Case study: report on credit risk

Data Communication Concepts

Hadrien Lacroix

Curriculum Manager

Credit risk

• Credit risk: probability of defaulting
• Loanme bank wants to predict if a customer is likely to default
• Raw data available
• Data Exploration Analysis
• Model training and evaluation

Audience

• Non-technical stakeholders
• Bank decision-makers

Story

 

• Background:
  • Increase in defaulting percentage over last 5 years.
  • Predicting which customers had a high probability of default.
• Insight: People with more unemployment periods tends to default more
• Insight: People with lower income tend to default more
• Climax: Possible to predict which people is more likely to default with an accuracy of 95%
• Next steps: Run a trial on a control population

Tech or non-tech

 

• Translate technical results

The right data

 

• Audience persona
  • Role: Financing Department Director
  • Interest: Decision on implementing an automated loan rejection system
  • Appropriate data:
    • Relationship between age or income and loan default
    • Percentage customer defaulting over the next months

Statistics

 

• Median age and income
• Percentage of change

Visuals

 

• Boxplot with age vs. default condition

Visuals

 

• Boxplot with age vs. default condition
• Lineplot with % change defaulting customers

Correct format

 

• Who? Financial Department director
• Why? Important decisions ahead
• Content: Key findings and recommendations
• Channel: Send the results before the meeting

Report

• Written report
• Summary report or final report?

Report

• Summary report
• Informational report vs. analytical report?

Report

• Summary report
• Analytical report

Summary report structure

• Introduction
  • Purpose
  • Contextual information
  • Question of analysis
• Body
  • Data
  • Results: Key findings
• Conclusions
  • Restate question
  • Central insight
  • Add recommendations

Let's practice!

Data Communication Concepts