Financial fraud takes countless forms and involves many different aspects of business including; insurance and government benefit claims, retail returns, credit card purchases, under and misreporting of tax information, and mortgage and consumer loan applications. Combating fraud requires technologies and business processes that are flexible in their construct, can be understood by all who are involved in fraud prevention, and are agile enough to adapt to new attacks without needing to be rebuilt from scratch. Armed with advanced data analytics, firms and government agencies can identify the subtle sequences and associations in massive amounts of data to identify trends, patterns, anomalies, and exceptions within financial transaction data. Specialists can use this insight to concentrate their attention on the cases that are most likely fraud. This guide will help you understand the complex environment of financial fraud and how to identify and combat it effectively.

In a world where everything is becoming more and more connected, Mabe, a leader in home appliances, is leveraging the convergence of big data, analytics and simulation to accelerate innovation. Martin Ortega, Senior Design Engineer at Mabe, explains how they are using Altair’s AI, data analytics and simulation solutions to uncover insights, create new business opportunities, and advance product development. Learn more - click here to read how connected products deliver big ROI.

Data drives vital elements of our society, and the ability to capture, interpret, and leverage critical data is one of Altair’s core differentiators. While Altair’s data analytics tools are applied to complex problems involving manufacturing efficiency, product design, process automation, and securities trading, they’re also useful in a variety of more common business intelligence applications, too. Explore how machine learning drives EV adoption insights - click here. An Altair team undertook a project utilizing Altair Knowledge Studio® machine learning (ML) software and Altair Panopticon™ data visualization tools to investigate a newsworthy topic of interest today: the adoption level of electric vehicles, including both BEVs and PHEVs, in the United States at the county level. This guide explains the team’s findings and the process they used to arrive at their conclusions.

Protecting consumers and enterprises involved in online transactions is just one example of how machine learning (ML) influences our daily lives. In fact, the list of use cases is already long, diverse – and growing fast. The reason is clear – ML is a game-changing tool that enables organizations to make better decisions faster. What’s more, ML is highly effective at balancing conflicting objectives.

Given the breadth and depth of potential use cases, one thing is clear – more and more people will find themselves working in environments where ML plays a critical role. And thanks to the emergence of low-code and no-code software, ML is no longer the exclusive preserve of programmers, data scientists, and people who paid attention in math class. More of us can – and will – be involved in developing and deploying practical ML solutions.

This eGuide will help you understand the key concepts behind ML, some common applications, and how ML becoming more useful to people at all levels of the modern organization.