"The adoption of machine learning in fighting financial crime will likely explode as technology solutions become more effective and efficient--driven by work-stream prioritization, product maturity, and refinement of implementation processes." This was the key takeaway from a global survey conducted in 2020, "The Evolving Role of ML in Fighting Financial Crime," conducted by Guidehouse and Compliance Week, in partnership with the International Compliance Association. While survey responses demonstrated similarities between US and Europe (UK/EU) organisations, our analysis also identified notable differences among participants in both regions. This article examines key areas where European survey responses deviated from those by US or global participants. This should be of particular interest to European organisations wishing to benchmark maturity against both global and European peers.

After many fits and starts, the era of enterprise machine learning has finally arrived. According to 451 Research's Voice of the Enterprise, AI and Machine Learning survey, 20% of enterprises have already deployed the technology and a further 33% plan to do so within one year. These figures should come as no surprise: AI has the potential to benefit almost any company by automating and improving a variety of business processes. These encouraging numbers, however, do not indicate that adoption is easy; on the contrary, as with any emerging technology, there are numerous obstacles to success. The lack of skilled resources is the chief obstacle for enterprises, cited by 40% of respondents.

Gradually recovering from the effects of COVID-19 pandemic, will be a top priority for practically every firm and industry in 2021. A few organizations may get stale or never recuperate. Others will see the purge as a remarkable opportunity to comprehend and improve their data and analytical assets, operationalize and update their model production process, and promise clients that their machine learning adoption can be trusted. Everybody is hoping to improve over their present AI and ML insights, for example, a bank improving fraud detection, a medical care provider moving to telehealth, a retailer or manufacturer attempting to make your supply chain more proficient. All through the recent years, there have been a couple of revelations in machine learning and artificial intelligence.

As machine–learning models become more prevalent in finance, experts warn that banks need to be on the lookout for a lurking threat: data poisoning.

After many fits and starts, the era of enterprise machine learning has finally arrived. According to 451 Research's Voice of the Enterprise, AI and Machine Learning survey, 20% of enterprises have already deployed the technology and a further 33% plan to do so within one year. These figures should come as no surprise: AI has the potential to benefit almost any company by automating and improving a variety of business processes. These encouraging numbers, however, do not indicate that adoption is easy; on the contrary, as with any emerging technology, there are numerous obstacles to success. Read the 451 Business Impact Brief today to learn what obstacles to look out for and how best to overcome them.

We have data-driven decision support systems implemented in Management Information Systems(MIS). Algorithms created by humans coded into MIS chew raw data and spit out decisions. The MIS systems were developed by human with substantial effort in software development. Once created, they allowed very little flexibility in deriving insights from new data sets. Now we have Machine Learning (ML) systems capable of making data-driven decisions or predictions without the need for explicit programming.

Our friends over a H2O.ai have sponsored a new Business Impact Brief from 451 Research – "Overcoming Obstacles to Machine Learning Adoption." The brief highlights the organizational barriers to machine learning adoption from 451 Research's Voice of the Enterprise: AI and Machine Learning 2H 2018 survey, asking the question: "What are your organization's most significant barriers to using machine learning?" After many fits and starts, the era of enterprise machine learning has finally arrived. According to the 451 Research survey, 20% of enterprises have already deployed the technology and a further 33% plan to do so within one year. These figures should come as no surprise: AI has the potential to benefit almost any company by automating and improving a variety of business processes.

What methodologies (such as Agile) do they use to develop ML? Do they build their ML models using internal teams, external consultants, or cloud APIs? How long have they deployed ML in production? How do they evaluate success with machine learning? If you're curious (we were), check out our free ebook, The State of Machine Learning Adoption in the Enterprise. GET THE FREE EBOOK Ben Lorica Chief Data Scientist P.S.

While the use of machine learning (ML) in production started near the turn of the century, it's taken roughly 20 years for the practice to become mainstream throughout industry. With this report, you'll learn how more than 11,000 data specialists responded to a recent O'Reilly survey about their organization's approach--or intended approach--to machine learning. Data scientists, machine learning engineers, and deep learning engineers throughout the world answered detailed questions about their organization's level of ML adoption. About half of the respondents work for enterprises in the early stages of exploring ML, while the rest have moderate or extensive experience deploying ML models to production.

Despite the hype surrounding artificial intelligence, we're still in the early stages of adopting machine learning in the enterprise, according to a new survey released today by O'Reilly Media. The survey also found that large-scale production deep learning rarely happens on the cloud, and that companies pursuing machine learning are actively embracing privacy, security, and fairness. Nearly half (49%) of the 11,400 people who took O'Reilly's survey this June indicated they were in the exploration phase of machine learning and have not deployed any machine learning models into production. That compares to 36% of who said they were an early adopter (models in production from two to five years), while 15% considered themselves sophisticated users (models in production for more than five years). "A lot of people are very interested in machine learning, but a lot of them are in the getting-started phase in terms of actually putting these things into productions in products and services," O'Reilly's Chief Data Scientist Ben Lorica tells Datanami.