving for a higher credit score. However, the scale of points possible to obtain is often divided into credit score ranges, which makes it possible to maintain a separate approach to the customer depending on the level of estimated risk. These ranges are crucial in determining creditworthiness, as they influence loan approvals, interest rates, and access to credit products, emphasizing the importance of understanding the specific score range used by different creditors and scoring models.
However, the question is why specific characteristics are taken into account in a given scoring model and on what basis specific points are assigned to particular attributes. Building a model or a scorecard is not a trivial activity, as it requires expert knowledge of mathematics or statistics. Each bank has data on loans granted and their repayment. This data is divided into groups of ”good” and “bad” loans, and then within these groups sets of data are determined. Those sets will be used to build a scoring model, and to verify its effectiveness.
Credit report data
The scope of analyzed data depends on the type of information previously collected and archived by a given institution and the type of scoring used (application- or behavior-based). Using various statistical techniques or data mining, risk departments try to determine which parameters have a significant impact on the probability of a default. For years, logistic regression has been the most popular method used for building scoring models, but this area also uses decision trees, genetic algorithms, neural networks, and other AI or machine learning methods. Additionally, the integration of risk assessment tools such as decision trees and neural networks enhances the analysis of credit report data, employing techniques like risk matrix, failure modes and effects analysis (FMEA), and bowtie model to review and update assessments based on changing conditions. Therefore, specialist statistical software or data mining is most often used for creating scoring models.
Creating a scoring model and building a scorecard is the first stage in the full life cycle of this risk assessment method. Before it is fully utilized in credit processes, it is verified, most often under the Champion-Challenger approach, where the new model works alongside the current one and the metrics of effectiveness of both variants are checked. If the new model is more efficient, it becomes a Champion and is used for determining new customer risk. In order to streamline the scoring calculation process, credit processes most often use scoring engines, i.e. IT solutions based on business rules processing. Such systems also provide the ability to monitor the effectiveness and efficiency of scoring models implemented. This is important as the risk assessment should be adjusted to the current internal procedures of the bank and the general economic and demographic situation on a given market.
Credit scoring models software
Banks need to use efficient tools to best assess the creditworthiness of their customers and to reduce the risk of potential insolvency. Therefore, more and more often they use credit scoring software, which analyzes critical types of information like credit accounts, allowing for determining the risk of an undesirable event such as a defaulted loan. This software evaluates factors including credit card balances, emphasizing the importance of managing these balances for a favorable credit score. Such solutions are most often one of the key elements of the entire system infrastructure supporting the bank’s employees in the credit granting processes. Additionally, credit scoring models software aids in conducting thorough risk assessments by evaluating various factors like credit card balances and credit accounts, enhancing the bank's ability to identify and mitigate potential risks effectively.
Advantages of scoring tools:
- significant shortening of risk assessment processes and improvement of calculation efficiency. Creditworthiness assessment processes may be accelerated by as much as 75 per cent by selecting the appropriate software in the bank and ensure a multiple increase in loan sales.
- faster and more effective assessment of creditworthiness, minimizing the number of risky loans granted
- better efficiency with the same employment - credit scoring software allows to streamline credit processes without additional human resources, i.e. the need to hire additional employees
- automation of the credit assessment process, which also reduces the risk of human error and potential fraud
- integration with other systems provides the possibility of using a wide range of data but also allows to determine the scoring within different systems or business processes
- minimizing the risk of a wrong decision or granting a loan on conditions unfavorable to the bankcomprehensive credit policy management and its constant monitoring – all data and scoring calculations are performed and stored in one place.
- comprehensive credit policy management and its constant monitoring – all data and scoring calculations are performed and stored in one place.
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