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How Machine Learning Helps in Financial Fraud Detection?

#artificialintelligence

The financial services sector is undergoing digital transformation, and the driving force behind it is machine learning (ML). ML provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. As the finance sector operates on tons of personal data and billions of critical transactions every second, it becomes especially vulnerable to fraudulent activities. Scammers are always seeking to crack the servers to get valuable data for blackmailing. According to PwC's Global Economic Crime and Fraud Survey 2020, respondents reported losses of a whopping $42 billion over the past 24 months due to fraudulent activities.


Artificial Intelligence And The Future Of Financial Fraud Detection

#artificialintelligence

False positives occur regularly with traditional rule-based anti-fraud measures, where the system flags anything that falls outside a given set of parameters. For example, if you are planning a trip abroad and you start buying airline tickets and accommodation, this may trigger a fraud warning. A smarter system as described in the two previous paragraphs, that can better understand the underlying patterns of human behavior, could potentially use the new customer data (your travel purchases) to match you with a different cluster of users (for example, holiday travelers). It can then test your behavior against transactions typical to that of the new cluster of users, holiday travelers in this example, before automatically raising a fraud flag on your account.


How AI fights fraud in the telecom industry

#artificialintelligence

Over 59 million Americans said they lost money as a result of phone scams in approximately the past 12 months, with an average reported loss of $502, according to the Truecaller Insights US Spam & Scam Report. "Fraud is a major consideration in the telecom industry," said Dr. Gadi Solotorevsky, CTO at Amdocs cVidya, an AI solutions provider. "Today, close to 2% or over $1.5 trillion in yearly global revenue is lost annually due to fraudulent behavior. The total losses across the industry are staggering." Solotorevsky cited a 2019 survey from the Communications Fraud Control Association (CFCA) that found that two-thirds of respondents experienced an increase in fraudulent activities. "We mostly encounter payment and subscription fraud, identify theft/impersonation, account takeover, insider threats, and SIM swap," Solotorevsky said.


Book: A Guide to Data Science for Fraud Detection

@machinelearnbot

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention.


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AAAI Conferences

In this paper, we propose to use machine learning to automate Medicare fraud detection. By cross checking Medicare payment database and provider exclusion database, we build datasets with millions of service providers, including a handful of convicted fraudulent service providers. One essential challenge is that the dataset created is extremely imbalanced, making it extremely difficult to learn accurate classifiers for fraud detection. To tackle the challenge, we first use feature engineering to design effective features, by taking the difference between each service provider and its group cohort into consideration. At the instance level, we also use a synthetic instance generation approach to generate positive samples to alleviate the data imbalance challenge.