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How AutoML is Revolutionizing the Insurance Industry

How AutoML is Revolutionizing the Insurance Industry-2

Automated machine learning helps businesses across various industries  - healthcare, marketing, retail, manufacturing - to leverage on AI/ML technology. Earlier these advanced technologies were available to only a few privileged organizations with an extensive workforce. But with the advent of AutoML, organizations with limited workforce can also harness the power of machine learning

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What is Automated Machine Learning?

What is Automated Machine Learning?

Automated machine learning or AutoML includes an array of techniques that automate the iterative tasks of developing a machine learning model. If you apply traditional machine learning techniques to real-world business problems, it can be quite challenging and time-consuming. Also, it may require expert resources across different disciplines. But by automating the tasks of developing and deploying ML models, implementing ML solutions can be a cakewalk for any business. Besides, automated machine learning enables a company’s data scientists to spend their energy on more complex issues.

How AutoML can solve challenges in the Insurance industry

  1. Improving transparency through automation - According to a recent survey, only 43 percent customers have trust in the insurance industry. So, what is the main cause of this lack of trust. Mostly, the lack of transparency keeps people away from investing in insurance policies. Through AutoML implementation, the insurance process can be transparent, automatic, and seamlessly connected.  Customers can claim their insurance in a matter of minutes with a quick selfie of their car or home. Whether it’s a report of car damage or a house on fire, machine learning models will notify the respective insurer, arrange suppliers, and pay claims. This has reduced any dependency on insurance intermediaries and made the entire process trustworthy.

  2. Simplifying the complex procedures - The insurance industry is usually perceived to involve lengthy documentation and complex customer servicing process. Most individuals look for easy and straightforward user interfaces for motor or home insurance. In the coming years, machine learning could boost the sale of highly complex commercial insurance policies with an easy-to-use interface. A trained ML model can suggest appropriate policy and premium amount based on an individual’s risk taking capacity.

  3. Providing Bespoke solutions through customer profiling - Managing customer satisfaction levels and churn rate are important aspects of insurance marketing strategy. Statistical and data analysis creating exhaustive customer profiles so that insurers can offer bespoke solutions by studying the real-time needs of clients. Dashboard visualizations and robust analytics provide deeper strategic insights that help insurers in meeting customer requirements.

  4. Touchless claim settlement process -  Global insurers mostly bear the brunt of customer outrage due to inefficient claim settlement process. ML automation would pave the way for an effective, hassle-free and quick claim settlement process. The touchless claim settlement procedure can capture, report any damage, and directly interact with the customer.

A good example can be the introduction of smart chatbots for claim evaluation and policy details verification. Then it goes through a fraud detection algorithm, which triggers a message to the bank to settle the claim. Detecting fraudulent cases that are invisible to the human eye can now be done quickly, easily, and more reliably.

Related: Download the checklist that lays out everything you should consider  before implementing a machine learning project or workflow → as an automated Machine Learning platform is a dynamic platform that enables automated Machine Learning by processing unstructured data. It provides templatized and guided Machine Learning workflows in the fields of computer vision and Natural Language Processing(NLP).  Computer vision is the ability of machines to see the visual world and interpret it beyond human vision. can help in the analysis and categorization of images in an effective and scalable manner through its state-of-the-art computer vision technology.

Natural language processing plays a critical role in deriving business intelligence from raw business data. Skyl's powerful Natural Language Processing platform lets you work with text and help build systems based on sentiment analysis, entity analysis, entity extraction and content classification. Text data can range from multiple sources such as customer feedback, social media posts, police reports on accidents to claims adjuster notes, medical records, underwriter notes, surveys, emails, and web documents. This text analysis can help to find overall company sentiment, brand management, fraud detection, and analyze the contract’s clauses.

Below, I have discussed a few ways how NLP and computer vision can help in solving problems faced by the insurance industry.

  • Natural language processing (NLP) is the semantic text interpretation that enables systems to grasp, analyze, and understand human language. Insurers are widely using NLP to improve their claims processing and customer servicing operations. NLP is being used to scan existing policies and structure the framework of new policies to make the insurance process more efficient. NLP is also used for scanning ambiguities in claim reports for quick fraud detection.

  • In insurance, drones use computer vision to inspect property damage and assessment to check whether claims are feasible. During the catastrophic Hurricane Harvey, insurance agencies used drones to inspect road, railway tracks, oil refineries, and power lines in Houston. This made the process accurate with no scope of human error. Computer vision technology can help prospective home buyers to estimate home premium based on the satellite images of a property.

  • Building, training, and deploying models for predicting premium and losses for respective policies. The models continuously improve every time new data is received, so we can assure that you get to use the most accurate model any time.

If you would like to learn more about how automated machine learning and AI-related techniques can drive your business success in the future, visit

How used computer vision to detect damaged vehicles for motor insurance

Recognition and classification of damaged vehicles is a crucial step for claims management in the motor insurance industry. We implemented a similar use case in Skyl and created a model which could predict whether a vehicle was damaged, dented or undamaged just from the picture. Images of several vehicles were collected and a “Vehicle Insurance Damage Prediction” project was created. It’s been described in detail in the below example.

1. Creating a project provides multiple templates in Computer Vision and NLP for a guided machine learning workflow. For this project, the Image Classification multi class template is chosen. An image classification multi class template categorizes an image into exactly one class from the given options of a class.

Creating a project

2. Designing The Data set Schema

We then designed the schema of the data set through the guided flow of The data set name, description and schema is designed as per the requirements of the project. The different categories were named as “damaged”, “dented” and “undamaged”.

Designing the data set schema

3. Collecting Data

The data is uploaded using the “CSV upload” feature of Skyl. You can easily see the format in which the CSV file needs to be uploaded by downloading the schema from the button provided on the top right hand side of the drag and drop window.

Collecting data

4. Creating The Feature set

In no time, the data was uploaded to the data set and we created our feature set for Machine Learning Training. A feature set is basically a subset of your data set which is used as the input to your machine learning algorithm. also provides a summary of the feature set you are creating so you can analyse if your feature set is properly balanced and there are no biases or bad data. After all, your machine learning model is only as good as the data its being fed with.

Creating the Feature set

5. Model Training

We initiated the model training using suggested algorithms and parameters. allows you to tune parameters like batch size, number of epochs, learning rate etc. as well as suggests the best possible optimized training parameters for your model training.  

Model training-1

6. Model Deployment

As soon as the training got over, a model was created which was listed under ‘Model Deployment’. The model achieved an accuracy of 83%. Training reports for the model were also generated for metrics like loss, accuracy, recall, precision, etc. allows one-click model deployment for your models, thus eliminating all the work required for setting up a model deployment pipeline.

Model deployment-1


7. Inference API

Inference API’s were generated once the model was deployed which could be hooked in any application and can be used for predictions. inference API is easy to use and available in all major programming languages for seamless integration.

Inference API-1

Automated Machine learning has accelerated digital transformation process for most industries, insurance being no exception. is a world-class platform that aims to support insurance companies in improving operations and increasing customer retention. It helps to identify primary factors relevant for fraud detection in claims settlement, optimizing pricing strategies, and risk assessment.

Check out our demo to see how it works for your business, and opt for a customized subscription plan to create a deep model of your own.

Feel free to get back to us for any queries on how to solve ML problems using computer vision and natural language processing.