December 13, 2022

๐Ÿ“„ Applying Machine Learning to Actuarial Pricing Workflows

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December 13, 2022

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โ€Incorporating machine learning techniques into pricing applications

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Machine learning has opened the door to new possibilities for actuarial and insurance pricing practices. Applying complex algorithms to processes built upon the idea of transparency and judgment has been quite controversial. However, these concepts can be highly complementary if machine learning is implemented correctly.

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One common pushback to employing modeling or machine learning techniques is the lack of sufficient data. While many insurers are tempted to reject the idea of using machine learning techniques for actuarial pricing, the reality is that machine learning can be used to obtain the best data-driven starting point for additional analysis. The use of machine learning algorithms can begin when a team is working with small datasets and will evolve as the team and company grow.

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The Consequences of the Black Box

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Aside from the necessity of incorporating machine learning techniques to actuarial and pricing practices,ย  it is important to recognize the prerequisite, which is transparent machine learning. A user must have the ability to determine which model is the most appropriate for implementation given business considerations.

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This differentiation is quite difficult with black box models, as it may not immediately be clear what is happening within the black box to cause the models to produce different scores for similar risks. While black box algorithms may create models with good performance, the ability to identify, interpret, and adjust their inner workings is unavailable, resulting in little to no potential for a full investigation and operational adjustment of the model.

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Transparency and algorithmic bias

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Lastly, machine learning cannot be discussed without also acknowledging the topic of algorithmic bias. While it is uncertain how the industry will move forward to address issues of equity and disparate impact, transparent machine learning clearly has an advantage over black box methodologies in the ability to be explained and validated.

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Transparent machine learning algorithms will still be as biased as the data going into them - but this bias is not hidden by black box methodology. Transparent machine learning algorithms are necessary for insurance companies and regulatory bodies that want to address possible biases in insurance models.

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Conclusion

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Machine learning is quickly entering the insurance space, and actuaries can benefit greatly by incorporating it into pricing processes. Expanding the implementation of machine learning analysis will open exciting new opportunities. Beginning with an easy-to-use platform for machine learning will allow actuaries to take advantage of machine learning capabilities, and use it to its fullest potential.

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