Machine learning is an application of artificial intelligence (AI) that blends algorithms with statistics to find patterns in huge amounts of data. Any type of data which can be digitally stored– numbers, images, clicks and others – can fuel a machine learning algorithm. We can experience machine learning applications in many of the services that we use today: recommendation systems, search engines, social media feeds, voice assistants and the list goes on. In all of these instances, each platform is collecting as much data about the customers as possible and uses machine learning to make accurate decisions or predictions, for example about what customers might want next.
Machine learning algorithms bring strengths such as the ability to cut through complexity that are different from, but at the same time complementary to, human skills.
The modern workplace is transforming into a new environment where employees and their new digital co-workers can benefit together from innovated internal processes, such as chatbots and extensive customer analytics, as well as an improved way of doing business.
The purpose of utilizing intelligent business automation is to drive a more productive relationship between people and digital systems.
In auto insurance, for example, machine learning algorithms can use customer profiles and real-time driving data to estimate policyholders’ risk levels, as well as vetting prospective buyers and making decisions on whether to approve applications.
Claims handling and
Applying machine learning to the Financial Services industry enables claims processes to be handled by machine learning models – decisions on whether to pay out on claims, in some
cases, can be made without the need for human intervention.
Based on data gathered by AI and IoT, they can formulate personalized rates to potentially create savings for both
consumers and insurance companies.
Fraud classification and detection is a key endeavor for financial services companies in their search for an optimal and timely manner to manage risks.
For example, AI can be applied when coupled with a Custom Vision scenario, in which the claim details, such as the parts of the goods, which have been damaged, the severity and other information, are automatically determined by such a model and fed into a fraud detection model afterward.
AI-Driven Fraudulent Claim Detection in Insurance Applications
In our Bucharest edition of the Global AI Bootcamp, we developed
a model during an interactive application exercise that was able to detect fraud at various probability levels, based on the data used in the training session. New claims can be submitted to the service for classification. The app uses the information returned from calls to the Web Service to decide if the claims are fraudulent or not. It also shows the probability that the classification assigned to each claim is correct. It is important to note that the more claims are used to train the model with, the more accurate its probability rate will be.
We can help you build powerful machine learning capabilities: starting with high-quality data we ensure the accuracy of what-if analysis and support consultations so that you can evolve your machine learning capabilities as your data volume grows.
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