This latest piece is by Shantanu Tewari, Head of Insurance Practice, Newgen Software and it looks at the benefits of AI.
Insurance is a highly complex and data-intensive industry. Multiple business lines have multiple product offerings. And when you map these with corresponding processes and data sets, you have a highly challenging operation to manage. Trying to handle this mammoth complexity manually leads to work overload, poor departmental productivity, and excessive delays in operations.
Bringing artificial intelligence (AI) into the picture can help insurance companies do away with such roadblocks and drive them to achieve greater efficiency, accuracy, and profitability.
AI for Driving Success in the Insurance Industry
Let’s take a look at some of the AI use cases in insurance processing:
1. To Smoothen the Underwriting Process
Underwriting is a crucial aspect of the insurance business for evaluating incoming proposals to assess associated risks. And underwriters are always required to maintain accuracy and quality. However, the large volume of applications received can make it strenuous for underwriters to efficiently evaluate, assess, and deliver quotes on time while maintaining maximum accuracy.
By leveraging an AI model, insurance companies can generate quotations with the best possible coverages and rates by independently evaluating captioned proposals based on past data, previous decisions, and underwriting guidelines.
AI can assist insurance companies in underwriting decision-making by providing risk insights with respect to parameters like customer life expectancy or premium paying credibility, risk location, STFI exposure, vehicle GPS history, and the probability of natural catastrophic events.
2. To Enhance the Accuracy of Claims Processing
For insurance firms, the claims process has long been regarded as a cost center. It also serves as an important “Moment of Truth” for clients. As a result, insurance firms must be extremely cautious when analyzing claims in order to reduce losses and shorten settlement times.
By leveraging AI, insurance companies can save time by improving triaging and increasing accuracy.
Claims Triaging: It is essentially the process of segregating incoming losses. And executing this manually can significantly clog the bandwidth of the claims user. At the “first notification of loss,” the user must invest significant time to segregate the cases. This often results in even low-severity claims taking longer time to settle. AI models can use historical data, previous decisions, and defined rules to segregate claims automatically as per their severity level. Low-severity claims can be identified as straight-through cases and routed directly for settlement without manual intervention. This helps to eliminate redundant manual activities and accelerate the claim settlement time.
Auto-Reserve Setup: Setting up a reserve is a critical step in the insurance claims cycle as it is locked under the company’s liability and will be in the company’s book until the actual settlement. Any sort of discrepancy in the reserve amount can lead to an error in financial ratios.
AI can help insurers set up accurate reserves at the onset of claim registration by taking into account the line of business, policy type, cause of loss, and other related parameters. This will help the insurance companies in doing away with manual work and in securing exact financial ratios.
Fraud Detection: Many insurance companies often struggle to detect fraud cases in their incoming claims requests precisely. A high volume of claims requests makes it difficult for insurers to analyze every case thoroughly. It is a high-stakes game, as insurance companies around the globe pay billions in fraud losses every year.
With the help of AI fraud detection tools, insurance companies can analyze any incoming losses and compare them with the previous data to identify possible frauds. AI models can be trained to decipher user decisions under complex claims investigations to assist decision-making. By leveraging an AI-based fraud detection model, insurance companies can reduce costs, ease the bandwidth of overwhelmed claim managers, and reduce the dependency on any external agency.
3. To Deliver an Exceptional Customer Experience
Insurance companies are highly data-intensive; however, often, they find it challenging to wring value out of this data.
AI and ML models can help insurance companies identify and segregate customers under multiple focus groups. This allows them to design specialized campaigns and attractive benefit coverages to target customers with a high probability of conversion. AI models can also be deployed for predicting the lifetime premium value of customers, enabling insurers to maximize their effort towards customer cohorts where higher premium share can be gained with minimal effort.
To Conclude:
Insurance companies must embrace new-age technologies like AI and ML to thrive in this digital age. AI adoption makes it very easy for insurers to swiftly assess and manage risks, fast-track claims processes, and deliver a delightful customer experience.
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