The Must-Have Technology for the Energy Sector 
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With the capability of integrating data from different systems and processes, hyperautomation provides new valuable insights that can be used to better energy production and distribution, improve overall grid management and reduce the overall environmental impact in the energy sector. Hyperautomation helps oil & gas, utility and virtual power plant companies to quickly and effectively respond to changing market conditions and demands, such as increasing the use of renewable energy sources and the need to comply with regulatory requirements.

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Powering New Value: What’s Enabled with Hyperautomation 
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With its powerful AI automation and analytics capabilities, Waylay’s hyperautomation platforms provides companies of any size in the energy sector with a technology-driven approach to streamlining operations, increasing efficiency and productivity, and enhancing reliability, sustainability and decision-making with our data orchestration solution that automates and integrates multiple processes and tasks using IoT, IT and cloud data in the most flexible way.

Waylay’s Enterprise and Digital Twin platforms can be applied to automate and optimize various processes in field operations, including:
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• Predictive maintenance to reduce equipment downtime and maintenance costs
• Supply chain management to improve operational efficiency
• Resource planning and optimization to optimize energy generation and distribution
• Customer service and billing to improve customer experience and reduce operational costs.
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Fraudulent Credit Card Transaction Prevention
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With the extensive use of e-commerce, credit card fraud has become a major issue that every bank, payment processing platform, and web-shop is facing. Big loss in business is not only related to fraudulent transactions, but also in denying legitimate transactions (false positives), which are deemed to be fraudulent leading to additional loss in revenues, high levels of customer dissatisfaction, and on occasion posing a risk to customers traveling across states and internationally.
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Coping with Legacy Systems
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Conventional fraud detection approach is setting up rules which are executed against every transaction. These rules are “hand crafted” — it often takes an enormous amount of time and expertise to configure and tweak them. They are meant to find fraudulent activities and patterns based on historical records and expert knowledge, but they are often hard to change. Which means it’s very hard to keep up with new patterns, should those arise . Still, they are easy to verify and interpret and have been used and deployed over decades. In addition, these rules are often implemented as database stored procedures, making them applicable only to offline/bulk processing use cases.
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Fraud Detection with Machine Learning
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As fraud patterns are changing rapidly, new fraud detection methods are needed — moving from a reactive to a proactive approach. In this regard, implementation of efficient fraud detection algorithms using machine learning techniques is a rather straightforward idea. Such an approach enables a system to adapt to new, unknown fraud patterns in real time. Machine learning models are also often hard to interpret. Even for trivial decisions, looking at the confidence score doesn’t yield to anything obvious. Moreover, all ML techniques result in a confidence score, where at the end of a day, someone needs to put a threshold on whether a transaction should be let through or not.
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