Recent exciting advances in artificial intelligence have startled insurance companies into rethinking their business and operational systems. Deep Learning, a subset of artificial intelligence pioneered by AI researcher Geoffrey Hinton, holds immense potential for novel products and applications in the insurance business. This has sparked the insurance industry’s interest in the potential impact of artificial intelligence on areas like as customer experience, underwriting, distribution, and claims processing. This blog is dedicated to the impact that deep learning has had on and will continue to have on the insurance industry in the future.

A Brief Idea of Deep Learning

Deep Learning is a subset of Machine Learning that uses multi-layered neural networks to discover patterns in data. This implies machines may now acquire new skills like image recognition while operating. In 2010, the ImageNet competition released a dataset of 14 million tagged photos open source to promote the development of cutting-edge image classifiers. Deep Learning has the benefit over other AI methods in training large volumes of unstructured data quickly.

The insurance sector is founded on centuries-old norms and laborious processes. Most insurance firms have massive amounts of organized and unstructured data stored in silos in formats including text, picture, and voice. Insurance businesses may utilize data lakes to automate procedures that previously required human intervention. This method is very useful in estimating damages, discovering patterns in claims, and improving customer experience.

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Claims Management
The insurance business has distinct regulatory and compliance needs. Every hour, it deals with hundreds of claims and thousands of client complaints. Automating the process using machine learning and deep learning algorithms helps speed up the claims processing from initial report to analysis and client interaction.

To assist insurers, minimize cycle times, Aspire Systems has created an algorithm that converts handwritten or typed forms into digital forms with 99.9% accuracy. Tokio Marine, Japan’s largest P&C insurance firm, uses this method.

Using AI can reduce claims management manpower by 70% to 90%, according to a McKinsey paper titled “Insurance 2030 – The Impact of AI on the Future of Insurance”. Firms can profit from increased internal efficiency and consumer satisfaction as processing time decreases from days to hours. Leading insurance companies have noticed the benefits of using deep learning-powered generative chatbots to answer to consumer enquiries, freeing up humans to handle more complex claims. Lemonade, one of the fastest growing insurance firms, has introduced AI into its systems and procedures. A deeper understanding of claims costs can help insurers reduce risk and boost income.

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(Source- Machine learning- insurance)

Fraud detection
Fraud is a major risk for insurance firms, costing the US industry $40 billion annually. In Norway, 829 false claims totaled $14 million. False medical receipts and bills are examples of fraudulent claims.
• Finding hidden and implicit relationships in data

• Analyzing submitted claims for face recognition and emotion

• Using labelled historical data to train fraud detection models

• Eliminating document verification delays, allowing for speedier fraud detection

A Paris-based firm, Shift Technology, has developed a technology that detects fraudulent claims with 75% accuracy, double the industry standard

Improving Underwriting and Pricing
Actuarial analysis is error-prone and time-consuming. Insurers can use automated reasoning to reduce the cost and time it takes to approve a customer’s policy. With deep learning, you can optimize risk and insurance pricing in a competitive market, increasing client acquisition. Employing deep learning technologies has benefited two categories, health and property insurance, and will continue to do so in the future. Daisy Intelligence, for example, makes price options based on individual risk variables such as age, location, and even blood pressure.

Also in the property insurance business, the Tensorflight project uses satellite and street image processing to enhance underwriting and reduce misclassification mistakes, which are anticipated to cost the US sector $4.5 billion over the next four years. Enodo, for example, allows users to study past rent, concession data, and market prices. These tools benefit both policyholders and insurers.

Insurers are using deep learning to construct AI-based systems in P&C (56%) and life (39%) segments.
Customer analytics and Personalized offers
Insurers are continually seeking to improve consumer satisfaction. According to McKinsey, tailored suggestions and dynamic remarketing may improve conversion rates and reduce client acquisition costs by up to 50%. These techniques rely on deep learning. Deep learning algorithms uncover similarities in large troves of unstructured data, allowing insurers to create bite-sized and on-demand insurance solutions in a highly competitive market.

Vehicles Damage Assessment using Deep Learning
Car insurance is a big business; it’s required for automobiles that aren’t paid off. The intricate automobile damage evaluation procedure needs assessors to have extensive knowledge and abilities in addressing automotive damage. The evaluators will use evidence, such as video from the car’s camera, images from mobile phones demonstrating the damages, and log data from IoT devices. It’s also required that they estimate the cost of repairs. This method is prone to human mistakes, weariness, and prejudice. Insurance firms want to improve accuracy without hiring a large number of highly compensated damage assessors.
A computer can distinguish things in a picture or video clip using computer vision, which is directly useful to business. By using edge computing, front-end devices can evaluate photos in real time.
Using a smartphone, evaluators may record entire views of an automobile and examine the images or videos in real-time to assess damage and repair costs. Any insurance provider demands images of damaged vehicles or property as proof. So we used modern computer technologies to automate damage appraisal from images of damaged cars—part identification, damage evaluation, and repair cost calculation.

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Healthcare and Artificial Intelligence: A Winning Combination

Combining healthcare claims data with other datasets creates massive and rich data streams. The dimensionality and complexity of these integrated datasets might make traditional statistical studies difficult to perform. Recent advances in artificial intelligence (AI) have resulted in the creation of algorithms and systems capable of learning and extracting complex patterns from such data.
AI has been effectively used to such integrated datasets in the past, with applications ranging from enhancing the insurance claim processing pipeline to lowering estimate bias in retrospective research. Nonetheless, much more might be done. The discovery of complex patterns within large-scale datasets may lead to the discovery of novel predictors of illness start or to a more proactive providing of individualized preventative treatments. While dangers and obstacles are inherent in the application of AI, they are not insurmountable. As with any invention, caution and responsibility will be required as we progressively use AI approaches in healthcare.


Rapid technological advancements over the next decade will result in significant disruptions in the insurance business. Carriers who use new technology to develop novel solutions, harness cognitive learning insights from new data sources, optimize processes and reduce costs, and meet consumer expectations for personalization and dynamic adaptation will win in the AI-based insurance space. Most importantly, carriers who embrace a mentality that views disruptive technologies as possibilities rather than threats to their present company will succeed in the insurance market in 2030.