Insurance is a data-heavy industry. Typical compliance and claims filing processes involve considerable amounts of documentation with little room for error. As such, the sector is ripe for AI disruption.
The insurance industry has not ignored this potential — 77% of insurers in 2024 have implemented AI to some extent. While these AI projects cover many use cases, a few general applications are particularly promising.
Data Management
One of AI’s most valuable assets in insurance compliance is the ability to compile and sort data quickly and accurately. Insurers rely on information such as policy numbers, risk factors and customer details, but keeping track of everything and using it effectively can be challenging.
Manually sorting through records to update legal documents or file claims is time-consuming and error-prone. Natural language processing (NLP) models can make it easier by automating data entry on legal forms and gathering all information on a customer into a single profile for greater accuracy. Such automation saves time and reduces mistakes to avoid expensive arbitration or customer service issues.
AI-powered continuous monitoring solutions can also help secure information. These systems save $2.2 million on average in data breach costs by enabling faster responses to emerging cybersecurity incidents.
Risk Assessments
Insurers can also use AI to improve data analysis. They can automate risk assessments to form more accurate policies, ensuring fairness for customers and avoiding costly mistakes for the business.
Predictive analytics is particularly helpful here, as it can estimate how risks may change or play out in the future. The resulting insight makes it easier for insurers to determine what kind of coverage a client may need or what their premium should be. Minimizing human involvement in this decision-making may also reduce regulatory risks from the bias issues that have historically plagued insurance, as long as insurers follow ethical AI and data practices.
Similarly, machine learning can review draft policies to find areas where customers may lack insurance but would benefit from it. For example, general liability coverage does not always cover data breach losses, but that is an easy detail for humans to miss. AI could catch the discrepancy and suggest a policy change to improve client protection.
Streamlined Reporting
Other AI applications are not as mission-critical but still provide needed improvements. Streamlining regulatory reporting is among the most beneficial of these use cases.
Laws require insurers to provide mandatory disclosures to their customers, applicants and government regulators. However, filling out this paperwork and submitting it takes time, and humans are liable to make mistakes in such repetitive, data-heavy work. Automating it through NLP and similar machine learning models reduces those risks and ensures a faster turnaround time.
AI’s speed is important here, as some disclosures must occur within 60 days if businesses hope to avoid penalties. Its accuracy is also crucial, as mistakes could jeopardize compliance with the Fair Credit Reporting Act or other regulations.
Remaining Obstacles for AI in Insurance
While AI’s potential in insurance compliance is promising, several challenges remain. Reliability and explainability are two of the most prominent. The machine learning systems insurers may use to identify fraud are prone to false positives, and risk assessment algorithms can amplify human bias. Black box models may further complicate the matter by making it difficult to prove an insurer arrived at a decision fairly.
The National Association of Insurance Commissioners (NAIC) has issued safe AI guidelines that insurers can turn to for help. Following these best practices — which include thorough AI risk management controls and ethical frameworks — will minimize regulatory concerns around bias and reliability. Cleansing and anonymizing data before feeding it to AI wherever possible will also help.
Data privacy laws will almost certainly evolve in the future. As a result, insurance organizations may need to adopt new security practices or rethink how they use customer information to avoid fines.
Annual regulatory audits will give insurers the insight to stay on top of emerging and shifting legislation. Companies should also go above and beyond existing requirements to make it easier to comply with more stringent laws in the future.
AI Could Transform Insurance Compliance
AI can automate many of insurance’s most repetitive and time-consuming tasks. Capitalizing on those benefits will usher in an era of lower costs and improved legal compliance for insurers and fairer treatment for customers.
While several obstacles remain, none render such advantages riskier than they are worth. The industry can safely innovate and embrace AI if it pays attention to the obstacles and approaches implementation carefully.
