"Machine learning" is not just a buzzword for futuristic applications; it is the concept of machines carrying out tasks on their own that would typically require human intelligence. Its emergence is very much happening now. It is at the top of Gartner's hype cycle. In fact, Gartner predicts that by 2022, more than half of data and analytics services will be performed by machines instead of human beings, up from 10 percent today. And while not all machine learning use cases include real-time analytics, there is a definitive growth trend in the market for real-time decision making powered by machine learning.
Machine learning is a field of science that offers machines an ability to understand data and carry out processes just as a human would do. The ML technology uses complex algorithms to analyze large data sets and find data patterns that help in business decisions. This is why machine learning can detect fraud in the system easily. It is, in fact, used for various other purposes such as spam detection, product recommendation, image recognition, predictive analysis, etc. Gartner predicted that by the year 2022, the machines would be analyzing 50% of the data, which is only 10% more from the present scenario. Since machines are far better at detecting patterns, ML can analyze huge sets of data in one chance and find fraud-related behavior through cognitive technology.
The push for operational analytics that seeks to eliminate the requirement for constantly moving data between storage and databases to support transaction and analytical workloads is fueling the growth of translytical data platforms. The framework uses a single data tier that can serve both transactional and analytical workloads. The requirement to reduce data movement between data silos and technology stacks along with rise of machine learning and streaming analytics has fueled the rise of a maturing translytical data platform sector. Market watcher Forrester released a market survey earlier this month that identifies and ranks a dozen key players in the nascent analytics sector. The market researcher also identified the top translytical database workloads, including: real-time applications; Internet of Things (IoT) analytics operational data; connected data apps; and continuous learning in which translytical databases are used to train and retrain machine learning models.
In 1997 when Deep Blue, a supercomputer, beat the then chess champion, Garry Kasparov, we all were taken aback. That was more than 20 years ago and most of us did not even know that computers like that even existed. In late 2017, AlphaZero taught itself how to play chess under just four hours and beat the world's then best chess-playing computer program. Remember, AlphaZero, the game-playing AI created by DeepMind, was not taught any domain knowledge but the rules of the game. Such is the power of machines to learn and improvise and industries across the world are tapping a machine's ability to learn and improve from its experience without being explicitly programmed.
The greatest challenge when talking about artificial intelligence/machine learning is actually in understanding what data sets we are looking at, and what model/combination of models to apply. Amazon's Machine Learning offering is one example of an automated process which analyses the data and automatically selects the best model to use in the scenario. Other big players who have similar offerings are IBM Watson, Google and Microsoft. Provenir's clients are continually looking at new and innovative ways to improve their risk decisioning. Traditional banks offering consumer, SME and commercial loans and credit, auto lenders, payment providers and fintech companies are using Provenir technology to help them make faster and better decisions about potential fraud. Integrating artificial intelligence/machine learning capabilities into the risk decisioning process can increase the organization's ability to accurately assess the level of risk in order to detect and prevent fraud. Provenir provides model integration adaptors for machine learning models, including Amazon Machine Learning (AML) that can automatically listen for and label business-defined events, calculate attributes and update machine learning models. By combining Provenir technology with machine learning, organizations can increase both the efficiency and predictive accuracy of their risk decisioning.