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How Financial Institutions Use Machine Learning to Prevent Fraud


Machine learning algorithms can reveal fraud patterns much faster and more accurately than humans or traditional rule-based systems. Read this article to understand how exactly banks can benefit from ML-powered solutions in fraud detection. Each year, banking and financial institutions from all over the world lose many billions of dollars because of fraud. Machine learning seems to be the most efficient technology for detecting and preventing fraud in this rapidly evolving sphere. From this article, you'll understand how exactly banking and financial institutions can benefit from integrating ML algorithms. Plus, you'll learn about the shortcomings of traditional fraud detection techniques.

Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook Artificial Intelligence

The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.

A Time-Frequency based Suspicious Activity Detection for Anti-Money Laundering Artificial Intelligence

Money laundering is the crucial mechanism utilized by criminals to inject proceeds of crime to the financial system. The primary responsibility of the detection of suspicious activity related to money laundering is with the financial institutions. Most of the current systems in these institutions are rule-based and ineffective. The available data science-based anti-money laundering (AML) models in order to replace the existing rule-based systems work on customer relationship management (CRM) features and time characteristics of transaction behaviour. However, there is still a challenge on accuracy and problems around feature engineering due to thousands of possible features. Aiming to improve the detection performance of suspicious transaction monitoring systems for AML systems, in this article, we introduce a novel feature set based on time-frequency analysis, that makes use of 2-D representations of financial transactions. Random forest is utilized as a machine learning method, and simulated annealing is adopted for hyperparameter tuning. The designed algorithm is tested on real banking data, proving the efficacy of the results in practically relevant environments. It is shown that the time-frequency characteristics of suspicious and non-suspicious entities differentiate significantly, which would substantially improve the precision of data science-based transaction monitoring systems looking at only time-series transaction and CRM features.

AI and advanced analytics in AML: From rule-based controls to intelligence-led capabilities


AI is a broad term covering multiple fields. For AML professionals, perhaps the most relevant subfield of AI is machine learning, which refers to the use of algorithms to continually improve a task, without the need for human intervention. Machine learning algorithms search for patterns within a given data set. Repeated recognition of patterns allows an algorithm to make ever more swift and accurate predictions. According to a survey of 296 UK-based AML professionals conducted by The Economist Intelligence Unit, the areas where respondents believe AI and advanced analytics can best be applied to combat money laundering are suspicious activity reporting (45%) and transaction monitoring (43%).



By John Spooner, Head of Artificial Intelligence, EMEA, Artificial Intelligence (AI) has evolved significantly from being a mere technology buzzword, to the commercial reality it is today. The technology is making a positive impact across many industries, including the financial sector. The financial services industry has a reputation of constantly innovating and advancing new technologies, in the pursuit of strengthening the customer base, and finding new revenue opportunities. This is happening across all segments including capital markets, commercial banking, consumer finance and insurance. The use of AI in the financial services is rapidly changing the business landscape, even in traditionally conservative areas.

Uncovering Insurance Fraud Conspiracy with Network Learning Machine Learning

Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially fraudulent claims every day. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.

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.

How AI spots fraud quicker than people - Raconteur


Identity fraud, in which a slice of your identity ranging from new credit cards to entire bank accounts is taken over by criminals, rose by 49 per cent in 2015 on the previous year. That totalled almost 170,000 cases, according to data collected by Cifas, the financial industry's non-profit fraud advisory service. The reason for the rise is that more and more we use the internet for financial transactions, but have very few ways to verify our identity without cumbersome systems involving human interaction, which are also vulnerable to fraud. Cifas' 2015 Fraudscape report shows that 86 per cent of identity fraud happened online, with bank accounts and credit or debit cards most targeted, closely followed by loans and communications, typically mobile phone accounts. Businesses looking to tackle fraud are turning to artificial intelligence and deploying neural networks because the systems learn in a manner like the brain's own neurons to try to bust fraud Traditionally, companies dealing with such problems have acted after the fact, trying to unravel complex or opportunistic frauds by working back through audit trails.

Brighterion CEO: 2018, the Year of AI


Dr. Akli Adjaoute, CEO of Brighterion, wrote this AI-focused piece as part of our 2018 year-end eBook. On Dec. 3, 2018, the U.S. Treasury's FinCEN and Federal Banking agencies issued a joint statement encouraging innovative industry approaches to combating money laundering, terrorist financing and other illicit financial threats. As a result, anti-money laundering (AML) has been occupying the headlines as of late. The financial industry has paid $321 billion in fines just through the end of last year, as estimated by Boston Consulting Group. JPMorgan had to pay more than $2 billion in fines due to violation of the Bank Secrecy Act, tied in part to the infamous Bernie Madoff scheme.

Detecting Crime Through Artificial Intelligence Analytics Insight


Artificial Intelligence (AI) is proving its powers to prevent and detect everything gripping them from routine employee theft, frauds, insider trading and business risks. Many large corporations, business enterprises have been employing AI to detect and prevent money laundering and widespread frauds. Machine learning has been increasingly deployed by social media platforms to block illicit content such as child pornography and fake news. Businesses have been using AI for higher risk management and responsive fraud detection towards prevention and prediction of crimes. The earlier monitoring systems used by the industries need manual interference and are often not cent percent accurate.