Case Study
Advanced Analytics and Business
Intelligence in CFPB Complaint Analysis
Advanced NLP, Machine Learning, and Business Intelligence to
automate the classification and analysis of CFPB complaint data.
automate the classification and analysis of CFPB complaint data.
At a Glance
This case study highlights how we automated the classification and analysis of mortgage-related complaints from the CFPB dataset using NLP, Machine Learning, and Business Intelligence. The project addressed challenges like data inconsistency, scalability, and manual inefficiencies by building an AI-driven solution. As a result, the client achieved faster fraud detection, enhanced regulatory compliance, and significantly improved operational efficiency in complaint management.
Key Highlights
Automated
Complaint
Classification
Complaint
Classification
High
Accuracy
Achievement
Accuracy
Achievement
Massive
Reduction in
Processing Time
Reduction in
Processing Time
Scalable AI-
Powered
Solution
Powered
Solution
Proactive Fraud
Detection &
Risk Monitoring
Detection &
Risk Monitoring
Real-Time
Business
Intelligence
Business
Intelligence
Challenges
- The CFPB dataset contains complaints and makes manual classification unfeasible.
- Advanced NLP for accurate contextual understanding is required for unstructured and varied consumer complaint text.
- Traditional keyword-based methods could not capture the complaints effectively.
- Problems like pagination complexities and SSL certificate errors delayed direct API integration.
- To categorize millions of complaints, require AI-driven solutions.
About Client
The client is a leading organization in the financial services sector, committed to improving customer experience and regulatory compliance. With a focus on consumer protection and fraud monitoring, the client handles large volumes of customer complaints and data to ensure transparent and efficient financial practices.
Financial Services
Industry
Consumer Protection & Compliance
Domain
USA
Location
Retail
Industry
Workforce Management
Domain
USA
Location
How We Worked?
We designed and implemented an AI-driven solution using NLP, ML, and BI tools to automate complaint classification and trend analysis. By addressing data inconsistencies, API challenges, and scalability needs, we built an accurate, real-time, and scalable system for efficient complaint handling.
Key Highlights of the Solution
Advanced NLP Implementation: Utilized BERT, TF-IDF, and word embeddings for accurate complaint classification.
AI-Powered Thematic Clustering: Applied KNN and K-Means clustering to detect and group similar complaint themes.
Automated Data Extraction & Cleaning: Built robust pipelines for API-based data retrieval, cleaning, and preprocessing.
Scalable Complaint Processing Pipeline: Processed millions of complaints with reduced latency and high efficiency.
Interactive BI Dashboards: Developed real-time visual analytics using Azure ML Studio and Power BI.
End-to-End Automation: Eliminated manual effort by 99% through complete automation of classification and reporting workflows.
Technologies
Results
- Similarity detection and automated clustering enabled early identification of risks and proactive monitoring of fraud trends.
- Reduced complaint processing time from 36 seconds to just a few milliseconds per complaint.
- Successfully processed millions of complaints using robust NLP pipelines integrated with Azure AutoML.
- Achieved 95% accuracy in thematic complaint classification using advanced NLP and ML models, significantly outperforming the previous 60-70% accuracy of rule-based methods.
Fraud Detection
Automated clustering enabled early risk and fraud trend identification.
Fast & Scalable
Reduced processing time to milliseconds while handling millions of complaints.
High Accuracy
Achieved 95% classification accuracy using advanced NLP and ML models.
Conclusion
By integrating advanced NLP, Machine Learning, and Business Intelligence, we transformed the complaint analysis process into a highly accurate, scalable, and automated system. This solution not only improved fraud detection and regulatory compliance but also enabled faster and smarter decision-making, positioning the client for long-term operational excellence.
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