Fraud

lookout

Device management blind to 125 percent increase in financial sector phishing attacks

As guardians of valuable monetary assets and highly sensitive data, financial institutions are the perfect target for cybercriminals. According to IBM, the financial services sector was the number one target of cyberattacks in 2020 among all industries. This means these organizations continue to be challenged and invest heavily in both people and technology to make sure they can withstand attacks of any type.

unravel

Mastercard Reduces MTTR and Improves Query Processing with Unravel Data

Mastercard is one of the world’s top payment processing platforms, with more than 700 million cards in use worldwide. In the US, nearly 40% of American adults hold a Mastercard-branded card. And the company is going from strength to strength; despite a dip in valuation of more than a third when the pandemic hit, the company has doubled in value three times in the last nine years, recently reaching a market capitalization of more than $350B dollars.

datadog

Detect application abuse and fraud with Datadog

Protecting your applications from abuse of functionality requires understanding which application features and workflows may be misused as well as the ability to quickly identify potential threats to your services. This visibility is particularly critical in cases where an adversary finds and exploits a vulnerability—such as inadequate authentication controls—to commit fraud.

Bits of Security, PedidosYa: Fraud Detection using Datadog and Sherlock

From day one, most organizations,especially the big ones, are targeted with a broad range of attacks. These range from information exfiltration attempts to fraud. Although a great majority of them can be addressed with the help of a Web Application Firewall, there are some that require more extensive tooling. Join me as I show you how we use Sherlock and Datadog to block 30,000+ fraudulent users per week in seconds. We will also discuss other applications and how you can implement similar solutions.
logz.io

Case Study - Online Skimming Attack Facilitated by Work-From-Home Arrangements

In May 2020, Kroll was contacted by a purveyor of high-end meats after receiving several customer complaints of potentially fraudulent credit card activity. The fraud allegations were raised after several customers observed unauthorized transactions on their credit cards shortly after placing orders through the purveyor’s e-commerce website. Kroll quickly assigned one of their seasoned Payment Card Industry (PCI) forensics investigators to review and investigate the matter.

logz.io

Case Study - Electronic Gift Card Fraud Investigation Uncovers Contractual Risks

Having closed brick-and-mortar operations on March 16, 2020 for safety reasons, the nearly overnight shift to a purely e-commerce revenue model brought uncertainty. However, a rapid uptick in online sales provided a sense of relief, albeit short-lived. Our client became concerned when a closer look at the online transactions revealed an unusually large volume of electronic gift card purchases made using their private label credit card.

splunk

Creating a Fraud Risk Scoring Model Leveraging Data Pipelines and Machine Learning with Splunk

According to the Association of Certified Fraud Examiners, the money lost by businesses to fraudsters amounts to over $3.5 trillion each year. The ACFE's 2016 Report to the Nations on Occupational Fraud and Abuse states that proactive data monitoring and analysis is among the most effective anti-fraud controls.

keboola

How can DataOps improve your financial institution's fraud program and mitigate risks?

Fraud comes in different forms, from client-facing credit card fraud to internal fraudsters twisting the loan portfolio. Banks (and other financial institutions) need to stay vigilant and act fast to prevent the loss of both money and reputation that follows each fraudulent incident. Fraud is expensive, but fraud prevention, detection and remediation can also be costly.