Effective Credit Scoring with Self-Developed Decision Support
公司规模
11-200
地区
- Europe
国家
- Denmark
- Estonia
- Finland
- Norway
- Sweden
产品
- STATISTICA
技术栈
- Logistic Regression
- MARSplines
- Boosted-trees
- Data Mining
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 数据挖掘
- 分析与建模 - 预测分析
- 分析与建模 - 实时分析
适用行业
- 金融与保险
适用功能
- 商业运营
用例
- 欺诈识别
服务
- 软件设计与工程服务
- 系统集成
关于客户
Folkia, established in Norway in 2006, operates in Sweden, Finland, Denmark, and Estonia. The company specializes in providing short-term loans and aims to offer economical and effective financial services to its customers. Folkia's primary focus is on selecting the right customers to minimize credit risk and ensure financial stability. The company collaborates with consulting firms and utilizes advanced software tools to develop customized solutions for its credit scoring needs. With a commitment to innovation and efficiency, Folkia continuously seeks to improve its decision-making processes and maintain a competitive edge in the financial services industry.
挑战
Folkia, a company providing short-term loans, faced the challenge of accurately selecting customers to minimize credit risk. The company needed a robust credit decision support system to improve its credit scoring model. The existing methods were not sufficient to discriminate effectively between good and bad customers, leading to potential financial risks. Additionally, maintaining and updating the system was cumbersome, requiring manual changes and extensive data structuring. Folkia aimed to develop a more efficient, accurate, and easy-to-maintain system to enhance its credit decision-making process.
解决方案
Folkia developed a customized credit decision support system in collaboration with the consulting firm Aregab, utilizing STATISTICA software from StatSoft. The software's powerful data management and analytical capabilities, including data mining, enabled the creation of multiple models using different statistical methods such as logistic regression, MARSplines, and boosted-trees. These models were tested against each other to determine the most effective one. The system's graphical presentation at every step, from selection to testing, simplified the process and enhanced efficiency. The completed score model effectively rated customers by credit risk, allowing Folkia to make informed decisions and treat high-risk customers differently. The system's easy maintenance and quick development, facilitated by the alliance with Aregab, further contributed to its success.
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