The Compelling Case of AI and Machine Learning (ML) in Insurance

Sanam Malhotra | 4th May 2020

As the world fights an invisible enemy, healthcare services including insurance are witnessing an overwhelming spurt in demand. In such challenging times, artificial intelligence (AI) is emerging as an effective, robust, and reliable insurance agent. With virtual agents and predictive analytics, AI and machine learning (ML) in insurance are overcoming business challenges for achieving operational efficiency.

In this article, we take a closer look at how the insurance sector is evolving and improving processes by deploying AI and machine learning development services.


Applications of AI and Machine Learning (ML) in Insurance

1) Virtual Insurance Agents

After the success of AI-powered chatbots, machines are beginning to play a larger role in customer services. As Gartner projects,

In 2020, over 25% of customer service operations would be using AI-powered chatbot technology or virtual agents.

In the insurance sector, attending to routine customer queries and aiding insurance advice is a key area for utilizing AI development services. According to a recent survey conducted by Accenture, more customers are now satisfied and happy to receive ‘computer-generated advice.’

insurance chatbots ai and ml
Recently, our Oodles AI team built a virtual insurance chatbot for addressing customer queries at a leading insurance portal.

Voice-controlled virtual agents and chatbots have machine learning algorithms running underneath that analyze customer needs and render personalized solutions. Chatbots integrated into mobile apps and social media channels are further streamlining and accelerating customer responses for claim queries and other FAQs. In addition, insurance chatbots bring the following benefits to the table-

a) 24/7 support for open and clear communication with customers

b) Resolve routine customer queries to channelize employee efficiency for other critical tasks

c) Analyze customer responses to extract actionable insights for the management team

d) Manage claim filing, scheduling inspections with agents, and more.

Also read- Benefits of Using AI Chatbots in Insurance


2) AI and ML for Underwriting

While virtual agents are more inclined to customer service, AI and machine learning (ML) in insurance are set to disrupt the core function, i.e. ‘underwriting.’ For most insurance companies, underwriting has been a traditionally slow operation that requires human intervention for-

a) Analyzing complex data, both textual and graphical

b) Anticipating risks and demands

c) Devising policy strategies including price, rates, and coverage.

The inefficiency of humans to process such prodigious amounts of data has led AI and machine learning (ML) in insurance to take roots. Today, more than 60% of insurance executives are exploring the AI’s robust data-processing capabilities for various underwriting steps, such as-

a) Navigate and analyze data collected from multiple sources

b) Predict premiums and losses for policies

c) Identify potential risks early with predictive analytics

d) Calculate policy pricing

ai amd amchine learning (ML) in insurance underwriting
FinTech news executive, Julia Zekorn visualized the levels of automation in the insurance underwriting process.

With accuracy, AI is able to discern critical analytics for insurance agencies to improve operational efficiency significantly.

Also read- Machine Learning and Augmented Analytics to Optimize Business Intelligence


3) AI-led Policy Marketing

Dynamic customer needs and strong competitive pressure has turned marketing one of the cornerstones of the insurance industry. In the technologically-dominated world, insurance companies need to ramp up their marketing efforts to expand the business scope and acquire new markets.

AI and machine learning (ML) in insurance are accelerating the accessibility and delivery of insurance products.

It is possible with in-depth insights about customer behavior and thereby formulating appropriate marketing strategies for different audiences. To map customer behavior, various channels of intelligent customer engagement and data extraction eyes work together. With deep learning capabilities, AI is churning the marketing, sales, and distribution of insurance products with-

a) Automated marketing materials and promotions with precise targeting

b) Personalized recommendations

c) Generating new product offerings

d) Machine learning for customer segmentation, and more.

Also read- Accelerating Sales Growth with Artificial Intelligence


Deploying AI and Machine Learning (ML) in Insurance With Oodles AI

As the world continues to battle the COVID-19 outbreak, healthcare insurance businesses are experiencing record demands, interactions, and queries worldwide. The exponential rise of patients, data, and claims have overwhelmed insurance agents who are struggling to manage processes efficiently.

We, at Oodles, are harnessing the power of AI and ML technologies to assist insurance companies in overcoming business challenges effectively. Our AI team has experiential knowledge in automating several insurance operations with the following AI solutions-

a) Intelligent Virtual Agents for delivering customer services, resolving inquiries, and augmenting marketing efforts

b) AI-powered OCR (Optical Character Resolution) systems for extracting accurate data and insights from scanned insurance documents

c) Recommendation systems for personalizing product suggestions for diverse customers

d) Predictive analytics based on customer behavior to devise effective policy strategies.

Reach out to our AI experts for exploring custom AI solutions to optimize insurance operations with ease and accuracy.

About Author

Sanam Malhotra

Sanam is a technical writer at Oodles who is currently covering Artificial Intelligence and its underlying disruptive technologies. Fascinated by the transformative potential of AI, Sanam explores how global businesses can harness AI-powered growth. Her writings aim at contributing the multidimensional values of AI, IoT, and machine learning to the digital landscape.

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