Over the last couple of centuries, the history of manufacturing has changed dramatically. As the world enters the fourth Industrial Revolution, or ‘Industry 4.0’, these changes have become all the more profound.
Over the past decade, the exploitation of personal data has been at the forefront of public concerns about digital technologies. However, as more and more businesses use AI, the debate is entering a new phase.
All of us make mistakes and (try to) learn from our experiences. Machine learning is pretty similar, but unlike us, machines can learn and improve automatically. There exist many types of ML techniques, all with different learning methods. What are those?
The trade sector is growing more and more competitive with each day, making businesses search for new ways to boost the efficiency of their supply chain processes. Can predictive analytics help?
Traditional methods for detecting money laundering have become outdated in our global, web-based economy. That’s why we need to add ML to AML: it gives us a unique opportunity to take anti-money laundering efforts to the next level.
The capabilities of artificial intelligence cannot be overestimated. The technology has made its presence felt in a variety of industries, the insurance sector being no exception. But how exactly is AI set to help insurance companies make the grade?
Fraud attacks have grown in sophistication. The concept behind using machine learning in fraud detection presupposes using algorithms that detect patterns in financial operations and decide whether a given transaction is fraudulent.
From investment decisions to enhanced efficiency, ML in FinTech is gaining more and more popularity. And since many financial institutions have invested heavily in deploying machine learning technology, is it a pairing made in financial heaven?
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