Optimizing high-risk user flows for better trust

𝐎𝐧𝐞 𝐥𝐞𝐬𝐬𝐨𝐧 𝐦𝐲 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐚 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐞𝐚𝐦 𝐭𝐚𝐮𝐠𝐡𝐭 𝐦𝐞 𝐚𝐛𝐨𝐮𝐭 𝐔𝐒 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫𝐬: Convenience sounds like a win… But in reality—control builds the trust that scales. We were working to improve product adoption for a US-based platform. Most founders instinctively look at cutting clicks, shortening steps, making the onboarding as fast as possible. We did too — until real user patterns told a different story. 𝐈𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐫𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐭𝐡𝐞 𝐣𝐨𝐮𝐫𝐧𝐞𝐲, 𝐰𝐞 𝐭𝐫𝐢𝐞𝐝 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐜𝐨𝐮𝐧𝐭𝐞𝐫𝐢𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞: -Added more decision points -Let users customize their flow -Gave options to manually pick settings -instead of forcing defaults -Conversions went up. -Engagement improved. Most importantly, user trust deepened. You can design a sleek two-click journey. But if the user doesn’t feel in control, they hesitate. Especially in the US, where data privacy and digital autonomy are non-negotiable — transparency and control win. Some moments that made this obvious: People disable auto-fill just to type things in manually. They skip quick recommendations to compare on their own. Features that auto-execute without explicit consent? Often uninstalled. It’s not inefficiency. It’s digital self-preservation. A mindset of: “Don’t decide for me. Let me drive.” I’ve seen this mistake cost real money. One client rolled out an automation that quietly activated in the background. Instead of delighting users, it alienated 20% of them. Because the perception was: “You took control without asking.” Meanwhile, platforms that use clear prompts — “Are you sure?” “Review before submitting” Easy toggles and edits — those build long-term trust. That’s the real game. What I now recommend to every tech founder building for the US market: Don’t just optimize for frictionless onboarding. Optimize for visible control. Add micro-trust signals like “No hidden fees,” “You can edit this later,” and toggles that show choice. Make the user feel in charge at every key step. Trust isn’t built by speed. It’s built by respecting the user’s right to decide. If you’re a tech founder or product owner, stop assuming speed is everything. Start building systems that say: “You’re in control.” 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐚𝐭 𝐜𝐫𝐞𝐚𝐭𝐞𝐬 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐬𝐭𝐢𝐜𝐤𝐬. 𝐖𝐡𝐚𝐭’𝐬 𝐲𝐨𝐮𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬? 𝐋𝐞𝐭’𝐬 𝐝𝐢𝐬𝐜𝐮𝐬𝐬. #UserExperience #ProductDesign #TrustByDesign #TechForUSMarket #businesscoach #coachishleenkaur LinkedIn News LinkedIn News India LinkedIn for Small Business

Midwest businesses do not need flashy AI. We need safe, reliable automation that respects people. A new safety report caught my eye this week. It shows a practical way to curb unhealthy “parasocial” dynamics in chatbots by adding a second model that evaluates each turn and only intervenes when all five checks agree. In tests it stopped the harmful chats early without blocking the normal ones. Here is why that matters on Main Street. Law firms, CPA practices, vet clinics, and real estate offices are rolling out chat and voice agents to handle intake and FAQs. If those agents drift into flattery or attachment, trust breaks. This pattern creates a calm middle ground where the bot stays helpful and human boundaries stay clear. The approach is simple to implement. Add an evaluation step after each message. Use a tolerant threshold that requires unanimous flags before you block or rewrite. Pair it with a separate “sycophancy” check so you do not confuse being agreeable with being harmful. Keep it practical. Limit the five-pass check to high-risk flows like intake, payments, and health questions. Log every intervention. Review samples weekly with a human. Track two numbers: false blocks and time to intervene. Aim to catch issues within 2 to 3 turns while keeping normal chats flowing. We have seen this mindset pay off. Small businesses can implement this in a week. What is the one conversation in your firm that needs a guardrail today? #SMBAI

🚀 Why AI Implementations in Fintech fail ? The 73% Accuracy Trap. An year ago, I met a fintech founder and he shared that last quarter, their fraud detection model was catching 73% of suspicious transactions. He was celebrating. I wasn't. Here's the uncomfortable truth 👇 In fintech, Accuracy metrics are Vanity metrics. What matters is the Precision-Recall Trade-Off in real user scenarios. Now, here's the problem with "Good Enough" AI: That 73% accuracy meant we were flagging 12,000 legitimate transactions daily as fraudulent. Each false positive cost us $23 in customer service overhead + an average 48-hour delay in legitimate transactions. The hidden cost? Customer lifetime value erosion. So, instead of optimizing for accuracy, I suggested him to restructure the success metrics around business impact: 📊 Metric 1: Weighted Precision Score Weighted false positives by transaction value. High-value transactions got 3x penalty weight. Result: 31% reduction in high-value customer friction 📊 Metric 2: Contextual Recall Windows Different recall thresholds for different user cohorts New users: 95% recall (strict) Established users: 78% recall (trusted) Result: 67% improvement in user onboarding completion 📊 Metric 3: Temporal Decay Modeling User risk scores decay over time with good behavior Implemented progressive trust scoring Result: 89% of users graduate to lower-friction tiers within 90 days The Breakthrough Insight: AI models should optimize for user journey progression, not just event classification. How did we Implement this? - Switched from binary classification to multi-class risk scoring (5 tiers) - Implemented ensemble methods combining transaction patterns, device fingerprinting, and behavioral biometrics - Built feedback loops where customer service interactions retrain the model in real-time What was the Business Impact? - Customer acquisition cost down 34% - Time-to-first-transaction improved by 2.3 days - Customer satisfaction scores up 28 points - False positive rate down 67% The bigger lesson: In fintech, AI product management isn't about building smarter algorithms - it's about building systems that get smarter about your customers. Most companies optimize AI for technical metrics. Winners optimize AI for Customer Lifetime Value. What's your experience with AI model performance v/s business impact misalignment ? How do you bridge that gap ? #AI #productmanagement #fintech #datadriven #machinelearning #customerexperience #productmetrics

Designing Safer AI Agents: The Power of Human Intervention Drawing from OpenAI's AI Agent Design Guide, one key takeaway stands out: plan for human intervention. It’s not just a safety net—it’s a critical strategy to enhance your AI agent’s real-world performance while maintaining a seamless user experience. Early in deployment, human intervention helps identify failures, uncover edge cases, and build a robust evaluation cycle. By implementing mechanisms for graceful handoffs, agents can escalate tasks they can’t handle—whether it’s a customer service query passed to a human agent or a coding task returned to the user. When to Trigger Human Intervention: Exceeding Failure Thresholds: Set clear limits on retries or actions. If an agent struggles (e.g., failing to grasp customer intent after multiple tries), it’s time to escalate. High-Risk Actions: Sensitive or irreversible tasks—like canceling orders, approving large refunds, or processing payments—should involve human oversight until the agent’s reliability is proven. By embedding human intervention into your AI agent’s design, you’re not just mitigating risks—you’re paving the way for smarter, safer, and more trustworthy systems. Let’s build AI that empowers users while keeping humans in the loop!

🔑 Trust isn't just a feature - it's THE product, especially in high-consideration purchases. After analyzing dozens of successful products in insurance, real estate, and home tech, I've noticed two principles that consistently drive growth: Power of Defaults: Smart defaults should optimize for user outcomes, not just conversion. Trulia nailed this by auto-creating rental resumes that increased response rates by 30%. Don't Make Me Think: Every extra decision point is a chance for doubt. When life insurance enrollment was simplified from 4 clicks + call center to one seamless flow, revenue doubled. The most fascinating example? Brilliant Smart Home They made personal photos the default screen state - seems simple, right? Result: 80% of households uploaded photos, which led to 2x device attachment per home. Key insight for product leaders: In high-consideration purchases where trust is paramount (think: life insurance, real estate, enterprise software), the fastest path to growth isn't speed - it's removing uncertainty while maintaining confidence. 3 ways to build trust into your product: • Default to the safest option, not the most profitable • Break complex decisions into smaller, contextual steps • Design for how people naturally make decisions What I see most teams get wrong: They optimize for short-term conversion instead of long-term trust. In markets where purchases impact life, family, or major assets, trust compounds. Build it into your core experience. What examples have you seen of products building/breaking trust through their experience? Would love to hear your perspectives. 🤔 Read more here: https://lnkd.in/g6Mg6isq

Checkout conversion optimization tip: Screen for fraud pre authorization. Most fraud checks happen after a user has clicked “buy” and the payment has started. But we can get earlier. Pre-auth signals include 1,000s of possible clues to a user intent like: 👉 Email reputation: Is this email address being aged but mostly dormant, or active? 👉 Expert mouse movement: Is this user creating accounts and getting to checkout a little bit “too fast?” 👉 Device red flags: Is the user hiding their location, and secretly in a high risk jurisdiction? The right signals before auth can screen for good users and bad users. Any high risk users, we can throw in increased friction before starting a payment. The result? Less bad users get as far as the payment and fraud checks. Good users convert faster. The second-order impact is even greater. As more fraud is screened out pre-auth, merchants will see card issuers begin to approve a higher percentage of transactions by default as they begin to trust that merchant more and more. Screening for fraud at pre-auth creates a flywheel of better conversion over time. Get control of pre-auth, and you will get better control of conversion.

How the fraud team maximises revenue (...by turning risk data into revenue opportunities): Most people think fraud teams just block bad guys. But smart fraud teams are actually revenue drivers. Here's how to use real-time risk signals to maximize sales while stopping fraud: 🔷 Dynamic journey mapping — Analyze user behavior from first click to checkout. Build trust scores that evolve throughout the session. Low-risk users get express lanes, suspicious ones get guardrails. 🔷 Intelligent friction layering — Add or remove security steps based on real-time risk levels. Known customers on trusted devices? One-click checkout. New user with suspicious patterns? Extra verification. 🔷 Smart payment routing — Match payment methods to risk profiles. Premium customers get instant access to high-value features. New accounts start with lower limits that grow with trust. 🔷 Continuous session monitoring — Track subtle risk changes during user sessions. Automatically adjust security measures up or down as behavior patterns shift. If you Implement this approach.... The results: - Reduction in customer churn - Faster checkout times = more revenue, quicker - Increase high-value transactions and customer LTV All while maintaining genuine user rates over 98% and keeping fraud rates under 1%. Want to see how to use real time device intelligence as part of a risk-based UX framework? Drop "+" below and I'll share how you can turn the fraud team into a revenue driver.