Consumer Finance Protection

🚨Important Safety 🚨 Beware of Scams Targeting Online Shoppers The Indian government has warned online shoppers about scams targeting users during upcoming sales events, such as: - Amazon's Big Billion Days Sale - Flipkart's Big Billion Day Sale - Other festive season sales Scammers exploit the excitement and urgency surrounding these events to deceive unsuspecting shoppers. Scam Tactics: 1. Fake Order Cancellation/Tracking Messages: - Scammers send messages claiming your order is cancelled or delayed. - Messages may contain malicious links or ask for personal info. Example: "Your Amazon order #XXXXX is cancelled. Click here to reschedule delivery." 2. Phishing Links: - Scammers send links claiming to offer exclusive discounts or gift cards. - Clicking on these links compromises your device's security. Example: "Get 50% off on Flipkart! Click here to redeem your coupon." 3. Fake Customer Support: - Scammers pose as customer support representatives. - They ask for sensitive info, such as OTPs or credit card details. Example: "Hello, this is Flipkart support. Your account needs verification. Please provide your OTP." 4. Discount and Gift Card Scams: - Scammers offer unrealistic discounts or gift cards. - These offers often require sharing personal info or making payments. Example: "Win a ₹10,000 Flipkart gift card! Click here to claim." Protect Yourself: 1. Verify sender information. 2. Avoid suspicious links. 3. Report suspicious messages. 4. Use strong passwords and two-factor authentication. 5. Keep your device's operating system and apps updated. Specific Safety Tips for Amazon and Flipkart Sales: 1. Check official websites for genuine offers. 2. Be cautious of urgent or threatening messages. 3. Don't share sensitive info. 4. Monitor your accounts and transactions. 5. Use official customer support channels. Report Suspicious Activity: - Cybercrime Reporting Portal: cybercrime.gov.in - National Consumer Helpline: 1800-11-4000 Stay vigilant and shop safely during these sales events! Share this alert with friends and family to ensure everyone stays safe! #OnlineShopping #ScamAlert #StaySafe #CyberSafety #ConsumerProtection #EcommerceScams #ShoppingTips #FraudPrevention #ProtectYourself #SecureShopping #DigitalSafety #FestiveShopping #CyberCrime #PhishingAwareness #ScamAwareness

🔕 Design Guidelines For Better Notifications UX (https://lnkd.in/eAUuMVGw), with practical techniques on how to make notifications more useful and less annoying — with snooze mode, by exploring how and when they are triggered and measuring their use. Notifications Decision Tree (Slack): https://lnkd.in/eunw_VFX 🚫 High frequency of notifications is a very frequent complaint. ✅ Not all notifications are equal: some are more useful than others. ✅ Users value updates from close contacts, transactions, insights. 🤔 Users ignore automated, irrelevant, promotional notifications. ✅ Sending fewer messages can improve long-term product use. ✅ Let users choose notification modes (silent, regular, power). ✅ Suggest switching from push notification to email digests. ✅ Let users snooze, pause, mute if high volume is expected. ✅ Track how often notifications are ignored and acted upon. 🚫 Avoid disruption and notification fatigue by sending less. In many products, setting notification channels on mute is a default, rather than an exception. The reason for that is their high frequency which creates disruptions and eventually notifications fatigue, when any popping messages get dismissed instantly. But not every notification is equal. The level of attention users grant to notifications depends on their nature, or, more specifically, how and when they are triggered. People care more about new messages from close friends and relatives, bank transactions and any actionable and awaited confirmations. To design better notifications UX, we break down notification design across 3 levels of severity: high, medium, and low attention. And then, we define notification types by specific attributes on those levels — e.g. alerts, warnings, confirmations, errors, success messages, or status indicators. Most importantly, we scrutinize the decision tree to find the right timing to send the right types of notifications. The timing is really everything, so you might end up designing notification profiles — frequent users, infrequent users, one-week-experience users, one-month-experience users etc. In fact, Facebook has been experimenting with the notification frequency and learned that both user satisfaction and app usage improve by sending fewer notifications (link in the comments). And: whenever possible, allow your users to snooze and mute notifications *for a while*, and eventually you might even want to suggest a change of medium used to consume notifications. And when in doubt, postpone, rather than sending through. 🌳 UI Decision Trees → https://lnkd.in/eXr7nZdE 🍣 Interface Design Patterns → https://lnkd.in/eZv7EfMU 🔮 How To Measure UX → https://measure-ux.com 🎢 Upcoming UX workshops → https://web-adventures.com Happy designing, everyone! 🎉🥳 #ux #design

I want to run a little experiment here. 👇 Fill in the ______ below with the first thing that comes to mind. > Your fitness tracker helps you improve your physical health. > Your calendar helps you manage your time. > Your bank helps you _______. 🤔 (Go ahead, I'll wait...) I don't talk a lot about my work, but I’ve spent the last 6 years working with some of the world’s largest banks. Using technology, design, and behavioural psychology make financial services more human - from mortgages anyone can understand to investing platforms that don't require a finance degree. While this work has been deeply rewarding, I can't help but feel we're only scratching the surface of what's possible when we truly align financial technology with human wellbeing. Everyone has a bank account. But most people have never experienced banking designed to actively improve their financial wellbeing. Imagine if… - 🛒 Mon 8:45 PM: While shopping online, your bank overlays a small notification: "This exact item was 30% cheaper last month and has followed a seasonal discount pattern for 3 years. Historical data suggests waiting 3 weeks could save you £67. Would you like us to alert you when the price drops below £120?" - 🔄 Wed 8:15 AM: A forgotten subscription is about to renew. Your bank sends a "Still using this?" reminder with one-tap cancellation. - ✈️ Thu 12:30 PM: You’re browsing holidays. Your bank calculates a "possible by" date based on your savings, and offers to add £1 for every £10 you save toward this goal. - 🏆 Sun 9:15 PM: Your weekly financial summary arrives: "Great job this week! you spent 12% less on your "stuff I don't really need" spending and moved your home purchase timeline forward by 2 weeks. We've automatically distributed the £157 you saved: 60% toward your emergency fund, 40% to your home purchase goal, and added a bonus 5% from us to your 'fun money' space as a reward for staying on track." AI can analyze your spending patterns to give you insights you'd never see. It can recognize when you're ready to save for a home before you do. It can nudge you toward financial wellness in moments when you're most receptive. The technology exists. The behavioral science is proven. What's missing isn't innovation - it's imagination about what banking could be. The most exciting financial innovation won't be a new payment method or credit card - it will be the fundamental shift from profiting from financial products to profiting from financial progress. Perhaps the most important question isn't directed at banks, but at us as customers: What would we demand if we truly believed our financial institutions could be partners in prosperity rather than just processors of transactions? - Pictured: 1: My Ultrahuman ring, which has been a game-changer in giving me insights to improve my health. 2: Motion calendar, which automatically adjusts my schedule to help me be more efficient. Not pictured: My banking app. For obvious reasons 😂

Yesterday, Colorado’s Consumer Protections for #ArtificialIntelligence (SB24-205) was sent to the Governor for signature. If enacted, the law will be effective on Feb. 1, 2026, and Colorado would become the first U.S. state to pass broad restrictions on private companies using #AI. The bill requires both developer and deployer of a high-risk #AI system to use reasonable care to avoid algorithmic discrimination. A High-Risk AI System is defined as “any AI system that when deployed, makes, or is a substantial factor in making, a consequential decision.” Some computer software is exempted, such as AI-enabled video games, #cybersecurity software, and #chatbots that have a user policy prohibiting discrimination. There is a rebuttable presumption that a developer and a deployer used reasonable care if they each comply with certain requirements related to the high-risk system, including Developer: - Disclose and provide documentation to deployers regarding the high-risk system’s intended use, known or foreseeable #risks, a summary of data used to train it, possible biases, risk mitigation measures, and other information necessary for the deployer to complete an #impactassessment. - Make a publicly available statement summarizing the types of high-risk systems developed and available to a deployer. - Disclose, within 90 days, to the attorney general and known deployers when algorithmic discrimination is discovered, either through self-testing or deployer notice. Deployer: - Implement a #riskmanagement policy that governs high-risk AI use and specifies processes and personnel used to identify and mitigate algorithmic discrimination. - Complete an impact assessment to mitigate potential abuses before customers use their products. - Notify a consumer of specified items if the high-risk #AIsystem makes a consequential decision concerning a consumer. - If the deployer is a controller under the Colorado Privacy Act (#CPA), it must inform the consumer of the right to #optout of profiling in furtherance of solely #automateddecisions. - Provide a consumer with an opportunity to correct incorrect personal data that the system processed in making a consequential decision. - Provide a consumer with an opportunity to appeal, via human review if technically feasible, an adverse consequential decision concerning the consumer arising from the deployment of the system. - Ensure that users can detect any generated synthetic content and disclose to consumers that they are engaging with an AI system. The law contains a #safeharbor providing an affirmative defense (under CO law in a CO court) to a developer or deployer that: 1) discovers and cures a violation through internal testing or red-teaming, and 2) otherwise complies with the National Institute of Standards and Technology (NIST) AI Risk Management Framework or another nationally or internationally recognized risk management #framework.

Many Product Owners and Engineering Leaders I’ve spoken to have told me how difficult it is to manage their transactional messaging infrastructure. For starters, there are way too many CPaaS tools / APIs to manage for different channels and there’s no self-serve visibility of analytics & real-time monitoring for Product teams. This translates into roughly 50-60 developer hours each month spent maintaining the different API integrations for each tool, and sometimes even a dedicated resource (~160 hours/month) to get it right. That got me and the team MoEngage thinking — surely there had to be a smarter, more optimal way to do this? As we started digging into Transactional Messaging, a host of other problems came up: - Cost: Transactional messaging spends across SMS, Email and Whatsapp continue to increase significantly. Adoption of transactional messaging via Push notifications, is at a nascent stage - Regulatory compliance: Regulations require brands to deliver transactional messages to customers despite payment failures or downtime, but tracking and managing this across multiple tools can be challenging. - Send time: Certain platforms were taking too long for critical alerts. Customers can’t be waiting around for 30-60 seconds for fraud alerts and OTPs. They need to receive it near-instantaneously. As we worked on a solution, one thing was clear — we needed a Unified Transactional Messaging platform that also guaranteed reliability, instant send time, and 100% regulatory compliance while also being able to save messaging costs. But was this possible? We developed a product, tested it internally, and dogfooded it for critical alerts before market release. It worked like magic! With excitement and anticipation, we launched it to select customers who gave us overwhelmingly positive feedback, and it’s currently being used by some of the largest enterprises in financial services & retail industries. They loooove being able to: - Send critical alerts instantly. - Get 99.999% uptime and 100% SLA maintenance for easy regulatory compliance. - Use a Unified platform for Email, SMS, and Push Notifications. They are able to increase mobile push opt-ins, as they are now delivering valuable alerts via Push notifications. - Save up to 60% on transactional messaging costs, with the ‘Smart Send’ functionality What is this magic product? It’s called MoEngage Inform. You should check it out if you resonate with any of these challenges. #techdebt #costoptimization #transactionalmessaging #standardization

Every brand we audit has the same three flows. → Checkout abandonment. → Cart abandonment. → Welcome series. On a $5M store, the gap between a 3-flow setup and an 8+ flow setup is $750K-$1.25M in annual revenue sitting in automations that haven't been built yet. This is every other flow a $5M store is using: BROWSE ABANDONMENT Trigger: same day as browse session. Cart and checkout abandonment captures the 3-5% of traffic that adds to cart. Browse abandonment targets the other 90-95%. POST-PURCHASE CROSS-SELL Trigger: 3-5 days post-delivery. Avg 60% open rates on transactional emails. Order confirmation emails convert 22x better than campaign emails. This is how one-time buyers become repeat customers. REPLENISHMENT/REORDER Trigger: predictive timing based on consumption cycle. Predictive timing per customer, not a flat delay. If your product lasts 30 days, the trigger fires at 28. High-impact for any consumable. WIN-BACK Trigger: 60-90 days of inactivity. 1 in 3 people who click an automated message make a purchase. Shift messaging from product to relationship. Too soon (<30 days) feels pushy. Too late (90+ days), and they've forgotten you. BACK IN STOCK Trigger: immediate when the product becomes available. Pre-qualified buyers. Intent already established. Fastest-growing automated flow type. It’s up 4x year-over-year. PRICE DROP ALERT Trigger: when a wishlisted item drops in price. Show old price vs. new price. Direct-to-cart CTA. Works especially well on higher-AOV products where price sensitivity is the conversion barrier. SUNSET/RE-ENGAGEMENT Trigger: 90-120 days of zero engagement. List hygiene as much as revenue. Non-optional after Gmail/Yahoo's 2025 enforcement changes. VIP/LOYALTY TIER Trigger: spend or frequency threshold. Loyalty programs drive 13.71% avg AOV lift. Exclusive access, early drops, personalized offers. Protects your highest-LTV customers from competitor poaching. You probably don’t have all of these in play. Build them in this order: • Week 1 → checkout + cart + welcome • Weeks 2-3 → browse abandonment + post-purchase • Week 4+ → layer in the rest The revenue shows up almost immediately. You're capturing behavior that was already there, just uncaptured.

If everything is an alert… nothing is. Let’s fix that. Here’s how: 1️⃣ Define your risk parameters. Start with clear criteria: transaction size, velocity, geolocation, high-risk merchants, etc. The more precise, the better. 2️⃣ Segment customer profiles. Different risk levels require different thresholds. A first-time crypto trader shouldn’t trigger the same alerts as a seasoned investor. 3️⃣ Use historical data. Look at past transactions to set realistic thresholds. If your system flags 90% of transactions, it’s not helping, it’s drowning you in noise. 4️⃣ Set up layered alert levels. Not all alerts should be treated equally. Low-risk anomalies? Review queue. High-risk red flags? Immediate escalation. 5️⃣ Automate escalation workflows. Who gets notified? What actions follow? Manual reviews? SAR filings? Define the process in advance to avoid bottlenecks. 6️⃣ Test and tweak regularly. Set it and forget it? Nope. Monitor alert effectiveness, refine thresholds, and adjust rules as customer behavior evolves. 🔹 Tired of false positives eating up your time? OMNIO automates transaction monitoring with smart alerts, risk-based prioritization, and real-time adjustments so you only focus on what truly matters. How do you fine-tune your alerts? Let’s swap notes in the comments! 👇

🌟 Day 77: Kafka in Microservices – Kafka-Powered Notifications 💌 How Your Emails Are Always on Time! Ever wondered how you instantly get email notifications for every transaction, order, or event? It’s all thanks to Kafka, the backbone of modern notification systems! Let’s dive into how Kafka ensures real-time, reliable email delivery. 📬 What is Kafka-Powered Email Notification? Kafka acts as the messaging pipeline between your system's events and the email service. It ensures that every critical event is tracked, processed, and delivered as email notifications to users in real-time. 🛠️ How Kafka Powers Notifications: 🔵 1. Event Generation – Producers -> When an event occurs (e.g., payment success, new order, or account update), it is published to Kafka. -> Producers publish these events to Kafka topics like: - email.transaction.alerts - email.order.confirmation Example: 💡 You purchase a product online, and the Order Service sends an event like order.placed to Kafka. 🟢 2. Event Streaming – Brokers - Kafka brokers manage these events and distribute them to the appropriate topics. - Events are stored reliably until they are processed by the consumer services. Example: Kafka ensures your order.placed event reaches the correct topic even during high traffic. 🟡 3. Notification Processing – Consumers - The Email Service (consumer) subscribes to Kafka topics and processes the events. - Emails are dynamically generated based on templates. Example: 🛒 For order.placed, the email might look like: “Thank you for your purchase! Your order is confirmed and will be delivered by [date].” 🔄 Kafka in Microservices – Why It Works :- ✔️ Real-Time Processing: Kafka ensures users get instant notifications. ✔️ Scalability: Can handle millions of notifications during peak events like Black Friday. ✔️ Reliability: No lost messages, even if the email service is temporarily down. ✔️ Decoupling: Email services operate independently from other microservices. 💡 Real-Life Use Case: Scenario: You book a flight ticket. Here’s what happens behind the scenes: 1️⃣ The Booking Service publishes an event: ticket.booked. 2️⃣ Kafka streams the event to the topic: email.booking.confirmation. 3️⃣ The Email Service consumes the event and sends: ✈ “Your flight from Bangalore to Mumbai is confirmed!” 🛠️ Key Configurations for Kafka-Powered Emails: Retries: To handle transient failures. Partitions: Ensures faster processing of high volumes. Consumer Groups: Enables multiple email services to work in parallel. 🎯 Takeaway: Kafka is the superhero behind real-time email notifications, ensuring every message reaches your inbox securely and on time! 💬 What other creative uses of Kafka have you seen? Share in the comments! 😊 Follow to learn more .... !

Fintech and payment nerds, when you see your frends and family over the holiday please, please, please caution them against financial fraud. Coach them so that they notice suspicious behavior and empower them to resist! Here is some guidance to share (links to add'l resources in the comments): 1️⃣ Red Flags🚩 👉Unsolicited contact via email, phone, or text. 👉High-pressure tactics demanding immediate action. 👉Offers that seem too good to be true. 👉Requests for sensitive information like Social Security numbers or bank details. 👉Payment demands via gift cards, cryptocurrency, or wire transfers. 2️⃣ Protect Your Personal Information 👉Verify requests before sharing personal or financial details. 👉Use strong passwords and two-factor authentication. 👉Avoid entering sensitive data over public Wi-Fi. 3️⃣ Learn to Recognize Common Scams 👉Phishing Scams: Fraudulent emails/messages that steal your information. 👉Tech Support Scams: Fake alerts claiming your device is compromised. 👉Pig-Butchering Scams: Long-term grooming on social media to push fake investments. 4️⃣ Monitor Your Financial Activity 👉Regularly review bank and credit card statements. 👉Set up alerts for suspicious account activity. 👉Consider a credit freeze to prevent identity theft. 5️⃣ Protect Older Adults 👉Share fraud prevention tips with older family members. 👉Encourage open communication about suspicious activity. 6️⃣Take Action Against Fraud 👉Report incidents to your bank, credit union, or local authorities. 👉Notify the Federal Trade Commission (FTC) via reportfraud.ftc.gov. 👉Change passwords and secure compromised accounts. Bottom line: 👉Don’t trust unsolicited offers or deals that require urgent action. 👉Always verify the identity of individuals or organizations contacting you. 👉Stay informed about emerging scams and update devices regularly for security. 👉 Finally, do not be ashamed to alert someone and ask for help if you get emeshed in a scam Use your expertise and whatever influence you may have to spread the word. The last thing you want is to be on the receiving end of a panicked phone call from a loved one.