Improving Loan Origination Process with Risk Analytics
Company Size
1,000+
Region
- America
Country
- United States
Product
- Risk-based loan application analytics solution
- Performance management solution
- Blueocean Market Intelligence lending portal
Tech Stack
- Data Management
- APIs
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Finance & Insurance
Applicable Functions
- Procurement
Use Cases
- Predictive Quality Analytics
- Regulatory Compliance Monitoring
Services
- Data Science Services
- System Integration
About The Customer
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.
The Challenge
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.
The Solution
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.
Operational Impact
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