Automating Classification of Customer Queries

Improving Efficiency and Quality at Alpha Bank's Machine Learning Lab

Slide1: Introduction

  • Presenting the problem of categorizing customer queries at Alpha Bank
  • Discussing the challenges and complexities involved in the process
  • Highlighting the importance of automating the classification task
  • Exploring the benefits of automation for both employees and customers

Slide2: Classification Process

  • Describing the communication process between customers and bank employees
  • Explaining the need for a unified classifier and its hierarchical structure
  • Highlighting the importance of accurate classification for better decision-making
  • Discussing the various thematic categories and their relevance to different banking products

Slide3: Data Analysis and Reporting

  • Analyzing the collected data to generate insights and reports
  • Examining the distribution of thematic categories over time
  • Identifying trends and anomalies in customer queries
  • Exploring the use of data analysis for making informed management decisions

Slide4: Modeling and Improvement

  • Introducing the task of text classification using machine learning
  • Exploring different modeling approaches and techniques
  • Discussing the challenges faced and possible solutions
  • Presenting the results of initial modeling experiments and their implications

Slide5: Data Annotation and Quality Control

  • Describing the process of data annotation by human experts
  • Explaining the need for quality control measures
  • Discussing the benefits of cross-verification and feedback
  • Presenting the results of data annotation and the achieved quality

Slide6: Incorporating User Feedback

  • Addressing the challenge of incorporating user feedback into the classification process
  • Exploring the use of customer's existing banking products as features
  • Highlighting the impact of feature inclusion on model performance
  • Presenting the results and demonstrating the improved accuracy

Slide7: Pilot Implementation

  • Describing the pilot implementation of the automated classification system
  • Discussing the results and benefits achieved
  • Highlighting the reduction in manual classification workload
  • Presenting the improved accuracy in comparison to human operators

Slide8: Conclusion

  • Summarizing the key points and achievements
  • Highlighting the importance of automation in improving efficiency and quality
  • Addressing the future scope for further improvements
  • Encouraging the adoption of automated classification systems in similar contexts

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