You are an ML engineer at a regulated insurance company.
You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers.
What factors should you consider before building the model?
Click on the arrows to vote for the correct answer
A. B. C. D.A.
As an ML engineer at a regulated insurance company tasked with building an insurance approval model, there are several factors you should consider before building the model. The correct answer is B. Traceability, reproducibility, and explainability.
Here is an explanation of each of these factors:
Traceability: In the insurance industry, it is crucial to be able to trace the decisions made by the model back to the data used and the logic implemented in the model. Traceability is important for accountability and transparency purposes, as well as to ensure that the model is making decisions in a fair and unbiased manner. This means that you should be able to track the data sources, data preprocessing steps, model training, and testing processes.
Reproducibility: Reproducibility is critical for ensuring that the model can be tested and validated consistently over time. This means that the model should be built in a way that allows for replication of results. One way to ensure reproducibility is to document the code and model architecture, as well as the data sources and preprocessing steps. Additionally, it is crucial to use version control to track changes made to the model and the data over time.
Explainability: In the insurance industry, it is essential to be able to explain the decisions made by the model in a way that is understandable to stakeholders. This means that the model should be interpretable and transparent, and the decision-making process should be clear. You should choose a model that is inherently interpretable, or you should use techniques such as feature importance analysis and model-agnostic explainability methods to explain the model's decisions.
In summary, before building an insurance approval model, you should consider traceability, reproducibility, and explainability to ensure that the model is transparent, fair, and unbiased.