How Micro-Deposits Are Increasingly Harming Fintech Companies
Micro-deposits are small amounts of money (usually less than $1) sent from one financial institution to another to verify the account's ownership or other information. Once the payment is sent, the receiver is asked to verify the micro-deposit amount. This is a common practice when linking a deposit account to another account at a fintech company, like an investment firm or payment platform. What Is Micro-Deposit Fraud? Criminals use micro-deposit fraud to gain unauthorized access to brokerage accounts and withdraw funds. They do this by entering many random account numbers into a brokerage system in an attempt to link with a legitimate bank account. Once such a link is achieved, usually with an unwitting confirmation by the account holder, the fraudster can access money in the account. Another way fraudsters exploit micro-deposits is by creating accounts en masse and pocketing the micro-deposits before they can be reversed. Though each deposit is worth only a few cents, they eventually add up, with potentially thousands of dollars in accumulated losses for the firm. Impact of Micro-Deposit Fraud on Fintech Companies Since micro-deposits are most often made by legitimate fintech businesses, the victims of such fraud will often blame these organizations when it happens. This can damage the fintech's reputation and take a lot of time to fix. If left unchecked, fraudsters can easily steal large amounts of money through micro-deposits before the firm notices what's happening. It's due time that fintech businesses solidly secure their micro-deposits or change to a different way of verifying accounts. If they cannot evolve away from micro-deposits just yet, they stand to benefit a great deal by implementing an advanced AI platform like nSure.ai, which can detect and block such fraud in real-time.
Asked a month ago
Perceptual Hashing: How Fraudsters Attack Businesses
Perceptual hash functions are widely used in multimedia applications. Facial recognition is a typical example of perceptual hashing, where the same face in different images or videos will generate similar hashes. In digital forensics, perceptual hashing can help analysts isolate similar data. Such hash functions also assist in catching online copyright infringement even when the media has been modified. What Is Perceptual Hashing? Here's how it works. An algorithm generates perceptual hash functions as shorthand fingerprints of various types of multimedia, producing a similar fingerprint for content with similar features. Conventional hash algorithms are based on the avalanche effect, where one small change drastically alters the hash of a file. Perceptual hashing focuses on what humans perceive to be related, generating only slightly different hashes for comparable media. How Fraudsters Exploit Perceptual Hashing While many businesses use perceptual hashing to prevent fraud, criminals have found ways to use this technology for their own nefarious ends. Many websites display images or audio to confirm that a user is a human. Fraudsters have found ways to pass such challenges undetected by creating automated solvers—software that generates perceptual hashes for thousands of media files and then compares those hashes to challenges presented on a website. When deployed successfully, a challenge can be solved automatically without human input. Fraudsters have also found ways to exploit weaknesses in perceptual hashing algorithms by forging similar media like facial images or by changing corresponding media just enough to avoid detection. Can Perceptual Hashing Fraud Be Prevented? nSure.ai is an advanced AI platform that does away with aging security challenges that are easily bypassed with sophisticated attacks. Instead, nSure.ai uses advanced AI technology to identify and stop these fraudsters in their tracks. Their machine learning platform uses behavioral and contextual analysis for temporal linking between transactions, intercepting both single fraud events and scalable fraud attacks.
Asked a month ago
Can Bots Bypass CAPTCHA?
A CAPTCHA challenge is a test presented to a subject to verify that they are human. Invented in 2000, CAPTCHA has evolved and today features various test models, from computer-warped text to identifying objects in grainy images. While they have been successful against unsophisticated bots, CAPTCHAs are no longer effective in stopping them completely. New CAPTCHAs are released all the time because previous iterations were beaten. Advanced machine learning bots now have little trouble overcoming CAPTCHA challenges. CAPTCHA Fails at Security - And User Experience A CAPTCHA is a reverse Turing test, which means that a computer tests whether the subject is also a computer. Herein lies the issue: even though humans are setting up the tests, it's a computer presenting them in a digital format that another computer can theoretically understand. The only way to make it harder for bots to pass such tests is to increase the difficulty of the test, which makes it harder for humans to solve it too. Fundamentally, these tests add no useful security protections. Every time a human is faced with (and possibly fails) a CAPTCHA, it tarnishes the entire user experience. The average time a user spends on a website (known as dwell time) is around 50 seconds, and the average person takes around 10 seconds to solve a CAPTCHA—20% of the dwell time. Failed CAPTCHA tests cause frustration and burn even more precious dwell time seconds. Alternatives to Obsolete CAPTCHA Systems Adding more friction to the user experience is not the ideal way to increase security. Instead, opt for an advanced AI tool that automatically detects suspicious activity and preserves the user experience for legitimate users. nSure.ai is a leading platform that incorporates a host of machine learning checks and verifications to beat bots at their own game.
Asked a month ago
How to Stop Online Gambling Fraud in Its Tracks
The online gaming world is growing by the day and so is the fraud targeting it. According to Grand View Research, the global online gambling market was valued at $57.54 billion in 2021, certainly a ripe target for those with nefarious motives. Gambling platforms have an increased risk of fraud for the same reason digital goods fraud is so rampant: little to no intelligent protection, short time to ROI, and low risk of getting caught. How to Prevent Online Gambling Fraud Fraud managers must work constantly to prevent such fraud without repelling legitimate customers. Staying well within the chargeback "safe zone" to avoid being backlisted by payment processors is an ongoing challenge for any manager, especially when the goal is to make it as easy as possible to start playing. Many websites opt to add more friction: long-winded KYC vetting, geo-locking, deposit/withdrawal wait times, transaction limits, and so forth. These tactics make it harder for customers to interact with the website and often mistakenly flag transactions as fraud, which hurts conversions. Machine learning represents a real solution to this thorny issue, rooting out fraud attempts instantly at scale while allowing for smooth, uninterrupted gaming by honest players. By building on their experience handling millions of transactions and constant accumulation of behavioral data, AI-based platforms like nSure.ai can streamline this process. Deploying top-tier technology to handle transactional fraud analysis is a game-changer for online gaming—eliminating guesswork, false flags, and endless hours of manual review. Common Types of Online Gambling Fraud While fraud comes in many shapes and sizes, most online gambling fraud involves exploiting the gambling website's systems. Some common types of online gambling fraud include: Multiple account fraud or collusionCard-testing fraudChip dumpingStolen funds (credit cards and PayPal)Promotion/bonus abuseFalse chargebacks
Asked 2 months ago
Here's How Synthetic Identity Fraud Detection Is Possible
Identity fraud, similar to identity theft, occurs when someone unlawfully uses someone else's personal information to commit fraud or other crimes. Synthetic identity fraud involves the construction of elaborate false identities that use real personal information to appear legitimate. Though fraudsters aren't stealing an actual identity in this process, they are using specific pieces of real information which make it very hard for a business to discern such fraud. It's also much harder to notify victims of fraud if the fake identity is constructed from multiple real ones. Fraudsters use a synthetic ID to cloak their true identities while committing crimes like stealing digital goods or opening fraudulent accounts. Together with a cloaked IP address and other advanced tools, fraudsters can do real damage without the business realizing it has been hit by fraud until well after the fact. How to Detect Synthetic Identity Fraud Synthetic identity fraud is a complex and relatively new form of identity fraud, and it's very difficult for existing solutions to control. Often, a human analyst must tediously sort through mounds of information to figure out what's going on. Even then, synthetic IDs appear legitimate and the fraud may only be revealed by in-depth metadata analysis. The best option is to use a fraud protection platform like nSure.ai. nSure.ai's predictive AI fraud detection uses machine learning to catch fraudsters before they act, without affecting legitimate customers. Any merchants looking for guaranteed chargeback coverage and a 98% approval rate should look no further.
Asked 2 months ago