AIF-C01 Question 188
Single answerA data science team is tasked with building an AI model for a financial institution to predict loan defaults. The institution requires the model to provide clear reasons for its predictions to comply with regulatory requirements. Which type of model should the team choose, and why?
- A
A complex deep learning model, such as a neural network, because it delivers the highest accuracy and is inherently explainable.
- B
A decision tree-based model, such as XGBoost, because it offers a balance of accuracy and explainability.
- C
A black-box model, such as a support vector machine (SVM), because it handles non-linear data well but does not require explainability.
- D
A simple linear regression model, because it is inherently transparent but may sacrifice predictive power.
Show answer and explanation
Correct answer: B
Explanation
In scenarios requiring explainability, such as regulatory compliance, the choice of a model must balance predictive performance and interpretability. Decision tree-based models like XGBoost are a strong option in such cases because they provide high accuracy while remaining explainable with tools like SHAP. This makes them suitable for financial use cases where transparent decision-making is critical.
- A. Incorrect.
Complex deep learning models, such as neural networks, often deliver high accuracy but are not inherently explainable. They are considered black-box models, which means their internal decision-making process is difficult to interpret, making them unsuitable for regulatory environments requiring transparency.
- B. Correct.
Decision tree-based models, such as XGBoost, provide a good compromise between accuracy and explainability. While boosted tree models are more complex than single decision trees, tools like SHAP (SHapley Additive exPlanations) can be used to make their predictions interpretable, making them a suitable choice for compliance needs.
- C. Incorrect.
Black-box models like support vector machines (SVMs) may handle non-linear data effectively but lack transparency. They are difficult to interpret and are not ideal for organizations that require explainability to meet regulatory or compliance standards.
- D. Incorrect.
A simple linear regression model is inherently transparent and explainable. However, it may not provide sufficient predictive power for complex datasets, such as those found in financial risk assessment, which makes it less desirable in cases requiring both accuracy and interpretability.