The platform, built in house and slated to be launched later this year, is an example of the broader financial-services industry trend of using AI to detect patterns in transactions that could signal criminal behavior. The platform is cloud-based, meaning that Visa's researchers and engineers can access it online from anywhere. "One of the transformative technologies of this era is going to be AI," said Rajat Taneja, executive vice president of technology and operations for Visa, the largest U.S. card network by cards in circulation and transactions. "There is a perfect combination right now of computing resources, algorithms, data and people that's allowing this incredible innovation," he added. The banking industry is expected to be the second biggest spender on AI systems this year, behind retail, according to market-research firm International Data Corp.
Machine-learning algorithms are ubiquitous these days. Technology giants like Netflix Inc., Amazon.com Inc. and Google Inc. use them to suggest items customers might like based on their past browsing. Scientists use them to identify gene mutations associated with treatment resistance or amenable to targeted drug therapy. And doctors use them for image classification, early disease detection and better treatment outcomes. These algorithms can improve quality of life and can even help save lives.
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.
Machine learning has been instrumental in solving some of the important business problems such as detecting email spam, focused product recommendation, accurate medical diagnosis etc. The adoption of machine learning (ML) has been accelerated with increasing processing power, availability of big data and advancements in statistical modeling. Fraud management has been painful for banking and commerce industry. The number of transactions has increased due to a plethora of payment channels – credit/debit cards, smartphones, kiosks. At the same time, criminals have become adept at finding loopholes.
Deep learning is a subset of machine learning, a branch of artificial intelligence that configures computers to perform tasks through experience. Deep learning, an advanced artificial intelligence technique, has become increasingly popular in the past few years, thanks to abundant data and increased computing power. It's the main technology behind many of the applications we use every day, including online language translation and automated face-tagging in social media. This technology has also proved useful in healthcare: Earlier this year, computer scientists at the Massachusetts Institute of Technology (MIT) used deep learning to create a new computer program for detecting breast cancer. Classic models had required engineers to manually define the rules and logic for detecting cancer, but for this new model, the scientists gave a deep-learning algorithm 90,000 full-resolution mammogram scans from 60,000 patients and let it find the common patterns between scans of patients who ended up with breast cancer and those who didn't.