Banking, financial institutions & customers have been facing fraud for a very long time, in fact ever since the financial industry was created. The chances of fraud being attempted are almost guaranteed wherever money and/or private data are present. As the use of digitization and use of technology increases, it also increases the ways and means for fraudsters to leverage the same technology to commit fraud. Fraud detection identifies an actual or expected fraud that has or may take place using advanced technologies like AI, OCR, and ML to identify potential threats, mitigate risks, and prevent their recurrence. Banks, financial institutions & any other organization that deals with money, finance, or any other financial instrument need to implement strict measures, systems, and processes in place to detect fraud at an early stage or if possible before it takes place.

A fraud detection product should be able to look at the traffic from multiple angles to cover as much of the attack surface as possible. For example, simple tricks like looking at the request velocity coming from clients works with volumetric and simple attacks but attackers have learned to circumvent this by load-balancing their traffic through proxy services long ago. A ruleset can help detect signals typically related to fraudulent activity, but the more advanced fraudsters over the years have refined their strategies, making such a ruleset less effective, especially since it may not be updated fast enough. The detection layer must consider multiple signals and have algorithms to automatically recognize anomalies and score the traffic accordingly.

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.

Join executive leaders at the Data, Analytics, & Intelligent Automation Summit, presented by Accenture. The thing about fraud is that it's constantly changing -- looking at a past attack doesn't guarantee the next attack will look the same or target the same kind of victim -- and defenders have to continuously adapt. Visa utilizes artificial intelligence to analyze all of the transactions that go across the network and track large-scale transactional changes as part of its fraud detection efforts, Melissa McSherry, Visa's senior VP and global head of data, security, and identity products, said at VentureBeat's Transform 2021 virtual conference on Monday. Visa scores all of the transactions that go across the Visa network, which allows the company to define a set of behaviors that would be considered "normal." The team is "constantly" updating the model's view of history and updates the model itself to reflect the data on a fairly regular basis, McSherry said.

With the US economy just starting to recover from Covid-19 and millions still out of work, Congress authorized expanded unemployment benefits that supplement state assistance programs. While it's laudable to fortify struggling Americans during an ongoing crisis, bad actors have made unemployment fraud a serious problem. Unfortunately, the many states seeking to stop fraud through surveillance are installing biased systems that may do far more harm than good. Twenty-one states have turned to high-tech biometric ID verification services that use computer vision to determine if people are who they claim to be. This is the same technology that allows users to unlock their phone with their face--a one-to-one matching process where software infers if your facial features match the ones stored on a single template.

Machine learning technology is poised to be huge thing in financial services. In fact, two-thirds of UK-based firms are already using it. That is according to two of the UK's top financial regulators. The Financial Conduct Authority (FCA) and the Bank of England have taken a deep dive into how the financial services industry in the country is using machine learning. The research is based on a survey sent out to 300 firms, including banks, credit brokers, e-money institutions, financial market infrastructure firms, investment managers, insurers, non-bank lenders and principal trading firms.

Many FIs and merchants that have fallen victim to fraud traditionally respond by assessing the damage, pinpointing how the attack succeeded and implementing new measures to prevent similar schemes from happening again. Some businesses are looking for solutions that will help them stop fraud from happening in the first place as criminals become increasingly creative and aggressive in their efforts to steal data and funds. The push for more intelligent anti-fraud solutions comes as the costs of such attacks are reaching new heights. Fraud losses hit $14.7 billion last year, according to the latest DataVisor Fraud Index Report. Account takeover (ATO) fraud proved to be particularly effective, causing $4 billion in losses.

All shoppers are familiar with the offer of a discount at checkout in exchange for taking out a store credit card. Their wallets may already be stuffed with plastic bearing the names of retailers including Amazon.com, Gap, JCPenney, and Lowe's, but there's often one company behind all of them: Synchrony Financial in Stamford, Connecticut, which is one of the world's largest issuers of store credit cards. This summer marks five years since it spun off from its parent company -- General Electric -- and ever since it's been building a digital strategy that uses artificial intelligence and machine learning. Synchrony now deploys AI to speed up everything including credit approvals and fraud detection. Chief Information Officer Carol Juel has led the undertaking.

Visa rolls out a new suite of tools to fight fraud. The firm announced that the slate of offerings is meant to "help prevent and disrupt payment fraud" and is available to its clients without an additional fee or sign-up. Visa's solutions leverage AI to prevent and quickly put a stop to fraudulent transactions, which could help it keep pace with the AI-driven fraud tools introduced by firms like Mastercard, TSYS, and First Data. Offering fraud-focused tools is particularly important as fraud rises, especially as e-commerce grows more popular, so doing so could make Visa more attractive to firms. Global payment fraud losses are expected to rise 8.5% annually to reach $31 billion in 2020, costing merchants 7% of their annual revenue, according to data from First Data sent to Business Insider Intelligence.

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.