Lenders need tools they can employ during the loan application process to advance an application to the next step or divert potential customers to alternative products that are a better fit. Implementing artificial intelligence (AI) models that facilitate fast credit reviews and even approvals can help lenders increase the quality, number, and amount of loans they grant without taking on unacceptable levels of risk.

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

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.

A multinational financial services company wanted to explore improving its collection rate and process but did not have the in-house talent it could dedicate to the project. With billions in assets and more than 25 million customers throughout the world, the bank needed to integrate new sources of highly granular credit data with historical data from a variety of structured sources and semi-structured. Their collections process lacked prescriptive analytics to optimize which loan they reached out to each day and through which channel. It was also having productivity issues and struggling to launch new credit products because of inflexible data transformation tools that couldn't accommodate new variables.