Artificial Intelligence (AI) technologies have rewritten how businesses are run in the digital economy as digital has become the default method of interaction for people and businesses. Automation has long been the solution of choice for improving efficiency in Insurance Claims Management. It will no longer do, as creeping loss ratios are toppling the profit margins that companies have been safeguarding. Cognitive computing, one of the AI-based technologies is bringing in human-like reasoning, insights, and decision-making at superior processing speeds to Insurance Claims Management. Cognitive computing holds great promise for reinventing insurance business models and catapulting its operational performance, organization productivity, and overall profit margin.
As a way to indemnify ourselves against potential loss, there is no better alternative to insurance. The insurance industry has been overly focused on improving underwriting standards and reducing administration costs for long. Claims management has gotten the short shrift until a steep rise in loss ratio and its adverse impact on profit margins started to change that.
Claims management involves various steps from the First Notice of Loss (FNOL), assignment of a claims adjustor, investigation, and claim settlement up to claim payment. It is typically a cumbersome web of processes needing considerable manual intervention. With rising demand from the ecosystem for stringent regulatory compliance and high customer expectations, insurance has tried to automate the manual processes in response. This has cut costs, reduced fraud, and improved customer experience, but the benefits have only been incremental.
Automation alone lacks the ability to take the insurance sector to the next dimension of growth unless intelligent decisions can be made quickly without manual intervention. With the emergence of disruptive demographic changes and volatile economies, the huge threat of unexpected financial loss to the insurance carriers needs to be addressed.
The current Claims Management Process is riddled with serious pain points. Any new solution that promises transformation needs to address them in depth. It goes a long way in future-proofing the business and uncovering new and exciting business opportunities.
Adverse Impact on Financial Performance Any revenue loss arising from higher than the required claims settlement amount has huge impact on an insurance carrier’s financial performance.
Undesirable Customer Churn Claim, by its very nature is quite a contentious subject. There are instances of delayed claims settlement, payment of less than the claimed amount, or even outright rejection of a claim. This can be a source of disgruntlement and dissatisfaction for the clients and can cause undesirable customer churn.
Inability to Handle Fraudulent Claims One of the challenges of the claims adjuster is to determine if the claim is fraudulent. Due to lack of a clear, defensible, and structured process/technique to identify such claims, the insurance carriers are left with no option sometimes but to settle the claim. This impacts the insurance carrier’s financial performance.
Increased Loss Ratio Actuaries consider the company loss ratio when determining the premium amount. Hence, the higher the loss ratio, the higher is the customer premium. It is in the interest of the insurance carrier to keep the loss ratio in check in order to be market-competitive.
Impact to Multiple Parties An inaccurate or inefficient claims process affects many parties that are involved.
Manual Intervention In a typical ‘Claims Settlement’, there tends to be a lot of communication, paperwork, travel, coordination, and fees that result in higher administrative costs for the insurance carrier and a higher premium for the customer.
In the cognitive computing approach, a judicious combination of human logic and machine-learning are used to great effect. It is supported by intelligent datadriven analysis based on insurance/loss history and outcome prediction resulting in highly effective claim settlements.
As part of the self-learning methodology, the data provided to the system will have the required knowledge to study the patterns, process them with machine learning algorithms, and provide the desired output. The use case below describes how precisely cognitive computing can help in effective claim settlement. Imagine a scenario in which a loss has occurred and the claimant is reporting the loss to the insurance carrier:
Cognitive computing in insurance, especially in the claims settlement process brings a highly effective solution to end-to-end claim processing raising operational efficiency along with the prospect of efficient AI-based prediction and self-remediation. It also enables smarter and faster decisions allowing insurance carriers to predict and prevent problems so that they can reap huge cost savings with a positive impact on their financial performance.
Coforge has a proven track record of a series of successful implementations of cognitive computing for many insurance clients. We offer:
The industry is relentlessly marching towards cognitive computing worldwide and the insurance sector is at the forefront. Cognitive computing in effective Claims Management is making a huge difference to insurance carriers. With the help of machine learning, deep learning, image recognition, reasoning, and many other intelligent and cognitive automation technologies, cognitive computing is enabling smarter and faster decisions. It is making it possible to predict and prevent problems leading to faster complex and ambiguous task execution with high accuracy. Faster time-to-market, higher agent effectiveness, lower customer acquisition costs, improved sales, and enhanced delivery on the interaction and customer experience are the new realities for the insurance industry