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?
AI is becoming a universal tool for game design. Despite video game artificial intelligence still being in its infancy, game companies have already started to recognize attractive benefits such as enhanced player experience and cost reduction.
Machine learning models are expected to evolve and become more precise in the data-driven business world as more data is collected. MLOps, the unification of machine learning workflows and DevOps principles, makes sure that expectation is met.
Artificial intelligence (AI) plays a pivotal role in cybersecurity, bringing smarter and faster threat detection to the forefront. By leveraging AI, we can now predict and neutralize cyber attacks with precision, greatly enhancing our digital defense capabilities.
AI technology has become a critical disruptor in almost every industry, and banking is no exception. Here’s everything that AI holds in store for the future of the industry.
Rapidly growing revenues generated by AI applications are attracting newcomers and fueling the industry’s development. Developers are always on the lookout for more efficient machine learning models, languages, frameworks, and libraries. In many aspects, the right choice of technologies determines a project’s level of success.
As computing power and software complexity grow, so does the potential for artificial intelligence (AI) to solve the world’s problems. Although most people don’t associate the mining industry with such things as advanced systems technology, AI is changing the narrative.