The strategy focuses on enhancing an analytics approach using logistic regression to detect vehicle insurance fraud. Initially, it involves summarizing the method by reviewing logistic regression's application, documenting dataset specifics, and outlining key risk factors reduced from 33 to 15. Notable indicators include Fault and Policy Type, with a 50% probability threshold for case escalation. Strengths like business focus and model interpretability are highlighted, although weaknesses such as data validation are identified.
In evaluating the strategy, logistic regression's suitability is assessed, and the feature selection process is analyzed for alignment with business needs. The approach scrutinizes model validation concerns, such as train/test splits, emphasizing the need for metrics like accuracy and recall. The evaluation process suggests avenues for analytical insights and improvements, highlighting gaps in feature engineering.
Improvements recommend robust validation through data splits and comprehensive metric calculations, such as precision and F1-score. Introducing advanced techniques like Random Forest and enhancing feature engineering through derived variables are also considered. A systematic data quality framework and real-time model monitoring are suggested, alongside exploring unsupervised learning to bolster fraud detection.
The strategies
⛳️ Strategy 1: Summarise the analytics strategy
- Review the proposed use of logistic regression to detect vehicle insurance fraud
- Document the dataset details, including size and fraud rate
- Outline the reduction of variables from 33 to 15 key risk factors
- Note the top indicators identified: Fault, Policy Type, Vehicle Category, and Address Change Claims
- State the achieved AIC value
- Describe the 50% probability threshold used for case escalation
- Highlight the strengths of the strategy including its practical business focus
- Summarise the interpretability benefits of the model
- Identify weaknesses such as lack of model validation
- List concerns about data quality and decision threshold simplicity
⛳️ Strategy 2: Evaluate the analytics strategy
- Assess the appropriateness of logistic regression for fraud detection
- Evaluate the systematic approach using backward elimination for feature selection
- Identify the effective alignment of probability outputs with business decision needs
- Analyse the model's practical implications as demonstrated in case studies
- Identify gaps in model validation such as train/test splits
- Critique the arbitrary 50% decision threshold
- Examine the quality of data exploration and preprocessing
- Note the absence of performance metrics like accuracy and recall
- Evaluate the limits of using only raw variables without feature engineering
- Determine opportunities for further analytical insights and model improvements
⛳️ Strategy 3: Suggest improvements to the analytics strategy
- Implement a robust validation framework using train, validation, and test data splits
- Calculate comprehensive performance metrics including precision, recall, and F1-score
- Conduct cost-benefit analysis to optimise probability thresholds
- Evaluate and compare advanced modelling techniques such as Random Forest
- Enhance feature engineering with derived variables and interaction terms
- Establish a data quality framework with systematic cleaning and imputation
- Develop a real-time monitoring system for model performance tracking
- Incorporate external data sources like weather and traffic patterns
- Explore unsupervised learning for advanced fraud detection
- Build capabilities for automated model retraining as new data arrives
Bringing accountability to your strategy
It's one thing to have a plan, it's another to stick to it. We hope that the examples above will help you get started with your own strategy, but we also know that it's easy to get lost in the day-to-day effort.
That's why we built Tability: to help you track your progress, keep your team aligned, and make sure you're always moving in the right direction.
Give it a try and see how it can help you bring accountability to your strategy.
