Fraud


Neo4j: How a lack of context awareness is hampering AI development

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What do we mean when we say'context'? In essence, context is the information that frames something to give it meaning. Taken on its own, a shout could be anything from an expression of joy to warning. In the context of a structured piece of on-stage Grime, it's what made Stormzy's appearance at Glastonbury the triumph it was. The problem is that context doesn't come free – it has to be discovered.


Companies starting to use AI technology to fight fraud

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Companies are beginning to employ advanced technologies such as artificial intelligence and machine learning to uncover fraud, according to a new report. The report, from the Association of Certified Fraud Examiners and analytical technology provider SAS, found that 13 percent of organizations currently use AI or machine learning to help fight fraud. Meanwhile, 25 percent of organizations expect to adopt that technology in the next year or two. The use of AI and machine learning as part of anti-fraud programs is expected to almost over the next two years. The report also found that 26 percent of organizations currently use biometrics as part of their anti-fraud programs, and another 16 percent expect to deploy biometrics as part of their programs over the next two years.


AI in Finance Summit & AI in Insurance Summit, New York: Why Attend?

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The AI in Finance Summit is returning to New York due to popular demand, this time accompanied by the AI in Insurance Summit. Across 2 days, 400 attendees will come together to learn from over 60 speakers about the most cutting edge advancements in the application of AI in the financial and insurance industries. Topics covered will include investment, fintech, financial compliance, financial forecasting, fraud detection, responsibility, deep learning and more. As the discussions around regulation, cybersecurity and ethics increase, these topics will take centre stage across both tracks at the summit. Sessions will focus on the explainability of algorithms used within the financial industry, and there will be presentations for business leaders and decision makers specifically as well to compliment the technical sessions.


Artificial intelligence in Banking advantages, disadvantages & Mobile banking services Science online

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AI can be used in banks to decrease financial risk, It can improve loan underwriting through machine learning, improve financial crime risk with advanced fraud detection, It can improve compliance and controls, and reduce operational risk through improved accuracy in transcription & production of documents, banks can use machine learning and big data to prevent criminal activities and monitor potential threats to customers in commerce. Artificial intelligence (AI) includes machine learning and natural language, it can be used in the banking industry, Machine learning is a method of data analysis which automates analytical model building, Machine learning occurs when computers change their parameters/algorithms on exposure to new data without humans having to reprogram them. Natural language processing (NLP) refers to the ability of technology to use human communication, naturally spoken or written, as an input that prompts computer activity, natural language generation (NLG) refers to the ability for technology to produce human quality prose, It sorts through large amounts of available data to produce a human-sounding response, NLG can take the form of speech, or of a multipage report summarizing financial results. AI can help the bank understand the expenditure pattern of the customer, The bank can come up with a customized investment plan & assist the customers for budgeting, banks can send the notification about the advice for keeping a check on the expenses and investments based on the data, The transactional & other data sources can be tracked to help understand the customer's behavior and preferences to improve their experience. Artificial intelligent can sift through massive amounts of data and identify patterns that might elude human observers, One area where this capacity is particularly relevant is in fraud prevention, Artificial intelligence and machine learning solutions are deployed by many financial service providers to detect fraud in real time.


Big Data in Banking – AI and Data Management Use-Cases Emerj

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Ayn began her career in journalism and went on to work in corporate communications at Accenture for seven years before joining the content and research team at Emerj. Banks are in one of the best positions for leveraging AI in the coming years because the largest banks have massive volumes of historical data on customers and transactions that can be fed into machine learning algorithms. We recently completed our Emerj AI in Banking Vendor Scorecard and Capability Map in which we explored which AI capabilities banks were taking advantage of the most and which they might be able to leverage in the future. When it comes to big data in banking, banks might be primed to think about using their customer data to build a conversational interface or chatbot to improve the customer experience and, perhaps most importantly, attract millennial customers who are used to getting their needs met quickly over the internet. Despite this, banks are unlikely to leverage their customer data nowadays.


Artificial Intelligence Makes Boosting ID Theft Protection Critical, House AI Task Force Chair Warns

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AI's growing popularity with crooks makes boosting ID theft defenses more critical says House AI Task Force Chair Bill Foster Artificial intelligence is making improving identity theft protections imperative, House Financial Services Committee AI Task Force Chair Bill Foster warned Wednesday. The Congressman said AI has become an increasingly popular tool for crooks to swipe assets and sensitive financial information from consumers. AI is being used to help steal Social Security numbers, credit card numbers and other personal identity factors can be stolen and sold on the dark web, or used by criminals for quick and easy profit gain, Foster explained. Experts estimate nearly 15 million Americans were victims of ID theft last year costing them billions. At the same time crooks are profiting from AI, nation's financial regulators and law enforcement agencies are availing themselves of the technology to detect fraud and money laundering and to improve market surveillance, Foster said.


AI Will Automate Dispute Resolution

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All financial industry analysts agree that the number of transactions paid using credit and debit cards will continue to grow annually. Moreover, this trend is bound to continue given the planned introduction of the new Apple Card, which is expected to become an instant hit with Millennials, especially those who currently do not have a credit card. Banks that process credit and debit card transactions, therefore, constantly must be on the lookout for new technologies that can handle all this new data traffic quickly and dependably. By every indication, they've found it. AI is quickly becoming central to emerging bank technologies.


Using Artificial Intelligence, Visa Is Combatting Fraud at Nearly the Speed of Light

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By using artificial intelligence (AI), Visa Inc. helped issuers prevent an estimated $25 billion in annual fraud, the company announced on June 17. The company accomplished this using Visa Advanced Authorization (VAA), a comprehensive risk management tool that monitors transaction authorization on the Visa global network, VisaNet, in real time. VAA evaluates every single transaction on VisaNet and helps issuers swiftly identify emerging fraud trends and patterns, allowing the issuers to respond promptly to instances of fraud, while approving legitimate transactions. "One of the toughest challenges in payments is separating good transactions made by cardholders from bad ones attempted by fraudsters without adding friction to the process," said Melissa McSherry, senior vice president and global head of Data Products and Solutions at Visa. The speed with which Visa can evaluate a transaction is crucial.


How it works: Visa's artificial intelligence (A.I.) for payment authorization and fraud detection

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Merchants want to make a sale. Consumers want a fast checkout experience without false declines. Financial institutions want to mitigate fraud. The A.I. technology powering Visa Advanced Authorization can help.


PayPal Feeds the DL Beast with Huge Vault of Fraud Data

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PayPal is no stranger to fraud. As one of the Internet's first online payment services, PayPal has been exposed to every type of wire fraud imaginable (and some beyond imagination). Sometimes the fraudsters had the upper hand, but now, thanks to deep learning (DL) models running on high performance computing (HPC) infrastructure, PayPal is leveraging its vast repository of fraud data to keep the fraudsters on the run. PayPal is one of the classic success stories of the Internet era. Founded during the dot-com heyday of 1998, the company carved out a lucrative niche – facilitating secure payments online -- early in the Internet's development.