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How to Guide – Underwriting

How to Create an AI-Process for Underwriting

Artificial intelligence (AI) is transforming the underwriting process in several ways. AI-powered underwriting tools can help insurers to:

Automate routine tasks, freeing up underwriters to focus on more complex risks.

Improve the accuracy and consistency of underwriting decisions.

Identify new risk factors and develop more tailored pricing models.

Offer more personalized and proactive coverage to customers.

To create an AI-process for underwriting, insurers need to follow these steps:

Artificial intelligence (AI) is transforming the underwriting process in several ways. AI-powered underwriting tools can help insurers to:

Identify the underwriting tasks that can be automated

Some good candidates for automation include:

  • Data entry and validation
  • Risk assessment
  • Pricing
  • Policy issuance and renewal

Gather data

AI models are trained on data, so it is important to gather a large and comprehensive dataset of underwriting data. This data should include information on past claims, risk factors, and pricing decisions.

Choose the right AI technology

There are a number of different AI technologies available, such as machine learning, deep learning, and natural language processing. The best technology for a particular underwriting task will depend on the nature of the task and the type of data available.

Develop and train the AI model

Once the data has been gathered and the AI technology has been chosen, the next step is to develop and train the AI model. This involves feeding the model the underwriting data and allowing it to learn from the data.

Deploy and monitor the AI model

Once the AI model has been trained, it can be deployed to production. However, it is important to monitor the performance of the model over time and make adjustments as needed.

Technical Details

The technical details of creating an AI-process for underwriting will vary depending on the specific underwriting tasks that are being automated and the AI technology that is being used. However, there are some general technical considerations that all insurers should keep in mind:

Data preparation

Data preparation

The underwriting data should be cleaned and preprocessed before it can be fed to the AI model. This may involve removing outliers, correcting errors, and normalizing the data.

Feature engineering

Feature engineering

Feature engineering is the process of creating new features from the existing data. This can be done to improve the performance of the AI model.

Model selection

Model selection

There are a number of different AI models available, such as linear regression, logistic regression, and decision trees. The best model for a particular underwriting task will depend on the nature of the task and the type of data available.

Model hyperparameter tuning

Model hyperparameter tuning

Once an AI model has been selected, its hyperparameters need to be tuned. Hyperparameters are the parameters that control the learning process of the AI model.

Model evaluation

Model evaluation

Once the AI model has been trained, it needs to be evaluated on a held-out dataset to assess its performance.

Expected Outputs

The expected outputs of an AI-process for underwriting will vary depending on the specific underwriting tasks that are being automated. However, some common expected outputs include:

Increased efficiency

Risk scores

AI models can be used to generate risk scores for each applicant. These risk scores can be used to price policies and make underwriting decisions.

Pricing recommendations

AI models can be used to generate pricing recommendations for each policy. These pricing recommendations can help insurers to set competitive prices and maintain profitability.

Policy issuance and renewal decisions

AI models can be used to make decisions about whether to issue or renew a policy. These decisions can be based on the applicant's risk score, pricing, and other factors.

Data Preparation and Feature Engineering

Data preparation and feature engineering are two essential steps in creating an AI-process for underwriting.

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Data Preparation

The goal of data preparation is to clean and preprocess the underwriting data so that it can be used effectively by the AI model. This may involve the following steps:

  • Removing outliers: Outliers are data points that are significantly different from the rest of the data. They can skew the results of the AI model, so it is important to remove them.
  • Correcting errors: The underwriting data may contain errors, such as typos or inconsistencies. It is important to correct these errors before feeding the data to the AI model.
  • Normalizing the data: Normalizing the data involves scaling the data so that all the features have a similar range of values. This makes it easier for the AI model to learn from the data.

Feature Engineering

Feature engineering is the process of creating new features from existing data. This can be done to improve the performance of the AI model. For example, an insurer might create a new feature that represents the number of years of driving experience for an auto insurance applicant. This feature could be created by combining the applicant’s date of birth and the date they first obtained a driver’s license.

Some examples of feature engineering techniques that can be used for underwriting include:

  • Binning: Binning involves dividing the data into several bins. For example, an insurer might bin the underwriting data for auto insurance applicants by age group.
  • One-hot encoding: One-hot encoding is a technique for representing categorical features as numerical features. For example, an insurer might one-hot encode the state of residence feature for auto insurance applicants.
  • Feature scaling: Feature scaling is a technique for normalizing the data so that all the features have a similar range of values. This can be done using techniques such as min-max scaling and standard scaling.
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Different AI Models and Which is Best for Underwriting

There are several different AI models that can be used for underwriting. The best model for a particular underwriting task will depend on the nature of the task and the type of data available.

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Some common AI models that are used for underwriting include:

Linear regression is a simple but powerful AI model that can be used to predict continuous variables. For example, linear regression could be used to predict the amount of a claim for an auto insurance policy.

Logistic regression is an AI model that can be used to predict binary variables. For example, logistic regression could be used to predict whether an auto insurance claimant is likely to file a fraudulent claim.

Decision trees are AI models that can be used to classify data. For example, a decision tree could be used to classify auto insurance applicants into different risk categories.

Random forests are an ensemble learning method that combines the predictions of multiple decision trees to improve accuracy. Random forests are often used for underwriting because they are very robust to noise in the data.

Gradient boosting machines are another ensemble learning method that can be used for underwriting. Gradient boosting machines build a series of weak learners, such as decision trees, and then combine them to produce a strong learner.

Neural networks are a type of AI model that can learn complex patterns in the data. Neural networks are often used for underwriting because they can be very accurate, even when the data is noisy and complex.

The best AI model for underwriting will vary depending on the specific underwriting task and the type of data available. However, in general, random forests and gradient boosting machines are good choices for underwriting because they are very robust to noise in the data and can learn complex relationships between the features. Neural networks can also be a good choice for underwriting, but they require more training data and can be more difficult to interpret.

Conclusion

Creating an AI-process for underwriting can be a complex and challenging task. However, the potential benefits are significant. AI-powered underwriting tools can help insurers to automate routine tasks, improve the accuracy and consistency of underwriting decisions, identify new risk factors, and develop more tailored pricing models.

Insurers that are considering creating an AI-process for underwriting should start by identifying the underwriting tasks that can be automated and gathering the necessary data. Once the data has been gathered, the next step is to choose the right AI technology and develop and train the AI model. Finally, the AI model should be deployed and monitored to ensure that it is performing as expected.

If you are interested in a PoC for AI-driven underwriting, please fill out the following form

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