Insuranceciooutlook

Four Challenges in the Adoption of AI in Claims Processing

Insurance CIO Outlook | Wednesday, June 09, 2021

The AI algorithms are created by engineers, and their learning is largely based on historical statements. As a result, human bias can be introduced into the AI algorithm.

Fremont, CA: Insurance companies' administrative costs will rise due to claim processing costs and fraudulent claim payments, which can be very inconvenient for consumers. As a result, claims management has become a top priority for insurance firms, as it has a bearing on the bottom line and customer retention strategies. Artificial intelligence (AI) and digital transformation will significantly improve claims management practices and ensure greater customer satisfaction in the form of disruptive technologies. Here are four challenges in the adoption of AI for claims processing.

Algorithmic Risks

The AI algorithms are created by engineers, and their learning is largely based on historical statements. As a result, human bias can be introduced into the AI algorithm. This might lead to the AI system being skewed and potentially mishandling thousands of claims.

The learning of the model is primarily focused on historical arguments. The model goes into self-learning mode after deployment. Continuous monitoring failure will result in a slight shift in the algorithm, which can directly impact the outcome estimation of thousands of potential claimants and lead to inappropriate settlement. This can have a direct effect on the organization's sales.

Unbalanced Data Sets For Training

To cover any possible claim case, the AI system must be trained on a large amount of data. This necessitates the AI system's ability to deal with a wide range of structured and unstructured data, including historical statements, records, transactions, investigative reports, GPS data, and photographs, among other things. Due to unbalanced datasets for testing, the AI system's predictive accuracy in fraud prevention and claim management is poor.

Regulatory Compliance and People

Regulatory mechanisms vary from state to state. As a result, compliance is more challenging to achieve, and data collection for AI models is restricted. The use of data is crucial, but the insurer must ensure that the information is used with sufficient permissions. This limits insurers' ability to use AI on a global scale.

Using data or a model with potential ethical or public-interest issues may lead to uncertainty among the various stakeholders. It's also challenging to clarify the AI model to backend business operators, including auditors and regulators. All of this puts the company at greater risk.

Data Security

The AI system works with a vast volume of data for both training and decision-making on current claims in real-time. This information is kept on the insurer's servers or in the cloud. Various programs are used to file a lawsuit, assess the damage, and determine the compensation access this data to process a claim. Additionally, this data is accessed to enable the AI system to learn continuously. There is a risk of data leak and security breach due to the size and nature of the data as well as the connectivity among applications. As a result, insurers are hesitant to implement AI.

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