Deep learning provides several benefits to the insurance industry by quickly assessing claims, verifying documents, enhancing customer experience and detecting fraud.
What is Deep Learning?
Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy. Deep learning attempts to mimic the human brain—albeit far from matching its ability—enabling systems to cluster data and make predictions with incredible accuracy.
Combining Deep Learning with Insurance
Earlier, the insurance industry worked around legacy systems, having bookkeeping and conventional software for documentation. The legacy systems, however, failed to provide optimized outcomes when large chunks of data were poured into the industry, much of what we see today. With the advent of new and advanced technologies, various sectors, including the insurance industry, have begun seeing path-breaking innovation. The insurance industry collects and generates a large volume of data on a daily basis, including a customer’s health records, sensor data from vehicles, confidential legal papers, to name a few. The data, if analyzed thoroughly, gives actionable insights that the insurance industry can use to improve its services. Deep learning comes with neural networks that are capable of analyzing swarms of data and learning from it. Deep learning in insurance not only enhances customer experience but also helps the industry detect fraudulent activities.
What Are the Benefits of Deep learning in Insurance?
Gone are the days when we had to meet an insurance agent in person to buy an insurance cover for ourselves or our prized possessions. The whole process of setting up an insurance account was stressful back then.
However, today customers expect to avail a service with minimum hassle and maximum support. Deep learning is helping fulfill this expectation to a large extent. And no doubt, the insurance industry is already benefiting from the incredible applications of deep learning. For instance, chatbots are helping the industry offer customers 24/7 assistance without getting tired. But, it’s not just about applications like chatbots, that are already in use. Deep learning applications are about to revolutionize the insurance sector like no one ever imagined.
By integrating IoT and deep learning, the insurance industry is reaching an altogether different level of sophistication. One such jaw-dropping innovation that will blow your mind is this U.S. based insurance solutions provider that uses deep learning and IoT to identify hail hits and missing shingles for roofs. With the help of drones, deep learning, and IoT, the solution makes informed decisions for customers on insurance claims, management, and roof inspection.
What Are the Use Cases of Deep Learning in Insurance?
Deep learning has several uses cases in the insurance industry including:
1. Property analysis
2. Facial recognition
3. Automated claim support
4. Personalized offers
5. Visual analytics in claim settlement
6. Interactive bots answering queries
6. Predictive analytics
7. Documents verification
8. Pricing/Actuarial analysis
Will Autonomous Cars Kill the Insurance Industry?
The 5 Levels of Automation
There is a common concern that autonomous cars will burn the car insurance industry in the future. However, the fact is that no matter what level of precision a technology scales, humans will always prefer prevention over cure. Absolute reliance on technology, without any precautions, does not come naturally to most of us, at least for now. Besides, deep learning is yet to go a long way before we hand over the reins completely. For instance, news like that of an “Uber self-driving car kills a pedestrian in the first fatal autonomous crash” cannot be ignored entirely.
So, even though deep learning technology will offer automated services, people will opt for an extra measure of security, meaning that the insurance industry will never burn out. Top brands like Google, Mercedes, and Volvo, have insured their robocars. They are also ready to accept liabilities if their technology is at fault. So, deep learning in insurance is only going to benefit the sector and nothing else.
Artificial Intelligence in the 4th Industrial Revolution
Artificial intelligence is providing disruptive changes in the 4th industrial revolution (Industry 4.0) by increasing interconnectivity and smart automation.
Industry 4.0 is revolutionizing the way companies manufacture, improve and distribute their products.
What Makes Artificial Intelligence Unique?
Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.
It allows computers to think and behave like humans, but at much faster speeds and with much more processing power than the human brain can produce.
AI offers advantages of new and innovative services, and the potential to improve scale, speed and accuracy.
There are 3 types of artificial intelligence:
Artificial narrow intelligence (ANI), which has a narrow range of abilities.
Artificial general intelligence (AGI), which is on par with human capabilities.
Artificial superintelligence (ASI), which is more capable than a human.
Artificial intelligence can also be classified as weak or strong.
Weak AI refers to systems that are programmed to accomplish a wide range of problems but operate within a predetermined or pre-defined range of functions. Strong AI, on the other hand, refers to machines that exhibit human intelligence.
Artificial intelligence has several subsets:
Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing.
What is the Fourth Industrial Revolution?
The Fourth Industrial Revolution is the current and developing environment in which disruptive technologies and trends such as the Internet of Things (IoT), robotics, virtual reality (VR) and artificial intelligence (AI) are changing the way modern people live and work. The integration of these technologies into manufacturing practices is known as Industry 4.0.
The first industrial revolution used water and steam power to mechanize production.
The second used electric power to create mass production.
The third used electronics and information technology to automate production.
The fourth Industrial revolution is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres, with rising emerging technologies, as real AI, Narrow AI/ML/DL, robotics, automation, materials science, energy storage, the Internet of Things, autonomous vehicles, 3-D printing, nanotechnology, biotechnology, neurotechnology, cognitive technology, and quantum computing. It implies radical disruptions to everything, industries, jobs, works, technologies, and old human conditions. In its scale, scope, complexity, and impact, the AI transformation will be unlike anything humankind has experienced before.
The Role of Artificial Intelligence in the 4th Industrial Revolution
Artificial intelligence is making companies make the best use of practical experience, even displacing traditional labor and becoming the productive factor itself.
It offers entirely new paths towards growth for manufacturing, service, and other industries, reshaping the world economy and bringing new opportunities for our societal development.
As AI begins to impact the workforce and automation replaces some existing skills, we’re seeing an increased need for emotional intelligence, creativity, and critical thinking.
Zvika Krieger, co-leader of the World Economic Forum’s Center for the Fourth Industrial Revolution.
Deploying AI requires a kind of reboot in the way companies think about privacy and security, As data becomes the currency of our digital lives, companies must ensure the privacy and security of customer information.
Businesses will need to ensure they have the right mix of skills in their workforce to keep pace with changing technology.
How to Leverage TikTok’s New Ad Solution to Boost Brand Awareness
What Happens When Google Picks The Wrong Canonical URL?
Vlog Episode #179: Jaimie Clark vs Jon Clark
My Stack is Bigger than Your Stack, So What?
Need Content Freelancers? Try This Agency’s Model
6 Common Hreflang Tag Mistakes Sabotaging Your International SEO
‘Crime not to help’: South Korean ex-SEAL has no Ukraine regrets
9 Local Search Developments You Need to Know About from Q2 2022
Google Updates Crawl Stats Report Help Documentation
How Brands are Investing in Video Marketing On a Budget [2022 Data]
Why Google Doesn’t Like Some SEO Metrics
Google Bar & Pool Table Room
6 Tactics to Boost Ecommerce Sales [Without Discounting]
How Software Systems Enhance the Performance of Gym Business?
9 Creative Company Profile Examples to Inspire You [Templates]
How to Calculate Your Lead Generation Goals [Free Calculator]
Strategizing Your Instagram Marketing – DigitalMarketer
How To Build A Remote Team For SEO: Planning & Structure
24 questions to ask identity resolution vendors during a demo
Google Hints That Useful Nofollow Links Won’t Pass Weight (Or Much Of It)
SEARCHENGINES7 days ago
Good Web Sites Are Good For SEO, Says Google
SEARCHENGINES7 days ago
Daily Search Forum Recap: June 20, 2022
SEARCHENGINES5 days ago
Google No Longer Lowers Importance Of Content Not Visible On Page
SEARCHENGINES7 days ago
Does Changing Your Logo Hurt Your SEO Or Google Rankings?