Innovating in Financial Services

Machine Learning
Data-Driven Services

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integrating machine learning

What is the business value of Machine Learning?

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 services

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 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, 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 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.

machine learning solutions

How can we help you?

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.

Digital Acceleration Platforms

Data and Analytics Software Solutions

Software Engineering Services

Consultancy Services

blog

Articles and Insights

The various ways in which the world will be even better after the pandemic | Opinion piece by Adrian Blidarus for Biz Romania

Digital transformation is as much about having the right culture and partnerships as it is about tech itself. Read the full article below.

Media

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The state of software outsourcing

Understanding the role of software outsourcing. Why companies are outsourcing software engineering work. More details here.

Best Practices

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How electronic signature technology enhances business processes

All about Electronic Signature procedure. Find out how Electronic Signature technology can help the customer onboarding process. Read the post.

Best Practices

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Augmenting Underwriting Referral Decisions to Maximize Insight, Innovation and Impact

The novel COVID-19 pandemic has stimulated a greater reliance on digital delivery and many insurers have found that their own e-trade platforms are not robust enough. This issue may have only been extrapolated during lockdown.  It is anticipated that the use of e-trade is set to increase with many insurers exploring placement of further complex risks electronically.

Use Case

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Biometric Authentication with Facial Recognition Technology

Explore the Facial Recognition process. Find out how Facial Recognition can help the customer onboarding process in Banking, Insurance and more. Read the post.

Best Practices

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Make a difference in your digital transformation roadmap

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global hq & uk office London, United Kingdom

New Broad Street House, 35 New Broad Street, London, EC2M 1NH

romania hq & delivery center Bucharest, Romania

4D Gara Herastrau Street, 2nd Floor
Building C, 020334

+40 31 425 19 08

engineering delivery center Craiova, Romania

30 Stirbei Voda Blvd,
Malmo Business Center, 200423

+40 35 142 36 80

let’s get in touch E-mail us at

accelerate@softelligence.net