Applying Artificial Intelligence to Premium Balancing

As the insurance industry catapults to a new age, learn how to apply AI to be even more successful

Tunde PanakiJanuary 12, 2017

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At the mention of artificial intelligence (AI), images of a human-like robot may come to mind. Or maybe you think of your most recent conversation with Amazon’s Alexa or Apple’s Siri who are able to mimic certain human inflections. Or the ubiquitous devices that collect an extraordinary amount of information about you. Yes, intelligence exhibited by machines is all examples of artificial intelligence. And while 2016 could arguably be the breakout year for artificial intelligence, how do all of its components work to help people, businesses and industries thrive?

Working in the insurance space, I wanted to learn more about how artificial intelligence could help me, help carriers, and help customers have better experiences. It’s no secret that the insurance world is full of data – but what are we doing with that data and are we using it effectively, if at all?

Applying AI to Insurance

With the recent explosion of technology use within insurance, some areas where artificial intelligence is being applied include:

  • Monitoring driver behaviors
  • Collecting data about policyholders especially in conjunction with wearables and sensors
  • Predicting weather patterns based on extremely complex algorithms
  • Virtual assistants to help with complex policy questions


While just a few examples, what do all these areas have in common? They are all customer facing. Insurance companies have done a good job of applying artificial intelligence to create innovative products for the customer. But I think non-customer facing areas within insurance, like premium balancing, will benefit from artificial intelligence.

Applying AI to the Premium Reconciliation Process

The areas where insurers are using artificial intelligence can be categorized into 3 buckets: tracking and monitoring, automation and predictive analytics. I think that applying these broad concepts to the premium reconciliation cycle may alleviate issues that the finance and accounting departments often encounter – data loss, complex business processes  that may lead to incorrect premium receivables, introduction of new software that may require rewriting scripts, etc.

  • Tracking and monitoring: The gift of AI can create a scenario never thought possible. Let’s assume a perfect world where every piece of data is tracked and monitored. For example, a policy holder makes a payment and that payment is tracked from the payment gateway, as it makes several hops within the billing systems to downstream systems and finally into the general ledger. The portion of this payment that is the earned premium versus unearned is tracked and, over time, monitored to ensure that the insurer’s revenues are accurately stated. Lost data, incorrect earnings, unaccounted premium receivables that currently haunt finance and accounting will be a thing of the past. Too good to be true? It’s possible.
  • Automation: This is low hanging fruit. Manual reconciliations and scripts are error prone. Worse yet, when an insurer introduces a new core system or a version upgrade, those scripts may need to be re-written. But if all reconciliation nuances are automated the artificial intelligence introduced will mitigate the risk of errors that come from manual processes.
  • Predictive analytics: Again, low hanging fruit. Insurers can use the power of artificial intelligence to better predict what percentage of premium receivables will go uncollected, alleviating some of the unknowns during premium reconciliation.

Too Good to be True?

Of course, I am not saying that an insurance company should replace the finance and accounting departments with machines to conduct premium reconciliations. I am suggesting that there are areas of applicability within the premium reconciliation process from the lines of business to financial reporting that would help mitigate the risks associated with incorrect premium numbers.

All of the examples above use artificial intelligence to create scenarios that might sound like they’re too good to be true. But if applied correctly, that could be the reality for carriers. Mistakes around premium reconciliation, an often arduous task, are eliminated. Revenue is properly accounted for and insight into future payments help carriers plan for their future.

As the insurance industry catapults into a new age, it’s important to see the opportunity. Things are going to be done faster, yet smarter, because that’s what artificial intelligence introduces into the equation. And using artificial intelligence doesn’t end at the premium level. With many more opportunities, most notably fraud mitigation, it’s critical that insurers start thinking long and hard about the future of technology at their organizations.

While 2016 may have been the breakout year for customer facing AI, is 2017 the year that will see AI leveraged extensively in critical processes like premium balancing?  I certainly see back office AI as the next step in the insurance industry’s quest to modernize.

To learn more about automating the reconciliation of claims to payment checks to assure accuracy, check out this case study.

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