The use of AI in insurance has been touted as one of the most pathbreaking developments, which result in substantial economic and societal benefits that eventually improve risk pooling and enhance risk reduction, mitigation, and prevention. Insurance companies can respond on time to requirements and ensure they can deliver high-quality service to the customer they promise through automation.
Conventional insurance players have predominantly been slow to react to technological changes. A Deloitte study stated that while almost all industries have achieved success with AI or have started investing in AI, the insurance industry seems to be lagging substantially, with only 1.33% of insurance companies have invested in AI compared to 32% in software and internet technologies. With the advent of InsureTech startups and technology incumbents, the scenario is fast changing now. They can deliver speedier claim payments, greater price transparency, and on-demand policies while simultaneously reducing the cost and resources required. The changing dynamics open up winds of opportunities for the AI-enabled insurance landscape globally.
AI and Machine Learning Use Cases in Insurance
The role of AI in insurance has been growing by leaps & bounds, from claims processing to compliance to risk mitigation and damage analysis. For instance, Robotic Process Automation (RPA) is being used to carry out repeated tasks so that operational teams can focus on more complex actions. AI is fundamentally changing the way insurers have been operating over the years.
There are immense opportunities to move from the traditional coding of complex processes to an iterative use of trained AI models against large (enterprise) datasets. AI has incredible potential across the entire insurance value chain, from marketing to underwriting and claims management. The industry is growing at a rapid clip, expected to cross $2.5 billion by 2025. This milestone indicates a compound annual growth rate of 30.3% between 2019 and 2025.
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- Claims Processing
- Claims Fraud Detection
- Claims Adjudication
- Automated Underwriting
- Submission Intake
- Pricing and Risk Management
- Policy Servicing
- Insurance Distribution
- Product Recommendation
- Property Damage Analysis
- Automated Inspections
- Customer Lifetime Value Prediction
- Speech Analytics
- Customer Segmentation
- Workstream Balancing for Agents
- Self-Servicing For Policy Management
- Claim Volume Forecasting
#1 Claims Processing
Driven by policy and legal requirements, insurers need to ensure that the claims meet requisite criteria throughout the process cycle. Understandably, it is an ardent task to deal with thousands of claims and customer queries, making it time-consuming. Machine Learning makes the entire process efficient and effective. It dramatically improves claims processes value chain from moving claims through the initial report, analysis, and ultimately establishing contact with the customers. The process saves time and frees employees to focus on more complex claims and direct customer contact.
#2 Claims Fraud Detection
Federal Bureau of Investigation study on US insurance companies revealed that the total cost of insurance fraud (non-health insurance) is close to more than $40 billion per year. That means Insurance Fraud costs the average US family between $400 and $700 per year in the form of increased premiums. These startling statistics reflect the dire need for highly accurate automated theft detection tools to empower insurance companies to enhance their due diligence process.
#3 Claims Adjudication
Council for Affordable Quality (CAQH) Index report reveals that automating eligibility and claim verification can lead to an annual saving of $ 5.2 billion in healthcare insurance alone. The claim initiation automation process saves time for insurers with the help of a chatbot that interacts with customers and collects the required information. Through chatbots, information can be captured in a structured format, and a first-level validation can be carried out during the claim initiation process. World Economic Forum (WEF) study revealed that by 2022, 62% of an organization's data storage and data processes would be executed via computers. With the rising automation expanse, investing in auto-adjudication systems will help organizations stay relevant shortly.
#4 Automated Underwriting
Conventionally, insurance underwriting was heavily employee-dependent to analyze historical data and make informed decisions. Additionally, they had to alleviate risks and deliver customer value by working with haphazard systems, processes, and workflows. Intelligent process automation simplifies the underwriting experience by providing Machine Learning algorithms that collect and make sense of massive amounts of data. It also improves rules performance, manages straight-through-acceptance (STA) rates, and prevents application errors. By automating most of the process, underwriters can focus only on complex cases that may require manual attention.