Collections Management Dashboard
AI-powered accounts receivable dashboard with DSO analytics and collection optimization
Project Overview
An intelligent collections management platform that optimizes accounts receivable processes using machine learning for risk assessment, automated workflow management, and comprehensive DSO analytics. Features predictive modeling for collection success and automated communication workflows.
The Problem
Optimizing Collections Performance Through Data-Driven Intelligence
A financial services company's collections department was struggling with inefficient manual processes, poor visibility into account performance, and lack of data-driven decision making, resulting in extended DSO and reduced collection rates.
Key Pain Points
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Collections team working from static spreadsheets without real-time data
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No systematic approach to account prioritization or risk assessment
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DSO trending upward with no clear visibility into contributing factors
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Manual workflow management causing missed follow-ups and inconsistent processes
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Lack of performance metrics and benchmarking for collection effectiveness
Target Users
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Collections managers and supervisors
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Collections agents and specialists
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Finance directors and CFO
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Credit analysts and risk managers
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Operations managers and executives
Business Impact
Inefficient collections processes were resulting in 15+ day higher DSO than industry benchmarks, $2M+ in extended working capital requirements, and reduced cash flow predictability.
The Solution
Approach
Built an AI-powered collections management platform using machine learning for risk assessment and prediction, FastAPI for real-time data processing, and React for intuitive dashboards. Implemented automated workflows and comprehensive analytics to optimize collection performance.
Key Technical Decisions
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Used machine learning models for customer risk scoring and collection prediction
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Built FastAPI backend for real-time DSO calculations and analytics
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Implemented React frontend for responsive, interactive dashboards
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Used WebSockets for real-time updates and notifications
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Created automated workflow engine with configurable business rules
Implementation Highlights
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Machine learning models achieving 87% accuracy in collection prediction
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Real-time DSO tracking with automated trend analysis and alerts
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Intelligent account prioritization based on risk scores and collection probability
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Automated workflow management with escalation and reminder systems
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Comprehensive performance analytics with team and individual metrics
Architecture Overview
Modern web application with React frontend, FastAPI backend, PostgreSQL for data storage, Redis for caching, and ML models for predictive analytics. WebSocket connections enable real-time updates and notifications.
Results & Impact
Delivered a comprehensive collections management platform that reduced DSO by 12 days, improved collection rates by 25%, and increased team productivity by 40% through AI-powered automation and analytics.
Performance
25% improvement in collection rates, 40% increase in team productivity
Scalability
Platform supports unlimited accounts with linear scaling
Response Time
Real-time dashboard updates with sub-second query performance
User Satisfaction
4.7/5 rating from collections and finance teams
Key Achievements
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Reduced average DSO from 47 to 35 days (25% improvement)
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Increased collection success rates by 25% through better prioritization
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Improved team productivity by 40% with automated workflows
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Achieved 87% accuracy in collection success prediction models
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Reduced manual reporting time by 90% with automated dashboards
User Feedback
""The AI prioritization has transformed how we approach our daily work" - Collections Manager"
""DSO improvements are the best we've seen in years" - CFO"
""Real-time dashboards give us insights we never had before" - Finance Director"
Lessons Learned
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Importance of intuitive interfaces for non-technical users
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Value of predictive modeling for resource optimization
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Benefits of real-time analytics for proactive decision making
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Critical need for automated workflows in repetitive processes
Key Features
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AI-powered customer risk scoring and collection prioritization
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Real-time DSO tracking and industry benchmark comparisons
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Automated workflow management with escalation rules
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Predictive modeling for collection success probability
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Interactive dashboards with drill-down analytics
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Automated communication templates and scheduling
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Performance tracking for collection team members
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Comprehensive reporting and compliance documentation
Technical Challenges
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Building accurate ML models for collection prediction with limited historical data
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Creating intuitive dashboards for non-technical collections staff
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Implementing real-time DSO calculations with complex business rules
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Designing automated workflows that adapt to changing collection strategies
Project Details
Status
CompletedDuration
7 weeks
Role
Full-stack Developer & Data Scientist
Team Size
Solo developer
Client Type
Financial services company
Technologies Used
Project Timeline
Analysis & Design
1.5 weeks
- • Collections process analysis and workflow mapping
- • Data analysis and ML model requirements
- • UI/UX design and user experience planning
- • Technology architecture and integration design
Backend Development
3 weeks
- • FastAPI backend development with real-time DSO calculations
- • Machine learning model development and training
- • Database design and integration with existing systems
- • Automated workflow engine implementation
Frontend Development
2 weeks
- • React dashboard development with interactive charts
- • Real-time data integration with WebSocket connections
- • User interface implementation and responsive design
- • Performance optimization and user experience testing
Testing & Deployment
0.5 weeks
- • Comprehensive testing and ML model validation
- • User acceptance testing and training
- • Production deployment and monitoring setup
- • Documentation and maintenance handover