Deep learning is a subset of artificial intelligence, in particular, the field of machine learning. Deep learning uses a multi-layered artificial neural network to carry out a range of tasks, from fraud detection to speech recognition or language translation. Deep learning differs from traditional machine learning systems in that it is capable of self-learning and improving as it analyses large data sets. A highly flexible system it has a number of applications in business. In this article, we explain exactly what deep learning is and explore the ways that it is already transforming businesses. Deep learning is a function of artificial intelligence. It is designed to replicate the way that the human brain processes data. It also re-creates the patterns found in the brain's decision-making process. Sometimes called deep neural networking or neural learning, it is part of the wider field of machine learning. It is powered by networks that can carry out unsupervised learning. This process uses algorithms to analyse raw data, extracting information and presenting it in a structured, useful model. Often it is also used to process unstructured or unlabeled data.

Machine learning (ML) represents one of the keys to thriving in the modern age of big data. Consider "just how big" big data is for most organizations. Even small and medium-sized businesses (SMBs) deal with multiple data sources such as CRM tools, social media feeds, and transaction platforms. That's not even including internal data from HR platforms, submission forms, emails and media, and cloud-based tools that transmit metrics. All of that data means that unprecedented levels of insight are possible, but only if an organization can get a handle on the massive amounts of relevant data it has access to.

The market for anti-money laundering (AML) compliance jobs may be on the rise in Nordic countries, but don't count on that trend continuing. Advances in technology could render many compliance roles at Nordea Bank and Danske Bank obsolete, Bloomberg reported Monday. While Helsinki-based Nordea Bank relies on hundreds of employees to help scrutinize billions of transactions for signs of criminal activity, the system is costly and inefficient, and one the lender hopes to move away from, Mikael Bjertrup, head of the bank's financial crime prevention unit, told the news outlet. The bank, which currently uses machine-learning algorithms to close approximately 20 percent of its suspicious transaction alerts, is seeking to increase that total to 80 percent--a shift that would scale back the number of compliance officers needed by the institution, according to the report. "We'll be fewer people in the future, but our defense will be better," Bjertrup told Bloomberg.

The market for anti-money laundering (AML) compliance jobs may be on the rise in Nordic countries, but don't count on that trend continuing. Advances in technology could render many compliance roles at Nordea Bank and Danske Bank obsolete, Bloomberg reported Monday. While Helsinki-based Nordea Bank relies on hundreds of employees to help scrutinize billions of transactions for signs of criminal activity, the system is costly and inefficient, and one the lender hopes to move away from, Mikael Bjertrup, head of the bank's financial crime prevention unit, told the news outlet. The bank, which currently uses machine-learning algorithms to close approximately 20 percent of its suspicious transaction alerts, is seeking to increase that total to 80 percent--a shift that would scale back the number of compliance officers needed by the institution, according to the report. "We'll be fewer people in the future, but our defense will be better," Bjertrup told Bloomberg.

Teradata has announced today that Danske Bank, a financial services leader in the Nordics, has worked with Think Big Analytics, a Teradata company, to create and launch a state-of-art, AI-driven fraud detection platform that is already expected to meet 100 percent ROI in its first year of production. The engine uses machine leaning to analyze tens of thousands of latent features, scoring millions of online banking transactions in real-time to provide actionable insight regarding true, and false, fraudulent activity. By significantly reducing the cost of investigating false-positives, Danske Bank increases its overall efficiency and is now poised for substantial savings. "Application fraud is a critical, top of the agenda issue for banks, and there is evidence that criminals are becoming savvier by the day; employing sophisticated machine learning techniques to attack, so it's critical to use advanced techniques, such as machine learning to catch them," said Nadeem Gulzar, Head of Advanced Analytics, Danske Bank. "The bank understands that fraud is set to get worse in the near and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications. We recognize the need to use cutting-edge techniques to engage fraudsters not where they are today, but where they will be tomorrow. Using AI, we've already reduced false positives by 50 percent and as such have been able to reallocate half the fraud detection unit to higher value responsibilities."

How does a brick-and-mortar company of age create disruptive innovations? Danske Bank, the largest bank in Denmark, showed the way in 2013, by introducing MobilePay, a new and convenient way to transfer money. Today, the MobilePay is the Nordic's leading money transfer app with 3.8 million users in its core market, Denmark. Now, Danske Bank is applying the same approach as with MobilePay - but instead of payments, it focuses on customer behavior and analytics. The bank has created an in-hose startup, Advanced Analytics, whose sole purpose is to shake things up with bleeding edge AI and Machine Learning technology.

Data quality and critical thinking are imperative for the advance of artificial intelligence (AI), according to expert views from an event hosted by US-based Teradata. At day three (24 October) of the Teradata Partners Conference 2017 (22-26 October), in Anaheim, California, Banking Technology was there to gauge the zeitgeist in our data-drenched world. As a quick review, on Monday (23 October), the company set out its plans in a super-slick presentation; unveiled Teradata Analytics Platform; revealed Danske Bank has launched its AI driven fraud detection platform; and showed off Agile Analytics Factory. The Era of Human-Machine Collaboration", Stephen Brobst and Yasmeen Ahmad from Teradata; Nadeem Gulzar, Danske Bank; and Andrew Stephen, Said Business School – University of Oxford; discussed a lot more than just coding. Stephen says data quality and critical thinking "should be a given but it's not happening".

Demand for Artificial Intelligence (AI) expertise and technologies is being driven by compelling business value from across industries. Teradata (NYSE: TDC), the leading data and analytics company, is helping clients capitalize on the power of AI to deliver high value business outcomes in the areas of fraud detection, manufacturing performance optimization, risk modelling, and precision recommendation engines. To help clients accelerate their AI initiatives, Teradata leads with data science acumen and deep learning algorithms that significantly outperform most rules-based and machine learning approaches. For example: Danske Bank worked with Teradata to create and launch a state of-the-art, AI-driven fraud detection platform expected to meet 100 percent ROI in its first year of production. The engine uses deep learning to analyze tens of thousands of latent features, scoring millions of online banking transactions in real-time to provide actionable insight regarding both true and false fraudulent activity.

The AI has "already reduced false positives by 50%" Copenhagen-based Danske Bank has gone public on its deal with Think Big Analytics, the consulting arm of Teradata, to create and launch an artificial intelligence (AI) driven fraud detection platform. The announcement is all timed sweetly to be part of the Teradata Partners Conference 2017 (22-26 October), attended by Banking Technology and held in Anaheim, California. Teradata also revealed its new data analytics platform at the show – and unveiled two other initiatives – Analytics-as-a-Service and a new software portfolio. In terms of the engine, this uses machine leaning to analyse latent features, scoring online banking transactions in real-time to provide insight regarding true, and false, fraudulent activity. Nadeem Gulzar, head of advanced analytics, Danske Bank, says by using AI it has "already reduced false positives by 50% and as such have been able to reallocate half the fraud detection unit to higher value responsibilities".

TERADATA PARTNERS CONFERENCE -- Teradata (NYSE: TDC) announced today that Danske Bank, a financial services leader in the Nordics, has worked with Think Big Analytics, a Teradata company, to create and launch a state-of-art, AI-driven fraud detection platform that is already expected to meet 100 percent ROI in its first year of production. The engine uses machine leaning to analyze tens of thousands of latent features, scoring millions of online banking transactions in real-time to provide actionable insight regarding true, and false, fraudulent activity. By significantly reducing the cost of investigating false-positives, Danske Bank increases its overall efficiency and is now poised for substantial savings. "Application fraud is a critical, top of the agenda issue for banks, and there is evidence that criminals are becoming savvier by the day; employing sophisticated machine learning techniques to attack, so it's critical to use advanced techniques, such as machine learning to catch them," said Nadeem Gulzar, Head of Advanced Analytics, Danske Bank. "The bank understands that fraud is set to get worse in the near and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications. We recognize the need to use cutting-edge techniques to engage fraudsters not where they are today, but where they will be tomorrow. Using AI, we've already reduced false positives by 50 percent and as such have been able to reallocate half the fraud detection unit to higher value responsibilities."