Predictive Model for Tax Audit Risk

I'm looking for a data scientist with a strong background in predictive modeling and risk assessment. The primary goal of this project is to develop a model that identifies high-risk clients for tax audit insurance.

Data Available:
- Historical audit data
- Customer demographic data
- Accountant specific data
- Entity level data

Ideal Skills & Experience:
- Proficiency in predictive modeling
- Experience with risk assessment and data analysis
- Ability to work with diverse data types
- Familiarity with tax audit insurance data is a plus.

More info:

Core Model Requirements:
Data Sources:

Accountant-Specific Data:
Criminal history, audit history, industry body affiliations, portfolio structure, practice size, and number of partners.
Business Entity Data:
Entity type (sole trader, trust, partnership), number of related entities, group turnover, geographic location, financial history, and compliance records.
Financial Data:
Prior audit history, compliance history, and client portfolio complexity.
Third-Party Data Integration:
External datasets such as industry benchmarks, tax policy data, and historical audit outcomes relevant to business size and location.
Feature Selection:

Variable Weighting: Develop and assign weights for each structured variable based on historical correlation with audit frequency and severity.
Accountant-Specific Variables: Test and refine weights for factors such as audit history, practice size, and industry body affiliations.
Entity-Specific Variables: Assign weights to entity-specific factors (e.g., entity type, number of related entities, group turnover) based on their correlation with audit risk.
Model Design:

Probabilistic Risk Scoring:
Assign a probability score to each variable based on its correlation with audit outcomes, combining them to generate an overall risk score.
Use probabilistic frameworks (e.g., Bayesian Networks) to compute weighted scores, yielding an audit risk prediction range (e.g., low, medium, high risk).
Algorithm Selection:
Utilize machine learning algorithms like Logistic Regression, Decision Trees, or Random Forests to analyze structured data and generate risk scores.
Training and Validation:
Train the model on historical audit and financial datasets, refining the weights for each variable based on performance.
Validate the model using cross-validation techniques to ensure accuracy and reliability in predicting audit risks.
Data Preprocessing:

Data Cleansing and Normalization: Ensure structured data is cleansed (removing duplicates, resolving inconsistencies) and normalized for consistent input across various sources.
Missing Data Handling: Implement imputation techniques to manage any gaps or missing fields in the structured data, ensuring complete input sets for accurate predictions.
Feature Engineering: Derive new features from the structured data, such as ratios (e.g., revenue to expense ratio) or historical trends, to enhance model performance.
Model Outputs:

Risk Score Generation: The model will output a risk score (0-100 scale) for each accountant or entity, where a higher score indicates a greater likelihood of a tax audit.
Risk Explanation: Provide a breakdown of the key variables that contributed to the risk score, offering clear insights into the factors driving the prediction.
Actionable Insights: Deliver recommendations based on the model’s output, such as changes to compliance practices or tax filing behaviors to mitigate audit risks.
Deployment and Scalability:

API Integration: The model must be capable of integrating seamlessly with third-party APIs (e.g., Xero) to facilitate rapid data ingestion from financial systems.
Cloud Infrastructure: Deploy the model on a scalable cloud infrastructure that can handle increasing volumes of structured data from growing client portfolios.
Monitoring and Reporting: Build in monitoring tools to track model performance and accuracy, with reporting capabilities for stakeholders on risk trends and audit risk predictions.
Compliance and Security:

Deliverables:

A fully functional predictive audit risk model that utilizes structured data to assess and score tax audit risks.
Integration with structured datasets, including accountant-specific and entity-specific data, for real-time risk predictions.
Clear and actionable risk scores for accountants and businesses, with explanations of key drivers for each prediction.
A scalable model that can process growing volumes of structured data, while maintaining accuracy and performance.
Complete documentation detailing model architecture, variable weighting, and validation methods for future updates and improvements.


Please provide examples of similar projects you've completed in your proposal.