3 Benefits of Risk-Based Authentication for Crypto Exchanges
Risk-based authentication is a user verification method that uses pre-defined criteria to assess a user's risk factor and determine whether they are who they claim. This method is often used for fraud prevention in crypto and is usually integrated into a larger fraud prevention solution. Every time somebody logs on to an account, they are risk-assessed using their location, IP address, device information, time of submission, and potentially hundreds of other data points. If the risk is perceived to be low as for most users, they can continue using the platform without interruption. If the risk is perceived to be high, users will be routed through further steps of authentication to determine if they are fraudulent or not. So, what are the benefits of risk-based authentication? » Payment fraud protection in crypto isn't as hard as you think. Here's why 1. Protects Legitimate Users Legitimate customers should be the priority of any good business. Risk-based authentication within a wider AI model protects legitimate users while addressing any seemingly fraudulent activity. An account flagged due to suspicious activity is closely monitored and its transactions placed under special scrutiny by the AI fraud detection solution, allowing fraud to be blocked proactively. If no further fraud is detected, no action is taken, and legitimate users can continue trading with ease. » Is KYC still useful? Here are the pros and cons of trading without KYC 2. Convenient and Scalable Risk-based authentication is a convenient and scalable way to combat the increasingly bad state of digital goods and the fraud surrounding it. The most damaging fraud today is scalable and unexpected, and fraud prevention solutions need to be able to respond accordingly. Well-designed machine learning models will adapt as needed while conveniently handling most of the heavy lifting, allowing risk management teams to focus on improving the fraud prevention model or analyzing special cases. 3. Improves User Experience Payment fraud protection is imperative, but it should not come at the expense of good user experience (UX). Risk-based authentication, if done properly, should only challenge users when they trigger fraud limits. This means that the exchange doesn't need to use tedious friction layers such as unnecessary KYC, extra multi-factor authentication, and time-limit restrictions for new users. These reduced barriers to entry improves user experience above the competition. » Want to learn more about protecting your business from fraud? Here's the A to Z of payment fraud protection How to Implement Risk-Based Authentication Like we said before, risk-based authentication is best implemented within a wider fraud prevention solution, preferably one that can make proactively decisions without referring to the anti-fraud team every time. Fraud prevention platforms like nSure.ai used advanced AI models and deep machine learning techniques to identify fraudulent transactions in real-time, stopping fraud attacks before they can do damage while preserving UX. » Want to learn more? Let nSure.ai give you peace of mind
Asked 2 months ago
How Crypto Exchanges Use Automated KYC Verification to Reduce Chargebacks
Customer due diligence using methods such as KYC has been a key part of payment fraud prevention in the crypto industry for many years now, but online businesses are still losing money through fraud. Identity verification still has a big part to play in fighting crypto fraud, but using it alone is not viable in a high-risk industry such as crypto where over 75% of fraud comes from KYC accounts without an accompanying machine learning fraud prevention solution. AI technology dramatically reduces fraud and its symptoms such as chargebacks, making itself a viable investment. » Fraud prevention in crypto isn't as hard as you think. Here's why 4 Ways Crypto Exchanges Use KYC to Reduce Chargeback Rates While there are risks to trading crypto without KYC verification, findings show that in crypto there is less data, little time for manual review, and a higher likelihood of large-scale attacks. Therefore, it's best to use AI models that use complex behavioral analysis and “in & out” contextual features in tandem with regulatory-required KYC. Together with AI fraud prevention solutions, KYC helps to combat crypto fraud with the following advancements: Robotic process automation (RPA) Automating the KYC process using software checks that also match identities to payment details lifts the load off of employees, saving both money and time.Anti-money laundering (AML) KYC While AML usually involves a broad spectrum of fraud reduction practices, it's used in KYC to screen identities against blacklists, watchlists, and other compliance databases. This will catch fraudsters who previously committed fraud and prevents them from repeating it.Liveness checks KYC involving just using documents to verify identity still leaves an opportunity for identity fraud. 3D liveness checks scan a person's face to determine if they are human and match the documents provided, especially the details given for payment. » Should merchants allow crypto trading without KYC verification? Consider these risks Conclusion It may seem impossible to completely rid an industry like crypto of fraud like chargebacks, but advanced KYC verification together with fraud prevention solutions led by AI like nSure.ai makes it possible to significantly reduce chargeback rates as well as other types of fraud. KYC integrated into AI fraud prevention solutions creates a holistic fraud-fighting system built to recognize fraud in the high-risk crypto environment. Using KYC alone just doesn't cut it anymore. » Want to stay in the 0.5% chargeback "safezone"? Discover how to reduce chargebacks with predictive AI
Asked 2 months ago
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 3 months 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 3 months 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 3 months ago