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Banking on AI: A Fast-Evolving Strategy for Indian Banks


At a time like this, the banking sector is trying its hand, leg and even head to give a head-start to the AI developments. The financial services industry is appealing to enter AI market to avail the luxury of accurate data and investment. The development assists banks with better customer service, fraud detection, reduction of managing cost and easy decision-making through AI analysis. Customers have expectations that can't be turned down. Expectations to get work done faster and with zero error. The only by-standing solution is the utilisation of AI in the everyday banking sector.

Top 9 data science use cases in banking - Fintech News


Using data science in the banking industry is more than a trend, it has become a necessity to keep up with the competition. Banks have to realize that big data technologies can help them focus their resources efficiently, make smarter decisions, and improve performance. Here is a list of data science use cases in banking area which we have combined to give you an idea how can you work with your significant amounts of data and how to use it effectively. Machine learning is crucial for effective detection and prevention of fraud involving credit cards, accounting, insurance, and more. Proactive fraud detection in banking is essential for providing security to customers and employees.

Deep Labs Partners with NICE Actimize's X-Sight Marketplace Ecosystem


Helping financial services organizations keep up with a changing market landscape, NICE Actimize, a NICE business (Nasdaq: NICE) and leader in Autonomous Financial Crime Management, today announced that three new technology partners have joined the fast-growing X-Sight Marketplace, the industry's first financial crime risk management-focused ecosystem. X-Sight Marketplace helps financial services organizations evaluate new point solutions and move to stay on top of a challenging regulatory and criminal environment. The recent additions include Boku Identity, a leading end-to-end identity solution provider; Deep Labs, specialists in applying persona-based artificial intelligence to assess risk and reduce fraud and identity theft; and TeleMessage, which provides state-of-the-art cloud and on-premises messaging solutions. Deep Labs offers a suite of products that leverages persona-based dynamic adaptive risk and propensity profiles to solve a range of use cases including account takeover, anti-money laundering, false declines to marketing decisioning, identity, and friendly fraud. Deep Labs' DeepIdentity approach provides advanced identity verification solutions which, through risk-based decisioning, minimizes friction and enhances the consumer experience across numerous use cases, including identity verification, digital onboarding, authentication, and continually knowing the customer's persona.

Next-gen machine learning: Webinar - Electronic Products & Technology


Join Xilinx and TigerGraph to learn about the next-generation machine learning solutions on connected data. We will explore different practical use cases that rely on product or service recommendation and fraud detection solutions ranging from patient similarity in the health sector to anti-money laundering in the financial services industry. We will hear directly from the area and product experts so be sure to sign up now.

Fraud prediction; a challenge for machine learning algorithms


Fraud is a billion-dollar business and expands rapidly year by year. Thousands of people fall victim to it. Fraud always includes a false statement, misinterpretation, or deceitful conduct. Common varieties of fraud offenses include identity theft, insurance fraud, credit/debit card fraud, and mail fraud. The PwC global economic crime survey of 2018 (PwC, 2018) found that about half of the 7,200 surveyed enterprises had already experienced fraud of some kind. This is an increase compared to the PwC survey conducted in 2016 (PwC, 2016), in which slightly more than a third of organizations surveyed had experienced economic crime.

Amazon Fraud Detector now generally available


Amazon Web Services (AWS) has announced the general availability of its machine learning-based fraud detection service. Amazon Fraud Detector is a fully managed service touted as making it easy to quickly identify potentially fraudulent online activities, such as online payment and identity fraud, the creation of fake accounts, and loyalty account and promotion code abuse "in milliseconds". To use the service, customers can select a pre-built machine learning model template; upload historical event data of both fraudulent and legitimate transactions to build, train, and deploy machine learning models; and create decision logic to assign outcomes to the predictions. "Customers of all sizes and across all industries have told us they spend a lot of time and effort trying to decrease the amount of fraud occurring on their websites and applications," Amazon machine learning VP Swami Sivasubramanian said. "By leveraging 20 years of experience detecting fraud coupled with powerful machine learning technology, we're excited to bring customers Amazon Fraud Detector so they can automatically detect potential fraud, save time and money, and improve customer experiences -- with no machine learning experience required."

ComplyAdvantage nabs $50M for an AI platform and database to detect and stop financial crime – TechCrunch


The growth of digital banking has opened up a wealth of opportunities for making the world of finance more accessible and transparent to a greater number of people. But the darker underbelly is that it has also created more avenues for illicit activity to flourish, with some $2 trillion laundered annually but only 1-3% of that sum "caught". To help combat that, a London-based startup called ComplyAdvantage, which has built an AI platform and wider database of some 10 million entities to help identify and track those involved in financial crime, is today announcing a growth round of funding of $50 million expand its reach and operations. Specifically, the plan will be to use the funding for hiring, to invest in the tools it uses to detect entities and map the relationships between them, and to bring on more clients. "We've been focused on more granular analysis and being able to scale to hundreds of millions of searches across our database," said Charles Delingpole, founder and CEO, said in an interview.

Here's why machine learning is critical to success for banks of the future


MACHINE learning is a popular buzzword today, and has been heralded as one of the greatest innovations conceived by man. A branch of artificial intelligence (AI), machine learning is increasingly embedded in daily life, such as automatic email reply predictions, virtual assistants, and chatbots. The technology is also expected to revolutionize the world of finance. While it is slower than other industries in embracing the technology, the impact of ML is already visibly significant. Most recently, HSBC said that the bank was using the technology to combat financial crime.

Fraud detection startup Ravelin secures $20M Series C – TechCrunch


Ravelin, the London-based company using machine learning to help companies fight fraud when accepting online payments, has raised $20 million in new funding. The Series C round is led by Draper Esprit, with participation from existing investors Amadeus Capital Partners, BlackFin Tech, and Passion Capital. Ravelin disclosed $10 million in Series B funding in September 2018. Launched in 2016, Ravelin utilises machine learning and graph network technologies to help online businesses reduce losses to fraud and improve acceptance rates of orders. The idea is to do away with cruder, rule-based systems and use machine learning to negate false positives and give merchants more confidence accepting customers/transactions. With regards to product-market fit, Ravelin says it first found success with large-scale food and cab-ride marketplaces, but has since expanded into travel, ticketing, entertainment, gaming, gambling, and retail.

Modelling Credit Card Fraud Detection


Credit card frauds are a "still growing" problem in the world. Losses in frauds were estimated in more than US$27 billion in 2018 and are still projected to grow significantly for the next years as this article shows. With more and more people using credit cards in their daily routine, also increased the interest of criminals in opportunities to make money from that. The development of new technologies puts both criminals and credit card companies in a constant race to improve their systems and techniques. With that amount of money at stake, Machine Learning is surely not a new word for credit card companies, which have been investing on that long before it was a trend, to create and optimize models of risk and fraud management.