Leveraging AI’s data processing capability, insurers can enable a strategically built sophisticated environment where information pertaining to business and customer interactions.
AI-based chatbots can be implemented to improve the current status of the claim process run by multiple employees.
Artificial Intelligence and machine learning create great opportunities to read and analyze data from a variety of sources.
The performance of AI in private vehicles is not limited to strict requirements, such as safety. AI can be used for more control and entertainment inside the car.
It involves Network Planning and Design, Network Tuning, Network Audit and BenchMarking, improves customer loyalty, End-user performance optimisation, Network Planning, and investment
It involves AI-driven Predictive Analytics, Telco Data Analytics, Interactive Voice Response Systems, Data Center Management, Predictive Equipment Analytics.
Before we adapt to fully-autonomous cars, it makes sense to evaluate the capabilities of AI by incorporating driver-assist features. AI uses several sensors for blind-spot monitoring, collision detection, pedestrian detection, lane monitoring, etc.
While performing this task manually can take too long and prone to errors, document capture technologies enable insurance companies to automatically extract relevant data from application documents and accelerate insurance application processes with fewer errors and improved customer satisfaction
AI can assess customers’ risk profiles based on lab testing, biometric data, claims data, patient-generated health data, and identify the optimal prices to quote with the right insurance plan
Insurance companies need to generate high volumes of documents, including specific information about the insurer. While creating these documents manually consumes time and prone to errors, using AI and automation technologies can generate policy statements without mistakes.
29% of insurers have admitted to lying to their car insurance company to gain coverage in the US. AI-powered predictive analytics and text analysis tools might detect fraudulent claims based on business rules with data captured from the claimant’s story.
With the increasing popularity of IoT devices in their daily lives, there will be more data to process for insurance companies to assess customer risk profiles better.
Working together with the client’s data science team and estimators, we helped create three AI models. The first was to detect and classify car damages from the images, isolating where in the image there was damage and what type of damage was represented
Now for the hard part. The ability of an AI model to accurately interpret images was only half of the equation. The AI model needed to be trusted by the estimators as an important augmentation of their analysis.
As previously noted, non-renewals cost insurers money. Among our clients, we saw one insurer who tackled this problem to reduce churn by 1% and variable costs by 24%. This resulted in an estimated savings of over $400,000 per year.