Identifying potentially impactful use cases is one of the most cited roadblocks for organizations seeking to leverage AI in their business. To complicate things further, best practices dictate that you should have a portfolio of use cases ready to experiment with. If finding one is a challenge, developing a whole portfolio of use cases may prove to be very difficult.

In this guide, we'll cover:

• A wide variety of AI applications for enterprises
• The challenges that led each business to seek help from AI & machine learning
• The advanced solutions that were built and deployed to overcome each challenge
• The documented financial impact experienced by each client

If you have mountains of data at your fingertips that you're not using, you risk falling behind your competition. But, if you actively work toward becoming a more data-driven organization and closing the pervasive data science skills gap, you can promote internal alignment around how data is used, make a tangible impact with AI, and come out on top. The best time to start optimizing how data is viewed and used at your organization is right now, and in this whitepaper, we're going to walk you through how to do just that.

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.

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.