Many insurance companies are automating their claims processing as it minimizes turnaround time, regulation and claims processing cost leading to enhanced customer services and business profitability.

FREMONT, CA: Claims processing cost and fraudulent claims payment raise operation cost of the companies and cause a lot of hassle for customers. This is why claims management has become a priority for insurance companies as it influences the bottom line and customer retention strategies.

Digital transformation and artificial intelligence (AI) can optimize claims management practices and provide improved customer satisfaction. However automation in the insurance industry can face some challenges. Here are four issues they face while adopting AI for claims processing:

Data Security

The AI system handles vast amount of data which is stored in the insurer’s server or cloud for training and decision making of present claims. To process a claim, different applications access the data for filing claim, analysing loss and calculating payout. The data is also accessed to allow constant learning of the AI system. Because of the size and nature of the data and connection of different application, the risk of data leak and security breach increases making insurers doubtful to adopt the AI system.

Unbalanced Data Sets for Training

The AI system needs to be trained on a vast amount of data to handle the transferable scenario of a claim. It requires the system to address various structured and unstructured data that includes historical claims, documents, transactions, investigative reports, images and GPS data, among others. An unbalanced datasets for training can lead to reduced predictive accuracy of the AI system in fraud prevention and claim management.

Algorithmic Risks

Engineers that develop AI algorithms base the system’s learning on historical claims. This could result in involvement of human prejudice in the algorithm making it biased and mishandle claims.

The system based on historical claims enters a self-learning mode after deployment. If this is not constantly checked, it can lead to minor changes in the algorithm directly affecting the prediction outcome of thousands of future claims and improper settlement. This will impact the revenue of the organization.