Daniil Shvets’ Post

Are chargebacks draining your revenue and causing headaches for your business? It's time to take charge and implement effective chargeback deflection strategies. In this post, we'll explore some best practices that can help you reduce chargebacks and protect your bottom line. Let's dive in! 1. Understand the root causes: Start by identifying the main reasons for chargebacks in your business. Common causes include fraud, friendly fraud, and dissatisfaction with products or services. By understanding these root causes, you can tailor your deflection strategies accordingly. 2. Strengthen authentication protocols: Implement robust authentication measures, such as two-factor authentication, to ensure that only legitimate customers make purchases. This can help prevent fraud and reduce the risk of chargebacks. 3. Improve customer communication: Proactive communication with customers can go a long way in preventing chargebacks. Keep customers informed about order status, delivery updates, and any potential delays. Clear and transparent communication can help resolve disputes before they escalate to chargebacks. 4. Optimize your refund and return policies: Make your refund and return policies clear and easily accessible to customers. Transparent policies can reduce the likelihood of customers filing chargebacks out of frustration or confusion. Ensure that the return process is hassle-free and customer-friendly. 5. Implement fraud detection tools: Invest in advanced fraud detection tools that can identify suspicious transactions in real-time. These tools utilize machine learning algorithms and behavioral analysis to flag potential fraudulent activities, helping you catch fraudsters before they cause chargebacks.

A prospect nearly bought $50k of fraud "magic beans". Can you guess what the magic word was? "Out-of-the-box" Recently, a Fintech reached out. Their pain? Textbook early-stage startup: 😔 Fraud controls are declining too many good customers 😣 Scared to loosen up for fear of getting slammed with fraud 😖 Eyeing pricey alternatives, dreading the integration nightmare 🤬 Their vendor’s solution? $50k for their “out of the box” rules set Sound familiar? You're definitely not alone. But here are the dirty secrets about those shiny "out-of-the-box" fraud rules: One size fits none 🍪✂️ Your business is unique. Cookie-cutter rules are for cookies, not businesses. High false positives 🙅♂️ Nothing kills customer love faster than constant, unnecessary blocks. Fraud floodgates 🌊 Loosen the rules a smidge, and suddenly you're Fraud City, population: You. Integration insanity 🤯 The "easy" solution often needs more dev time than building your own. Costly complacency 💸 That $50k for "ready-made" rules? It's just the tip of the iceberg. The real cost? Lost customers stunted growth, and a fraud strategy as reliable as your Series A 'hockey stick' projections. So, what's the alternative? Tailor-made strategy. Yes, it takes more upfront effort. But the payoff? Massive. I've helped Fintechs craft custom strategies that: 💰 Slashed false positives by 55% 💰 Boosted approval rates by 14% 💰 Cut fraud losses in half All without breaking the bank or the dev team's spirits. Remember: Effective fraud prevention is never plug-and-play. Don't let "out-of-the-box" become "out-of-business."

Unlocking the Power of Cloud-Based Fraud Detection and Prevention In today’s digital age, fraud threats are constantly evolving. As businesses move online, robust fraud detection and prevention mechanisms are crucial. Cloud-based solutions offer a powerful and flexible approach to these challenges. The Rise of Cloud-Based Solutions Cloud-based fraud detection and prevention leverage the scalability, flexibility, and real-time capabilities of cloud computing to provide comprehensive protection. These solutions monitor, detect, and prevent fraud across various channels, including online transactions, mobile apps, and in-store purchases. Key Benefits - i. Scalability and Flexibility: Adaptable for businesses of all sizes without significant upfront investment. ii. Real-Time Detection: Analyzes transactions in real-time to quickly identify and respond to fraud. iii. Advanced Analytics and AI: Uses AI and machine learning to detect patterns and improve accuracy over time. iv. Cost-Effectiveness: Reduces costs by leveraging cloud infrastructure, with maintenance and updates handled by providers. v. Integration and Collaboration: Seamlessly integrates with existing systems through APIs. Key Features - i. Transaction Monitoring: Continuous monitoring to spot suspicious activities. ii. Identity Verification: Multi-factor authentication to verify user identities. iii. Behavioral Analytics: Detects deviations from normal user behavior. iv. Predictive Analytics: Uses historical data to predict and prevent future fraud. Case Study: IBM Security Trusteer - IBM Security Trusteer is a prime example of a cloud-based fraud detection and prevention solution. It offers a suite of services that help businesses assess risk, detect various types of fraud, and establish identity trust. Trusteer’s cloud-based platform provides real-time protection across all channels, ensuring a seamless and secure user experience. Conclusion - As fraudsters become more sophisticated, businesses must stay ahead by adopting advanced fraud detection and prevention solutions. Cloud-based platforms offer a robust, scalable, and cost-effective way to protect against fraud, ensuring the safety of both the business and its customers. By leveraging real-time analytics, AI, and seamless integration, these solutions are setting new standards in fraud management. #CloudBased #FraudDetection #FraudPrevention #FraudManagement #FinancialCrime #DigitalTransformation #RiskManagement #Compliance https://lnkd.in/dxAVkAS4 https://lnkd.in/dV7Y2Nmm

Thanks Soufiane OMRANA, FICA, CFE, CAMS, CFCS for sharing. Like many other forms of crime, it is essential in cases of fraud to understand all relationships in their entirety, end to end. It is rarely possible to detect FC by merely observing a single transaction from one person to another. This makes the EU’s Instant Payments regulation particularly challenging, as it lacks a comprehensive FCP framework. Fraudulent setups, which can easily occur domestically, should be prevented rather than detected after they have been carried out. Fraud, especially amid extensive geopolitical tensions and tight finances, must be considered a significant risk for all operators - whether individuals, organizations, or nations.

Understanding the Power of Phone Numbers in Fraud Prevention In the bustling web ecosystem of today, where every transaction and interaction leaves a digital footprint, businesses are in a constant battle against fraudsters. As someone who’s been in the trenches, fighting the good fight in fraud prevention, I’ve seen firsthand how traditional methods often fall short. But, what if I told you there’s a secret weapon hiding in plain sight? Yes, I’m talking about phone numbers. Let’s dive into how these everyday digits can be your crystal ball in predicting and preventing fraud, ensuring your onboarding process is as smooth as a buttered slide, leading straight to real users and real revenue. https://lnkd.in/dnGA_3Mv #osint #fraudprevention #riskanalysis #scamalert

🚀 Journey Data in Action: Critical Use Cases 🚀 Fraud and bots are evolving fast, but Journey Data keeps you ahead by tracking every user interaction across the entire customer journey. Here are some key use cases that show how Journey Data transforms fraud prevention: 💳 Card Testing: Identify and block fraudulent card testing attempts before they impact your payment systems. 🔐 Account Takeovers: Detect compromised accounts in real time by monitoring behavioral changes and suspicious login patterns. 🔑 Credential Stuffing: Spot large-scale login attempts using stolen credentials and prevent unauthorized access to your platform. ⚠️ Fraud Chargebacks: Analyze customer behavior to quickly identify fraudulent chargebacks and reduce revenue loss. 🤖 Bot Attacks: Stop bots in their tracks by identifying unusual behavior and blocking malicious automated activity. 💸 Refund Fraud: Uncover patterns of fraudulent refund attempts by linking suspicious behavior across multiple sessions. With Journey Data, you’re not just reacting to fraud—you’re proactively securing every customer interaction from the first click to the last. 🔗 See Spec in action—book a live demo today: https://lnkd.in/ecvYFRM2

We keep talking about fraud detection, but that’s the easy part. It’s identifying the good customers that is the real challenge. 🔪 🐶 Whether I use machine learning or rules, fraud patterns are easy to mark. Moreover, if I try to pinpoint the fraud pattern, I usually end up limiting the coverage of my solution, ending up in even more fraud bypassing it. So really, most fraud detection logics are embarrassingly simple. Logics like “distance between IP address and Billing address”. Or “risk score > 90” if you’re using a model. Thing is, I also end up wrongly blocking way too many good customers. And so the real solution development cycle begins. Let’s take the first example above, there could be many explanations for such a behavior: 🏝 The customer might be traveling. 🌏 The customer might be a soldier stationed away from home. 👩🎓 They might be attending a university on the other side of the country. 🕵♂️ The customer might be using VPN for privacy reasons. 💞 The customer might be visiting their home country. 🛑 The IP might be wrong due to data integration issues. 🌐 The IP might be geo-located wrongly. And these are just examples from the top of my head! (Side note: think of the headache the travel industry needs to deal with…) Researching, identifying and codifying these “stories” to be part of my solution are the tough part in the process. ⁉ But why is this all important? Because I’ve realized that acquiring “fraud” intelligence is of lesser importance. What I’ve learned (and missed many times in the past) is - Acquiring “good” intelligence is not less and often more effective. 🏢 Does the IP address belong to a business or a university? ✈ Does the IP address belong to an airport or a hotel? 📌 Have I seen this customer appearing with this IP in the past? 📍 Have I seen this customer appearing from a nearby location in the past? 💻 Is the customer using the same device they always use? Again, these are just examples. Even though we look at the same assets (IP, Device, etc.), we may want to look at them in different ways to uncover “Good” indicators. As opposed to focusing only on “Fraud” indicators. The thing to remember is that most vendors would focus on the latter and less on the former. 💡 Pro tip: next time you evaluate a vendor, ask them how they can help you identify good customers. Their answer will help a lot in determining if they are the right fit for you.

The latest article from IOL Personal Finance highlights a concerning trend – a sharp uptick in fraudulent activities impacting businesses worldwide. It's a sobering reminder of the evolving risks businesses face in today's digital landscape. As businesses grapple with these challenges, understanding the tools available to combat fraud is essential. Enter FraudGraph AI – an advanced solution designed to address the complexities of fraud detection and prevention. FraudGraph AI leverages sophisticated algorithms and machine learning to analyze vast amounts of data, identifying anomalous patterns indicative of fraudulent behavior. By providing real-time insights, businesses can proactively mitigate risks and safeguard their assets. In an era where fraudsters continuously adapt their tactics, staying ahead requires a comprehensive approach. By incorporating solutions like FraudGraph AI into their security strategies, businesses can enhance their resilience against fraudulent schemes. To learn more about the evolving landscape of fraud prevention and how FraudGraph AI can bolster your defenses drop us a message. Together, let's navigate these challenges and safeguard the integrity of our businesses. https://lnkd.in/eeaiGiD2

Fraud detection? An interesting study. Fraud in fund transfers is difficult to detect because it often involves multiple transactions and relationships between different customers. This study published recently proposes a solution using advanced technologies like self-attention mechanisms and graph neural networks (GNNs). Step 1: Understanding Transaction Patterns Most transactions happen irregularly, like someone transferring money in small amounts one day and large amounts weeks later. The system organizes these irregular transactions into a timeline, making it easier to spot unusual patterns, such as sudden spikes in transaction amounts or frequent transfers to new accounts. For example, if someone transfers small amounts to five different accounts and then receives a large transfer back, the model notices this unusual pattern. Step 2: Analyzing Customer Relationships Fraud is often not isolated to one account. It may involve linked customers, like business partners or co-signers on a loan. Graph neural networks help the system analyze these connections. For instance, if one account starts showing suspicious activity, like multiple international transfers, and another account linked to it has a similar history, the system flags both. Imagine a scenario where Account A frequently transfers money to Account B, and Account B has links to several flagged accounts. The system sees this network of suspicious activity and raises an alert. Step 3: Addressing Data Imbalance Fraud cases are much rarer than regular transactions, so the system uses a technique called data augmentation. It creates additional fake fraud cases based on the patterns of known ones. This helps the system learn better. For example, if only 10 fraud cases exist, it generates hundreds of similar examples to train the model. Step 4: Combining Everything The system merges these insights—transaction timelines and customer connections—into a single, trainable model. By doing this, it achieves higher accuracy. For instance, in tests, the model detected 27% of fraud cases compared to 22% from traditional methods.

🌐 Fraud: The Hidden Threat to Modern Businesses 🌐 Fraud is more than just a nuisance—it's a growing threat that costs businesses billions every year. From e-commerce giants to small startups, no one is immune. Fraudulent activities don’t just drain revenue; they erode customer trust, damage reputations, and jeopardize relationships with payment processors. With chargeback rates creeping up, exceeding critical thresholds like 0.65%, businesses risk severe consequences, including fines or even disconnection from payment service providers. But it’s not all doom and gloom. How Can Businesses Fight Back? The answer is simple: Disputeur is here to help. 🔹 Comprehensive Fraud Detection: With advanced analytics and machine learning, Disputeur identifies suspicious activity in real-time, helping businesses stay one step ahead of fraudsters. 🔹 Seamless Chargeback Management: Our platform streamlines the dispute resolution process, reducing chargeback ratios and protecting your relationships with payment processors. 🔹 Tailored Prevention Strategies: We work with your unique business model to implement fraud prevention measures that maintain a smooth customer experience while safeguarding your revenue. 🔹 Expert Guidance: With years of experience, our team provides insights and solutions designed to mitigate risks, giving you peace of mind and more time to focus on growing your business. Fraud doesn’t have to be a constant battle. With Disputeur, you can protect your revenue, maintain customer trust, and operate confidently in today’s competitive marketplace. 💼 Ready to take control? Let’s talk about how Disputeur can transform your approach to fraud prevention. #FraudPrevention #Chargebacks #Ecommerce