By Aditya Dadia, Founder, Alwrite
Both commercial and personal insurance provide a sense of relief in the minds of policyholders. Anyone purchasing an insurance policy has a certain bit of reassurance about different kinds of mishaps in the future. However, the process of insurance claims can be complex, thereby resulting in unnecessary confrontations between an insurance company and policyholders.
It was reported that around 43% of health insurance policyholders faced different kinds of troubles while processing claims. In commercial insurance, around 4.5% of all claims are delayed due to incorrect claim filings.
It is critical that the claim processing and settlement process should be more transparent. Using the latest technologies, such as Machine Learning, can be an excellent way of addressing some of the longstanding issues in insurance claim management. By automating claims processing and detecting anomalies, ML ensures faster, more accurate outcomes.
Here is how commercial and other insurance providers can benefit from integrating the latest technologies in claim management.
Challenges in traditional insurance claim management
Conventional insurance claim management has faced numerous challenges, both from the perspective of the policyholders and the insurance companies. Here are the most common ones:
Processing delays: Manual claim verification and settlement processes can result in increased financial strain, especially for businesses that rely on timely settlements to maintain operations.
Fraudulent claims: The processes used by legacy systems to detect fraud are reactive and inefficient, leading to significant financial losses for insurers. This increases the cost of premiums for both individual and commercial insurance customers.
Lack of transparency: There are hardly any real-time updates, which can easily result in complicated financial planning and disrupt business continuity for commercial insurance policyholders.
Human error: Manual claims handling is prone to errors, which can further delay settlements and reduce trust in the process.
How ML can enhance transparency?
Here are the key benefits that machine learning can have to enhance transparency within the insurance sector, especially in claims processing:
1. Automation of claims processing: This includes automating routine tasks such as claim verification and documentation review. Hence, the time usually undertaken to offer real-time updates and carry out routine tasks can be reduced. The overall precision of such operations can also be enhanced.
2. Fraud Detection: Due to its ability to process huge volumes of data, it is possible for ML algorithms to detect unusual patterns and flag frauds. This proactive fraud detection reduces losses for insurers and builds trust with clients by ensuring fairness in claim settlements.
3. Eliminating human errors: As ML helps automate routine and repetitive tasks, the errors that inadvertently occur during manual processing are completely eliminated from the system.
4. Data pattern assessment and predictive analysis: ML analyses historical claims data to predict claim outcomes and assess risk profiles, offering policyholders clearer expectations about the process.
Eventually, ML helps to communicate better between policyholders and the company through real-time updates and quick problem resolution.
Impact on customer trust and business efficiency
With enhanced transparency and better updates, especially for commercial insurance policyholders whose business finances depend on efficient claim processing, the overall level of uncertainty and frustration among customers can be reduced.
For commercial insurance providers, ML significantly boosts business efficiency. By automating routine tasks like claims assessment and fraud detection, ML accelerates the settlement process, leading to quicker resolutions. It inadvertently reduces operational costs for businesses and eliminates the chances for human errors in repetitive tasks.
Future of insurance claims with ML
As the latest technologies continue to evolve, the overall role of Machine Learning shall only increase in the future. In the future, commercial insurance companies are likely to introduce more sophisticated predictive models that can assess risk and detect fraud with even greater precision. The role of ML integrated with AI-driven chatbots can also improve the quality of customer relationship management. Since businesses require fast, accurate, and transparent claim resolutions, the role of ML shall help in improving the operational efficiency and financial planning, typically of MSMEs.
Summing up
The role of ML and AI in different aspects of insurance servicing has already demonstrated some exciting results. By automating processes and providing real-time updates, ML fosters trust and clarity, which is crucial for both individual and commercial insurance clients. For small businesses, any inadvertent delays in claim processing or unnecessary rejections can lead to serious business consequences, which can even lead to shutting down operations.
The future of commercial insurance includes swift integration of ML tools through which it becomes a business enabler. ML will play a central role in building trust and efficiency across the board.