AES, a leading renewable-energy company, was faced with the challenge of accelerating their transition to renewable energy at scale. This business transformation necessitated a digital and AI transformation to better predict and optimize the energy output from renewable sources, predict failures, and optimize load distribution. The company had to deal with the complexities of wind-turbine predictive maintenance, energy bidding strategy for hydroelectric power plants, and smart meters. The maintenance of wind turbines, which have numerous moving parts subjected to harsh environments, was particularly costly and time-consuming. The company also needed to optimize its energy bidding strategy to maximize revenue from its hydroelectric power plants. Additionally, the company had to manage over one million smart meters, which sometimes had maintenance issues or were subject to misuse.

The New South Wales (NSW) Government wanted to build out its data practice and initiatives. They needed to enable its analysts to draw upon data science and automatic machine learning platforms to help find answers, pinpoint solutions and use data to create better services for all. The government was looking for a solution that could improve the accuracy of its predictive models and empower its team of data scientists to build models faster.

Organizations responsible for fraud prevention are facing a host of challenges at the transaction, account, and network-level to detect fraudulent behavior and suspicious activities. Fraudulent transactions are rare, but costly if they aren’t detected. In the credit card business, for example, third-party fraud accounts for roughly 4 out of every 10,000 transactions. Modeling rare events is difficult, like finding a needle in a haystack. For best results, gather as much data as possible, and use the most advanced techniques available.