The Digital Future of Insurance Industry is Powered by Artificial Intelligence
What does a futuristic insurance scenario look like?
The future of the Insurance Industry is vibrant. Imagine a day in your life in 2040, you have your personal hi-tech digital assistant who schedules your ride with an autonomous cab for a meeting you need to go to. The personal assistant then maps the entire route and also stops to pick up your favorite cup of coffee.
Meanwhile, the digital assistant has mapped your entire journey and shared it with your insurance company. The insurance company’s digital assistant looks at the route and suggests an alternative route that has less likelihood of accidents and auto damages.
Your insurance provider notifies you that choosing the alternative route will see a rise in the premium for this ride by 1% and you happily agree.
Now, this is the kind of future we are talking about! Seamless, hassle-free, progressive, and business on the go. Insurance companies will function in their best capacity and people will be protected from all the unexpected events of life intelligently.
Immersive Artificial Intelligence into the Insurance Industry
25% of the insurance industry will be automated in 2025 thanks to AI and machine learning techniques.
Intuitive digital solutions will define the future of AI in the Insurance Industry. It is real that insurance companies now stand on a trembling ground and confront massive challenges. The risk incurred can be drastically reduced with the intervention of AI.
Smart technological applications can guide their customers to stay safe and always be secured and protected financially if any uncertainties arise in life.
Insurance companies have a lot to gain by investing in AI-powered technologies. A study claims that Insurance companies can benefit by up to $1 Trillion annually by incorporating smart solutions.
There are 3 areas that pose as a challenge for the insurance industry today:
- Reaching out at the right time to the customers who are looking to purchase an insurance
- Providing customized products to customers depending on their needs and requirements
- Delivering instant claim support to customers and rejecting false claims
A technologically advanced system is the need of the hour for insurance companies. This will help in getting rid of the redundant processes and time-consuming systems. Ultimately enhancing the customer experience.
Artificial Intelligence Use Cases in Insurance
- Claims Processing
- Risk and Fraud Detection
- Digital Assistant in Insurance
- Insurance Pricing
The claims processing cycle incorporates multiple tasks like investigation, adjustment, remittance, review, or denial. There are possibilities of numerous errors arising because of manual efforts put into these tasks.
- Claims processing traditionally requires human interaction that is prone to major errors.
- Customers send data in different formats to make claims
- There are numerous changes in regulations and thereby updating staff promptly is the need of the hour
Natural Language Processing (NLP) in Claims Processing
NLP is aimed at empowering computers to recognize human speech. Human speech is a cognitive function, NLP is considered to be one direction of AI.
NLP in claims processing can be used during a phone call when a representative is talking to the customer. The NLP program will recognize the client’s speech and automatically fill out the claims form – a perfect hassle-free and time-saving experience.
Similarly, real-time NLP can be used instead of a claims manager that processes claims. This NLP-based assistant can work 24*7 and machine learning techniques will keep making it more and more productive with learnings based on each mail the system receives and responds to.
Data Annotation for Claims Processing
For insurance claims processing, AI is taking over all the traditional paper-heavy practices and replacing it with digital intelligence. To make machines smarter, the right type of data needs to be fed. The real revolution of this process starts and ends with data.
Data annotation is the initial step to ensure AI and ML projects get the fuel to scale and achieve tremendous accuracy. Data annotation is the base for training a machine learning model with what it needs to understand and how it discriminates against various inputs to come up with accurate outputs.
There is image annotation for computer vision-based applications, text and voice annotations for natural language processing-based applications.
Data Labeling is needed so that any type of machine learning model does not have a difficult time in computing necessary attributes.
For example, home insurers can use computer vision applications to scan through the satellite images of a property to determine if the property is prone to flooding. For machines to understand attributes in each image accurate and fast data annotation is required. This structures the input data and gives the insurers the insight they desire.
This has been a real challenge to be tackled by the insurance companies. Recently Anadolu Sigorta, one of the largest insurers in Turkey, employed a team of 50 people. All of them were hired to check each claim for fraud based on the team’s collective experience.
This team was later replaced by an AI system and the predictive analytics mechanism of AI gave them an ROI of 210% within a year of using intelligence to detect fraud. Approximately 25,000 claims are scrutinized for fraud each month and it takes about two weeks to manually check the authenticity of each claim.
By adopting AI, Anadolu Sigotra saved $5.7 million in fraud detection and prevention costs.
Data Annotation Plays a Key Role In Fraud Detection
Identifying anomalies is the less common but most effective way to detect fraud. The anomaly detection approach is similar to other AI applications in that their machine learning models are all trained on a stream of labeled data.
The idea to use Data Annotation is simple, the claims arriving for approval are read by the machine and there are signals that need to be identified by the machine to gauge if the claim is genuine or fraudulent. These signals are read and categorized effectively by the task of data annotation.
The machine learning model needs to be fed with big data hence the task of data annotation needs to be automated because there are literally thousands of signals to look at and determine the possibilities of fraud.
Digital Assistant in Insurance
Let’s assume you are out for a drive to go pick groceries from the supermarket and while parking your car you hit the pole. Now in an everyday scenario, you can either apply for a claim later or completely ignore the damage.
But what if you can apply for the claim in less than a minute and get instant approval? Would you go for it?
I guess most of you will definitely go for it. Claims submission is a tedious process if done traditionally. But with AI-powered digital assistants, the time taken to get your claims approved has reduced drastically.
Companies are now launching smart apps where people get assisted by digital assistants and can be guided to take pictures of the damage, mention the situation verbally and submit their claims request. It’s just like telling a friend to take care of the car – that’s how easy AI technology has made things for us.
Digital Assistants also come in handy when you want to interact with customers who have approached the chatbot. Conversational AI comes into play where machines understand the customer’s past preferences and suggest relevant and customized insurance products.
Data Annotation for Building Smart Digital Assistants
By now you must be aware of how data annotation is used in NLP and CV applications. These annotation techniques come in handy while building a smart chatbot.
Manual annotation is out of the question when building a digital assistant. The data is huge and hence automation is much sought after to annotate every image and conversation with precision and accuracy.
Labellerr is a data-annotation platform that provides simple, clear, and easy to use UI with seamless UX to perform boundary box and text classification, entity recognition, etc. annotation on different types of unstructured/ semi-structured data catering to a wide array of industries like Insurance, Banking, Retail, Health care, E-commerce, Hospitality businesses to name a few.
A never-ending battle in insurance is determining the price of the insurance product. Insurance companies bear a lot of risks and inaccurate pricing proves to be costly for insurance companies. This is the reason why actuaries spend hours on fine-tuning pricing models.
It is important to take into consideration multiple factors while deciding the premium and actuaries today are using AI to help them estimate the risk. The machine learning models are effective in measuring how much a customer is expected to claim, based on information available at the time of underwriting.
Annotate Your Training Data with Labellerr
Experience the truly automated machine learning experience with Labellerr’s complete ML suite. Just plug in your data via a range of connectors as FTP, Local storage, Google Drive, AWS S3, Azure Blob, etc.
Allow our inbuilt Auto ML feature to suggest annotations based on your requirement. Leverage the Auto-label feature to annotate your data with 10x speed and save crucial man-hours. Get a list of confidence scores of the assigned labels and verify only those with a low score.
Connect Labellerr to the Cloud-Based Compute service of your choice with assured data privacy and train your machine learning models on the go. Without the hustle of downloading the data, Service-specific data conversion formats, Data Leakage to name a few.
Labellerr’s community service initiative
As a part of our community service initiative, we have created a GitHub repository, wherein we list the implementations and walkthrough guides of tools, technologies, state of the art algorithms catering to the latest developments in the field of Deep Learning and do our bit in building a strong community of deep learning enthusiasts.
Head over to our blog where we regularly write about the industrial and corporate use-cases of Deep Learning. The recent advancements in the field and how the current industry is accepting them, building over them in pursuit of solutions that were once deemed unachievable.
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