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Artificial Intelligence Is Transforming Fraud Management

Read the guide to learn about the twelve critical questions you should ask when evaluating AI / ML platforms.

Twelve considerations when evaluating AI vendors for Financial Services thumbnail Twelve considerations when evaluating AI vendors for Financial Services thumbnail

Volume & time related requirements, combined with high regulation levels, require a different approach to AI than other industries. Use our Executive guide to evaluate AI platforms that meet the unique business & regulatory needs of financial services. In the guide you’ll learn to:

  • Perform “whole” AI offer evaluations that go beyond the technology.
  • Assess architectural implications for ongoing innovation in a very fast-moving industry against TCO.
  • Understand how platforms increase your teams’ productivity & collaboration across the entire AI lifecycle with AutoML & MLOps, dramatically improving your time-to-market in the most dynamic market conditions.
  • Learn about platform capabilities implications for your bottom line & the criticality of AI explainability and model fairness and more.

Read our executive guide to learn about the twelve critical questions you should ask when evaluating AI and machine learning platforms.

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AI Transformation in Financial Services

In financial services the competition for customer share of wallet is intense with firms looking for every advantage in marketing while fighting fraud, money laundering and other issues. Companies that are making extensive use of AI are reaping the benefits of increased customer satisfaction and loyalty, decreased fraud, and reduced regulatory penalties which adds to their bottom line. Use-Cases and Customers

AI Helps Retain Valuable Banking Customers

Customer Churn Prediction

AI is a great solution for customer churn prediction as the problem involves complex data over time and interactions between different customer behaviors that can be difficult for people to identify. AI can look at a variety of data, including new data sources, and at relatively complex interactions between behaviors and compared to individual history to determine risk. AI can also be used to recommend the best offer that will most likely retain a valuable customer. In addition, AI can identify the reasons why a customer is at risk and allow financial institution to act against those areas for the individual customer and more globally.

Customer Churn Prediction Customer Churn Prediction

Customer Stories Beating Fraud

Agus Sudjianto

EVP, Head of Corporate Model Risk, Wells Fargo

"Managing machine learning model risk is of the utmost importance in heavily regulated industries such as finance; in particular, to manage potential risks due to bias/fairness, conceptual soundness, implementation, and model change control"

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The automation of the data science process reduced time and costs. And time is money. So, you can do more with the same amount of time. It's possible to deliver more value to the business, develop more use cases and focus the data science effort in the use case instead of development tasks.”

Ruben Diaz, Data Scientist, Vision Banco, Vision Banco

Rafa Garcia-Navarro

Co-Founder, CEO, and Chief Data Scientist ,

"When it comes to developing artificial intelligence products the deployment is where most platforms fall short and with H2O we've got the mojo implementation that essentially simplifies that to the point of being a java object that you incorporate to your platform. That's fantastic!"