Innovating Financial Services

Machine Learning Applications

We can help you transform information-intensive business processes, reduce manual work and errors, minimize costs, and improve customer engagement.

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Intelligent Automation

Accelerate Digital Transformation with
Intelligent Business Automation

Strengths

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.

Evolution

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.

Productivity

The purpose of utilizing intelligent business automation is to drive a much more productive relationship between people and digital systems.

Why is machine learning (ML) such an important item on the Financial Services industry IT roadmap?

Risk modeling

In auto insurance, 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 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.

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 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.

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