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The Automation Adaptability Framework

Updated: May 21, 2022

Unstructured media, like documents, recordings, and systems notes, can be structured into useful datasets. Artificial Intelligence relies on Machine Learning to interpret the unstructured media. The unstructured media provided for testing maybe newer and thus, the AI is less trained. As a result, the unstructured media may be rated as a lower accuracy probability. Alternatively, the unstructured media may be regularly encountered, resulting in a higher trained AI. In this case, the unstructured media may be rated as a higher accuracy probability.

The following diagram describes typical loan products most adaptable to automation (on the right side of the axis), as opposed to those least adaptable to automation. (to the left) Generally, higher volume, homogenous products will be more adaptable to automation.

Keep in mind, while the loan product categories maybe fixed along this continuum, individual loans are not necessarily fixed. That is, the idea is to transform existing loan products to “move to the right” on the continuum. This will enable lower costs, lower prices, higher efficiency and higher customer delight.

Below are the loan products and their related features. These features help dimension the products and their relationship to automation adaptability.

By the way, there is a practical way to understand your organizations potential need for automation. Walk through your back office or call center operations areas. How many staff members have two monitors on their desks? How many are engaged in reading information found in one system and moving it to another application? (aka, “stare and compare”)

The volume of resources involved in this activity is the starting point for your business case.


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