Machine learning refers to analytic techniques that "learn" patterns in datasets without being guided by a human analyst. Artificial intelligence refers to the broader application of specific kinds of analytics to accomplish tasks, from driving a car to, yes, identifying a fraudulent transaction. For our purposes, think of machine learning as a way to build analytic models and AI as the use of those models. Machine learning helps data scientists efficiently determine which transactions are most likely to be fraudulent, while significantly reducing false positives. The techniques are extremely effective in fraud prevention and detection, as they allow for the automated discovery of patterns across large volumes of streaming transactions.

We've been recently looking at how to introduce data science concepts to the wider team, including business analysts, management and engineers. This post is for anyone and everyone thats ever heard anything about Data Science but are still unclear on what it is, what it means for businesses and how to learn more. Well the term Data Science itself is heavily overloaded. It's used in a bunch of different contexts to define a whole variety of different subjects. When trying to sell a concept like this, especially to management teams or senior stakeholders, a term that means nothing and is difficult to explain will simply just be ignored.

Payment fraud is an ideal use case for machine learning and artificial intelligence (AI), and has a long track record of successful use. When consumers get a call, text, email or in-app messages from their card issuer asking them to validate a transaction, or informing them of fraud on their card, they may not even suspect that behind this bit of excellent customer service are a brilliant set of algorithms. Recently, however, there has been so much hype around the use of AI and machine learning in fraud detection that it has been difficult for many to distinguish myth from reality. At times, you might come to the conclusion that AI and machine learning have just been invented, or just been applied to payments fraud for the first time! In this blog series, I'm going to explore the five keys to using AI and machine learning in fraud detection.

The future of artificial intelligence will foster revolutionary and cost-effective solutions using machine learning, IoT, and cloud computing technologies. For this, data analysts are relying on the high computational powers of cloud-based infrastructures for optimum results. Cloud-based predictive analytics is the latest development under AI that is poised to enhance digital experiences with automated data analytics capabilities. In this blog post, we explore how cloud computing combined with AI development services is deriving cognitive insights for global businesses. While cloud computing empowers businesses with massive data storage, AI incubates this data to extract valuable insights beyond human intelligence.

Summary: If you are guiding your company's digital journey, to what extent should you be advising them to adopt deep learning AI methods versus traditional and mature machine learning techniques. By now everyone is at least familiar with using AI/ML as a required cornerstone of company strategy. Frequently this is referred to as'digitization' or the'digital journey'. There's plenty of data showing that early adopters who have gone all-in on this approach are already pulling ahead of competitors both in share and bottom line results. There's also mounting evidence that even though most companies are now aware of this need, either their planning or their execution has been half-hearted.

Artificial intelligence (AI) stands out as a transformational technology of our digital age--and its practical application throughout the economy is growing apace. For this briefing, Notes from the AI frontier: Insights from hundreds of use cases (PDF–446KB), we mapped both traditional analytics and newer "deep learning" techniques and the problems they can solve to more than 400 specific use cases in companies and organizations. Drawing on McKinsey Global Institute research and the applied experience with AI of McKinsey Analytics, we assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions. Our findings highlight the substantial potential of applying deep learning techniques to use cases across the economy, but we also see some continuing limitations and obstacles--along with future opportunities as the technologies continue their advance. Ultimately, the value of AI is not to be found in the models themselves, but in companies' abilities to harness them.

The use of advanced analytics techniques for customer care is finally moving out from the lab and into production. Machine learning and artificial intelligence can be used to find context in the tons of data that flow in and out of customer-facing organisations every day. These techniques allow a contact centre manager or agent to enrich (beyond basic caller or chat ID) who exactly is on the other end of the interaction, what that person's relative value is, anticipate why this person is interacting with the contact centre, and then both optimally route or escalate the interaction and provide the receiving agent with the right context and breadth of longitudinal data to better serve that customer. This has been a while coming. Analyst firm Forrester has been talking up "the age of the customer" - a period during which "technologies like artificial intelligence and robotics would emerge to challenge core notions of what it means to be a company, what it means to build human capital, and what it means to compete and win" - since at least 2010.

An analysis of more than 400 use cases across 19 industries and nine business functions highlights the broad use and significant economic potential of advanced AI techniques. Artificial intelligence (AI) stands out as a transformational technology of our digital age--and its practical application throughout the economy is growing apace. For this briefing, Notes from the AI frontier: Insights from hundreds of use cases (PDF–446KB), we mapped both traditional analytics and newer "deep learning" techniques and the problems they can solve to more than 400 specific use cases in companies and organizations. Drawing on McKinsey Global Institute research and the applied experience with AI of McKinsey Analytics, we assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions. Our findings highlight the substantial potential of applying deep learning techniques to use cases across the economy, but we also see some continuing limitations and obstacles--along with future opportunities as the technologies continue their advance.

Artificial intelligence promises considerable economic benefits, even as it disrupts the world of work. Three priorities will help achieve good outcomes. This new article is by James Manyika and Jacques Bughin of the McKinsey Global Institute. I hope you find it useful. The time may have finally come for artificial intelligence (AI) after periods of hype followed by several "AI winters" over the past 60 years. AI now powers so many real-world applications, ranging from facial recognition to language translators and assistants like Siri and Alexa, that we barely notice it. Along with these consumer applications, companies across sectors are increasingly harnessing AI's power in their operations. Embracing AI promises considerable benefits for businesses and economies through its contributions to productivity growth and innovation.

Artificial intelligence is poised to change the pharmaceutical and healthcare industries as it looks to streamline, speed-up and improve overall efficiency. It might still be early days but it's a bandwagon that organisations are quickly jumping on board. According to a CB Insights report, about 86% of healthcare organisations, life science companies and med tech firms were using artificial intelligence technology in 2016. Big pharma names announcing deals and applications include Bayer, J&J, Merck, Sanofi, Genentech and Pfizer. Meanwhile, more than 50% of healthcare industry executives anticipate broad-scale adoption of the technology by 2025, a TechEmergence study recently revealed, with nearly half of the respondents noting that chronic conditions will be the initial target.