Improving Loan Origination Process with Risk Analytics
公司规模
1,000+
地区
- America
国家
- United States
产品
- Risk-based loan application analytics solution
- Performance management solution
- Blueocean Market Intelligence lending portal
技术栈
- Data Management
- APIs
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 预测分析
- 应用基础设施与中间件 - 数据交换与集成
适用行业
- 金融与保险
适用功能
- 采购
用例
- 质量预测分析
- 监管合规监控
服务
- 数据科学服务
- 系统集成
关于客户
The customer is a US-based auto finance company. They are in the financial services industry and have a large scale operation, with over 1,000 employees. The company was facing challenges in managing risks, profitability, and growth due to their existing practice of approving all loan applications at the origination stage. They desired to build a new risk-based loan application analytics solution to better understand their existing customers and manage their portfolio more effectively.
挑战
The client, a US-based auto finance company, was approving all loan applications at the origination stage. They desired to build a new risk-based loan application analytics solution to better understand their existing customers and manage risks, profitability, and growth more effectively. The challenge was to profile, model, extract, and automate data into one repository for the loan origination process. This included statistical evaluation of the borrowers’ characteristics, risk-based pricing model based on the riskiness of the customer, and real-time decision-making application for monitoring performance, decision support, and automated scoring of customers.
解决方案
Blueocean Market Intelligence provided a comprehensive solution to the client's challenges. They profiled, modelled, extracted, and automated data into one repository for the loan origination process. This included a risk score/risk segmentation for statistical evaluation of the borrowers’ characteristics, a risk-based pricing model based on the riskiness of the customer, and a real-time decision-making application for monitoring performance, decision support, and automated scoring of customers. They also built APIs to connect to multiple data sources and used the information to make decisions in real time. Additionally, they created a performance management solution where key KPIs and metrics were tracked to understand how the overall portfolio is doing.
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