In the U.K. last year, authorised push payments (APP) fraud reached £583 million ($706 million), a 39% increase from 2020.
October 4th, 2022
This holiday season, you jolly well better be prepared for more fraud.
October 3rd, 2022
Anti-money laundering (AML) can be described as finding a needle in a haystack. But this analogy leaves out the considerable steps criminals go through to camouflage their activities. In corporate banking, finding the criminal among legitimate customers, like a needle in the haystack, is more difficult—because they have more tools to disguise their illegitimate activities.
September 12th, 2022
What comes to mind when you hear “innovation” mentioned? People tend to think of technological innovations involving digitalization, software, hardware, APIs, artificial intelligence, and machine learning. However, innovation existed long before the advent of the software industry.
August 24th, 2022
An emerging method of committing financial fraud is stealing checks, and the circumstances surrounding these scams are often bizarre and highly public. Checks are appropriated from the familiar blue United States Postal Service (USPS) collection boxes and rewritten, repurposed, and cashed by thieves. One victim had their check stolen twice from the same collection box, even after taking measures to get a new bank account and checks.
August 1st, 2022
Check out the previous blog ”What ISO 20022’s extra data means in the fight against fraud” to learn more about fraud challenges and opportunities.
July 27th, 2022
Social engineering scams are flourishing, putting a spotlight on the way unauthorized and authorized electronic transactions are interpreted under Regulation E (Reg. E). Banks and financial institutions (FIs) in the U.S. face increasing scrutiny, particularly with their responsibility toward customers in the event of a fraud report for a digital transaction and the associated liability when there’s a financial loss.
July 25th, 2022
When it comes to fighting financial crime, challenges exist that go beyond the scope of merely stopping fraudsters or other bad actors. Some of the newest, advanced technologies that are being launched often have their own specific issues that must be considered during adoption stages. Addressing one of these concerns, when AI-based systems are used to gain operational efficiencies in financial crime, model fairness and data bias may occur when a system is skewed for or against certain groups or categories in the data. Typically, this stems from erroneous or unrepresentative data being fed into a machine learning model. Biased AI-systems may particularly represent a serious threat when reputations may be affected. In fraud detection, as one example, biased data and predictive models could erroneously associate last names from other cultures with fraudulent accounts, or falsely decrease risk within population segments for certain type of financial activities.