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AI-Powered Proactive Credit Scoring System Development
  1. case
  2. AI-Powered Proactive Credit Scoring System Development

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AI-Powered Proactive Credit Scoring System Development

miquido.com
Financial services
Information technology
Business services

Challenges in Traditional Credit Scoring

Traditional credit scoring systems suffer from three core limitations: 1) Lack of transparency in scoring calculations leading to potential biases and errors, 2) Reactive approach only triggered by customer applications, and 3) Limited predictive power due to static models that fail to consider broader economic factors and individual circumstances.

About the Client

Fintech company providing cloud-based banking software solutions with focus on AI-driven credit assessment

Key Project Goals

  • Develop transparent AI/ML-based credit scoring engine with 97%+ prediction accuracy
  • Implement proactive customer identification system for revenue expansion
  • Create scalable microservices architecture for real-time risk assessment
  • Integrate 600+ data points for comprehensive creditworthiness analysis

System Capabilities

  • Automated credit scoring using machine learning models
  • Multi-source data integration (demographics, transaction history, credit history)
  • Real-time risk assessment API for loan applications
  • Proactive customer identification engine
  • Bias mitigation through historical repayment pattern analysis
  • Cloud-native microservices architecture with REST API

Technology Stack

Python
Pandas
PySpark
AWS SageMaker
LightGBM
XGBoost

System Integrations

  • Third-party data providers (credit bureaus, financial institutions)
  • Banking core systems APIs
  • Real-time payment verification services
  • Customer relationship management platforms

Operational Requirements

  • Horizontal scalability for 1M+ daily loan applications
  • Sub-200ms response time for scoring requests
  • 99.99% system availability with failover mechanisms
  • GDPR-compliant data processing and storage
  • Model retraining pipeline with drift detection

Expected Business Outcomes

Deployment of this AI-powered system is projected to enable financial institutions to process loan applications with 97% accuracy, reduce manual review requirements by 85%, and expand credit access to 20% more previously underserved customers through proactive identification. The solution's scalability allows processing of 500M+ loan applications annually while maintaining real-time decisioning capabilities.

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