How AI Reshapes Operational Accuracy in Insurance and Finance

GUEST POST by: Lisen Kaci, Founder & CEO at Discrepancy AI

AI models are tested to make sure they can perform accurately, fairly, and reliably.

Product validation accuracy reflects how well a model can perform under the balanced conditions used to make and test new models. Operational accuracy shows how the model performs once it’s in the real world. For example, product validation accuracy might test a photo recognition tool using clear, well lit photographs.

Operational accuracy looks at whether that same tool will perform accurately on blurry, imperfect snapshots. A model can identify monarch butterflies with 99.9% accuracy in nature photos from magazines, but might label the butterfly as a washing machine in real life. 

For insurance and finance companies, operational accuracy is more than a buzzword.

Insurers surveyed by KPMG say they feel behind the curve in regards to operational efficiency, with more insurers focusing on refining their processes, implementing lower cost servicing channels, and making legacy system repairs.

To help enhance these processes and streamline operations, AI models need to be consistent – and accurate!

Where Operational Accuracy Matters in Insurance and Finance 

Mortgage brokers, insurance companies, and finance professionals all rely on enormous amounts of data, including data from messy, unstructured sources.

AI that works perfectly in a testing environment might struggle with noise, inconsistent data, and formatting in real life. 

Accurately extracting and classifying data for banking and claims, for example, is important when working with documents that aren’t standardized.

Identity documents required to comply with anti-money laundering legislation (AML) and Know Your Customer (KYC) rules are sensitive, require careful review for compliance, and are not always the same.

Income and employment documents represent some of the 80% of corporate documents that are unstructured – and small variations, like differences in drivers license by state, can make operational accuracy more difficult outside of the testing environment.  

Where Operational Accuracy Matters in Insurance and Finance 

AI models can save humans a lot of time – but if humans can’t trust them, they can’t help.

When AI can’t reliably process these documents, human oversight can’t be replaced! 

When human knowledge workers are spending all of their time working on tedious, manual tasks, they have less time to devote to the highly skilled, problem solving part of their jobs; in order to eliminate the most manual components of the work, AI models need to process this information accurately. 

Replacing older, outdated forms of AI (like optical character recognition, or OCR) helps with operational accuracy.

Newer forms of text and character recognition, like Natural Language Programming (NLP) can look at information in context – for example, spotting subtle abnormalities that would signal fraud in KYC or AML documents.

Being able to rely on AI models for these sensitive tasks helps ensure compliant processing without using your human workforce to check, re-check, and examine these documents. 

AI models can save humans a lot of time – but if humans can’t trust them, they don’t help!

As organizations refine their AI models and adapt legacy tech to modern needs, operational accuracy will become a critical component in how, and where, organizations can stay ahead of the curve in efficiency.