atna_careers

Author: Careers DB Admin

  • How to Reduce Drop-offs in KYC Without Compromising Risk

    How to Reduce Drop-offs in KYC Without Compromising Risk

    The Problem: KYC Kills Conversions

    KYC is critical—but also painful. Every additional step in identity verification increases user friction, which directly impacts conversion rates, especially in onboarding-heavy industries like fintech, BNPL, and neobanking.

    • Users abandon when upload fails
    • They quit when video liveness takes too long
    • They hesitate when asked for too much too soon

    But removing checks is risky. So the real question is: How can platforms improve completion rates without compromising fraud detection or compliance?

    The Solution: Intelligent, Friction-Less KYC

    Here’s how smart platforms are reducing drop-offs while still managing identity risk:

    1. Replace Liveness Video with Image-Based Verification

    Videos take time to record, fail in poor networks, and intimidate users. ATNA uses image-only checks—verifying document integrity and facial consistency without requiring a selfie video.

    Impact:

    • 30% reduction in dropout during selfie stage
    • Works well in Tier-2/3 markets with low bandwidth

    2. Pre-Fill What You Can from the Document

    Instead of asking users to manually type their name, DOB, and address, extract those directly from the uploaded ID and auto-fill the form. ATNA’s AI-KYC does this in real time.

    Impact:

    • Reduces user effort
    • Prevents mismatches due to typos

    3. Skip Extra Documents Using ATNA Score

    Instead of asking for additional documents when you’re unsure, use ATNA Score to calculate real-time onboarding risk based on document quality, digital footprint, and passive behavior signals.

    Impact:

    • 25% reduction in document re-request
    • Better user experience without cutting risk coverage

    4. Start with Passive Signals First

    Before even asking the user for a document, analyze device, IP, network, and behavioral traits using ATNA’s Digital Footprinting.

    Impact:

    • Identify suspicious users before asking for verification
    • Personalize the level of KYC required

    5. Use Conditional Workflows Based on Risk Tier

    All users don’t need full KYC. ATNA lets you adjust flows:

    • Low-risk: Document + image validation
    • Medium-risk: Add footprinting + extra ID
    • High-risk: Redirect to manual review

    Impact:

    • Right-sized effort for every user
    • Reduces over-verification and under-verification risks

    Conclusion

    Reducing KYC drop-offs isn’t about removing checks—it’s about making them smarter, faster, and invisible where possible. ATNA delivers adaptive KYC without compromising.

  • Building a Risk Scoring Model That Actually Works

    Building a Risk Scoring Model That Actually Works

    Why Most Risk Scores Fail

    Risk scoring is often seen as a magic formula. But in reality, many scoring models fall short because they:

    • Rely on outdated or siloed data
    • Are hardcoded and inflexible
    • Lack transparency and explainability
    • Don’t adapt to evolving fraud patterns

    What Makes an Effective Risk Scoring Model?

    An effective model isn’t just a number it’s a decision enabler. It should:

    • Combine multiple risk dimensions (not just one source)
    • Offer tunable sensitivity based on your use case (e.g., lending vs. onboarding)
    • Provide explainable breakdowns of why a user was scored high or low
    • Be easy to integrate, monitor, and update

    Step-by-Step: Building a Real-World Risk Score with ATNA

    1. Start with the Signals You Trust

    Identify the risk signals you already collect or can plug in:

    • Document Signals (via AI-KYC): ID validity, layout inconsistencies, tampering indicators
    • Behavioral Signals (via Digital Footprinting): IP reputation, device anomalies, location mismatches
    • Business Signals (via KYB): registration status, UBO mapping, GST number match
    • External Signals (via AML Intelligence): watchlist presence, media flags

    Tip: The broader the signal set, the more resilient your score will be.

    2. Assign Weights Based on Context

    A lending platform might weigh behavior and ID quality more heavily. A marketplace might favor KYB and AML flags.

    With ATNA Score, you can:

    • Define custom weights per signal category
    • Apply different scoring logic to different user types or flows
    • Adjust thresholds as you gather real-world feedback

    3. Normalize and Aggregate

    ATNA automatically normalizes raw signal data into a standard format (0–100 scale), making it easy to compare and combine.

    Your scoring engine should:

    • Penalize strong risk signals (e.g., fake ID) heavily
    • Allow positive offsets (e.g., verified GST, clean AML)
    • Be explainable at every step

    4. Automate Decisions, Not Just Scores

    What matters most is <b> what you do with the score:</b>

    • 0–40: Auto-approve
    • 41–70: Escalate to additional checks
    • 71–100: Manual review or reject

    ATNA Score lets you embed rules directly in your system—or pipe results into your workflow engine, CRM, or onboarding UI.

    5. Monitor, Adapt, and Evolve

    No score is static. As fraud evolves, your model must too.

    ATNA gives:

    • Real-time dashboards to monitor score distributions
    • Signal-level feedback for failed approvals
    • Logs for auditing and machine learning refinement

    Pro Tip: Start Simple, Then Optimize

    You don’t need a perfect model to launch. Start with core signals, go live, gather data and then iterate.

    Conclusion

    A great risk score doesn’t just tell you who to trust it gives your platform the power to act, adapt, and scale.

    RATNA’s modular scoring engine was built exactly for this. No black boxes. Just signals that speak your language.

  • What Is Digital Footprinting and Why It Matters in 2025

    What Is Digital Footprinting and Why It Matters in 2025

    The New Frontier of Risk Intelligence

    In a world where IDs can be faked and forms can be filled by bots, the real question is: Can you trust the person behind the screen?

    Digital Footprinting gives you the answer—without asking the user a single extra question.

    What Is Digital Footprinting?

    Digital Footprinting is the science of analyzing the invisible traits of a user like their device, network, and behavior—to assess trust or risk in real time.

    It runs passively in the background and feeds high-signal data into fraud detection, onboarding, and compliance decisions before users upload a document or fill a form.

    Think of it as the digital body language of your users.

    Why It’s a Must-Have in 2025

    In 2025, fraud looks different:

    • Synthetic identities are indistinguishable from real ones
    • Bots and emulators mimic human interaction
    • Static checks like ID verification aren’t enough

    Digital footprinting adds a contextual, real-time layer of defense that legacy systems miss without slowing down genuine users.

    What You Can Detect with ATNA’s Digital Footprinting

    Signal Type Examples

    • Device: Jailbreak/rooted devices, screen resolution mismatches, font fingerprints
    • Network: IP reputation, TOR/proxy detection, geolocation drift
    • Behavior: Mouse movement anomalies, typing cadence, click patterns
    • Session: Shared devices, returning risk profiles, emulator environments

    Real-World Use Cases

    • Fintech Onboarding: Detect fake borrowers before asking for documents
    • Marketplace KYC – Device/IP: Flag merchant accounts using the same device/IP
    • Marketplace KYC – Risk Scoring: Adjust ATNA Score based on passive risk
    • Insurance Claims: Catch bots or replay frauds during claims submission

    Business Impact

    • 30% fewer fraudulent signups
    • Up to 70% fewer manual reviews
    • 100% passive — no added friction

    Seamless Integration

    • Lightweight JS or SDK embed
    • Feeds directly into ATNA Score or your own workflows
    • Real-time API access + dashboards

    Summary: Trust the Behavior, Not Just the ID

    In 2025, static checks won’t keep you safe.

    Digital Footprinting tells you what kind of user you’re dealing with before they type a single word.

    Know who’s real. Know who’s risky.