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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 Price Optimization
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 somecases, 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 bothconsumers and insurance companies.
AI-Enabled Fraud Detection
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 developeda 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.
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.
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 deploy machine learning applications across many business processes and flows such as recommendation systems, predictive analytics, AI-enabled decision making, natural language processing, image processing, and more, in order to drive value from increased efficiency.
starting with high-quality data we ensure the accuracy of what-if analysis and support consultations so that you can evolve your machine learning algorithms as your data volume grows. Applications tailored for machine learning in financial services include machine learning consulting services as well as development services.
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