Digital transformation and Machine Learning technologies enable automation which is actively been used in the car insurance industry. It enables quick vehicle damage detection, improves management, cuts employee expenses, and allows to improve the overall quality of service.
The article was written with the participation of an expert from a Solution Group – Max Galaktionov, with 15 years of expertise in e-commerce, social networks, CRM, ERP, fintech, and cloud solutions.
Digital transformation in the car insurance field: general overview
Car insurers need to perform many daily operations, including validation, data processing, management, and storing of huge volumes of data generated by different parties. Moreover, the variety of cars increases as well as the number of insurance claims, and car rental services have to adjust their calculations accordingly.
Automated car damage detection with AI for remote assessment
The insurance sector has to stick to strict regulations which sometimes cause delays in obtaining insurance for its customers. McKinsey estimates that AI investments could potentially cost insurers as much as $1.3 trillion annually. However, the losses caused by fraud and inaccurate assessment overreach this sum considerably.
The process of analysis of insurance claims is often delayed because the inspection involves human intervention. AI-powered technology allows for automatic car repair detection and auto-detection monitoring with the possibility of manual intervention.
The main challenges for car insurers
- Processing of big volumes of data. They need to be able to quickly assess and analyze data from various sources and provide exact estimations.
- Provide exact vehicle damage detection and assessment. It also requires analysis and estimation of health damage, medical services, etc. inaccurate and delayed estimates will spoil the relations with customers and the company’s reputation
- Handle IoT-connected car data. Nowadays, roads are filled with cars equipped with IoT sensors. These vehicles daily generate big amounts of various types of data that demand proper data processing. AI technologies help to cope with the analysis of data generated.
- Driver behavior monitoring. Machine Learning enhanced solutions help in monitoring drivers’ behavior, thus you pay the way you drive.
- Lower cases handling expenses. Automation of request handling allows employees to focus on more complex cases and cuts time and resources spent on case handling by generating automatic responses.
Why do insurance companies need AI-powered solutions?
Automation of daily operations, lower expenses, and data-driven decision-making are among the key factor why businesses are actively implementing machine learning models and AI technologies. And insurance companies are not an exception.
AI-powered solutions enable more efficient insurance claim management, lower expenses, increase the quality of service and enhance customer experience.
- Outstanding data capabilities
It is possible to train machine learning models in accordance with your needs. Based on exact data analysis, insurance companies can conduct better risk assessments, provide exact calculations, offer personalized customer experience, and assist in better detection of fraudulent cases.
- Expended range of services
AI significantly expands possibilities and enables to the expansion of the variety of services. It enables au companies to offer personalized quotes for their customers. Personally adjusted prices based on the owner’s driving patterns, speed patterns, traffic environment, and the number of miles driven.
- Remote car diagnostics
Car insurers can exactly evaluate the vehicle conditions based on gathered and processed data generated by internal car sensors. It also allows analyzing damage based on video and image processing taken from inside or outside cameras or smartphone-made videos.
- Possibility to recreate car incident
Based on the sensor and camera-generated data and enhanced with AI-based vehicle damage detection it has become possible to speed up the incident investigation process. Moreover, having the exact timeline details it has become possible to recreate the incident fully.
- Enables service personalization
A recently emerged user-based insurance approach relies on user behavioral patterns and enables more personalized claims processing.
The policy may depend either on the distance driven or the cost is adjusted to the driving patterns.
- Car damage assessment
Enables insurance companies to assess the damages remotely with the help of AI-based car damage detection. It allows for processing insurance claims quicker since experts can get instant damage reports and estimate repair costs within the shortest period.
AI-based car damage detection
Car damage assessment is a set of tools and processes that allows companies to provide users with automatic analysis of car damage. It’s important because it allows getting damage reports and repair cost estimation in minutes without waiting for an inspector.
In general, Damage Assessment includes the next steps:
- Model recognition;
- Location of damaged component and damage severity;
- Repairs estimation based on gathered data.
Modern technologies allow the use of AI/ML with features such as Computer Visual Intelligence, Deep Learning, and Instance Segmentation to provide services with high accuracy and fast response rate to achieve desired goals.
Vehicle damage detection has become possible thanks to proper training data and the installation of the necessary Machine Learning algorithms. The processing of each insurance claim presupposes the following steps:
- Process the user’s image of the damaged vehicle;
- Analyze car model;
- Analyze car angle;
- Locate damaged car parts;
- Analyze component damage severity;
- Prepare report.
Possibilities of AI-powered vehicle damage detection
The most common approach is to build up a system as a set of layers or gates. On each layer, the users’ photos of damaged vehicles will be processed and analyzed with AI-based tools which are the most reliable for a certain goal. Such layered architecture allows us to learn and classify data with the best AI model prepared for the exact purpose of the layer.
For example, when it is needed to understand at what angle a car was hit, the model specifically trained on a dataset of millions of different cars from different angles was provided, which will be used.
AI possibilities that enable you to provide more client-based services with better options for business
License plate recognition
This is a process using OCR tools, like Tesseract, OpenCV, to be able to make vehicle damage detection and recognition more accurate.
Damage location and severity including scratches, the whole car
Generally, service allows quick understanding and assessment of the level of damage to a car and the location of the damage. It also allows for detecting whether the car is really damaged as a result of a car incident, or is it simply dirty.
Car color recognition
Detection service that can be helpful to select the correct paint color for repairs estimation.
Fake image detection
This service may be helpful to prevent fraud by understanding is the image was modified or not.
Car model recognition
Helpful service to analyze body shape, damaged components, and overall repair cost. Very effective to build this using Keras, and Tensorflow.
Damaged parts of the car
Very helpful service based on Computer Vision Technique allows predicting the level of damage on certain car parts to prepare repairs report.
Segmentation of damaged car parts
Service using Computer Vision to define the damaged area.
There is a good practice to place AI-based and image recognition systems in clouds, providing customers with Software as a Service and enabling your business to rapidly evolve and grow. It allows you to save costs by running services from time to time (like when you need to retrain your models on a new dataset or run recognition service on-demand only) unlike your own servers, you have to pay for electricity and support all the time.
There are a huge variety of fine-tuned services for specific goals (like AI-training, dataset storage, or providing user interface) on most of the cloud providers, such as Amazon, Microsoft, Google, etc.
AI enables an analysis of each user`s activities and needs in order to provide personalized functionality for each of your customers. Additionally, AI in cooperation with machine learning not only checks the interaction with your SaaS solution but also is able to predict future steps or behavior of your users.
With the help of open API, you can integrate your SaaS app with the APIs required by your personal company needs in order to enhance business productivity. Actually, it splits your SaaS solution into different parts of functionality and each user can choose which parts correspond to the needs of their organization more.
Challenges faced while building the AI-powered damaged vehicles assessment solution
As is often the case, companies face several challenges while building and integrating AI-powered car damage assessment solutions.
- Training data
Primarily, you need to focus on building the right dataset to train the AI algorithms, since they are no publicly available datasets of damaged cars. Thus, the company needs to build its own data set, including various types of damaged cars, parts, and details.
- Choice of the right architecture
To ensure the maximum accuracy of data assessment, such solutions need to daily process huge amounts of data. Storing, training, and deploying such heavy datasets over the cloud would require the choice of the right architecture.
- Data security
As a rule, AI-powered car damage solutions are running in the cloud, thus, generating huge amounts of sensitive data. Thus, you need to take into account all risks and choose heavily structured frameworks to ensure customer data security.
- Need for human involvement
Although the process could be absolutely automated, it still needs human involvement to detect and avoid fraudulent insurance cases.
Steps to build efficient custom car damage assessment software
As is already mentioned, ML models should be properly trained to be able to detect and assess the available damages. Here are the steps you should follow to properly process and structure the data so that the system could recognize it:
- Gather data
Gather and organize a huge amount of photos and videos to train your ML models properly.
- Data licensing
The next step requires licensing of the gathered dataset to enable the accurate estimation of the data damage.
- Data annotation
After the data is collected and licensed, you need to be able to accurately assess real damages. Data annotators will help to annotate scratches, dents, and dings on various parts of the vehicle.
- Data segmentation
As soon as the data is annotated, it should be properly segmented and classified.
The right team to build you a custom solution
When gathering a team for building such service as AI-based Car Damage Recognition you have to always have in mind that you require to have ML specialists who will be responsible to rebuild service and models based on user feedback to keep your system in the best conditions.
There are 2 major groups of potential customers of Damage Assessment Services:
- Decrease the level of fraud
- Improve signing speed and efficiency
- Decrease operational costs
- Increase retention rate
- Increase customers happiness
Why Altamira team is the best choice for you
Understanding the challenges and pitfalls of implementing AI-based solutions, our experts will not only help to cope with huge amounts of data, however, will also guarantee smooth implementation of the solution, and proper choice of the relevant tech stack as well as absolute data security.
Custom solutions built by our team definitely guarantee:
- Compatibility and scalability: solutions built by our team could be easily integrated into the current business flow. Moreover, the solution could be easily scaled in case of growing business needs.
- Transparency: customer can take part in any processes related to the development, we guarantee no hidden costs and unplanned expenses
- Fast time to market: we release the product as soon as it is possible allowing you to test it and assess its efficiency, thus saving your time, and money and giving you a competitive advantage
- Security: data security is a priority for us, thus our QA team actively uses various types of testing from the preliminary stages of development, including the backup testing guaranteeing sensitive data security. Additional components such as multi-factor fraud detection and in-time risk detection enable you to save time and your positive reputation.
- Business performance boost: insurance companies will benefit by transforming the claim value chain. Our custom-built software will speed up time-consuming claim settlements and create a better customer experience, while also increasing efficiency.
Altamira team will supply the best specialists for your project, offering flexible cooperation models and the highest quality of services. You may choose the one that best suits your budget and business needs, including a dedicated team model and outstaffing.
Our experts have advanced experience in building AI solutions and are aware of all industry business needs and requirements being able to impress you with a high quality of service.
To sum up
Digitalization of the car insurance business accelerates claims and reduces budget loss. Innovative solutions designed for faster claim processing aren’t just for customers. They also aim to reduce processing costs for insurers, reduce human error and automatize the process speeding up the service and making it more personalized.
Artificial intelligence (AI) helps insurance companies to evaluate risks, prevent fraud and reduce human mistakes. It gives insurers an advantage in offering customers plans that best fit their needs. Customers benefit from streamlined customer service as well as claim processing provided by AI.
Machine learning is used widely for automated manual tasks. Using AI technologies and an algorithm framework AI will be able to identify damaged components accurately and predict the extent of damage to the structure and determine repair costs and the total cost if necessary. It is possible using image/video annotations of computers to train ML models. This ML model extracts analysis and provides insights that result in a quick inspection that is more accurate.
In training your Model Learning Machine for vehicle detection and assessments, the first step is selecting a highly qualified Training Data, followed by data analysis and segmentation.