elanтнιraιyan

#Husband, #Dad, #Engineer, #Developer

[elanтнιraιyan @elanthirian]

Ask me anything!

I lead Anti-Abuse & Anti-Fraud Engineering, focusing on scams, phishing, platform abuse, threat intelligence, and AI security.

I share practical insights, research, and engineering lessons, and I enjoy mentoring people in cybersecurity.

[elanтнιraιyan @elanthirian]

10/10

Quick field distinctions that matter:
CNP vs ATO: CNP uses stolen credentials directly. ATO starts with a compromised but legitimate account login, so it often looks trusted longer-especially when shipping to a newly added address.
Card testing detection: velocity spikes (many small transactions from the same device/IP/email domain) followed by larger attempts. Models trained on wide data catch this earlier than local threshold rules.
Friendly vs true fraud: friendly fraud is a real customer disputing a valid charge. True fraud is a third party using stolen data. Strong transaction-level evidence, behavioral logs, and delivery confirmation win far more friendly disputes.
What fraud pattern is currently costing your team the most time or money? Drop it below 👇
#FraudPrevention #Ecommerce #Payments #Chargebacks #RiskEngineering

[elanтнιraιyan @elanthirian]

E-commerce payment fraud, refund abuse, and false declines are costing businesses an estimated $448 billion a year.
Card-not-present (CNP) fraud is the dominant form because there’s no physical card to inspect. Everything rests on data the customer types at checkout. At merchant scale, even a sub-1% fraud rate quickly becomes thousands of bad orders per month plus the operational tax that follows.
After more than a decade building detection systems, here’s what actually matters. 🧵1/10

[elanтнιraιyan @elanthirian]

9/10
Operationally, size and prioritize your review queues around impact, not volume. High-value or high-risk cases should surface first. Maintain clear escalation paths for complex disputes.
Track leading indicators of new campaigns rather than waiting for losses to appear in the P&L. The teams that stay ahead treat fraud prevention as a continuous monitoring and adaptation problem, not a one-time rules project.

[elanтнιraιyan @elanthirian]

8/10
The biggest mistake I see teams make when trying to scale: relying on static rules.
Attack patterns shift constantly. Every new rule you add creates maintenance debt and false-positive tuning cycles. You end up perpetually one step behind.

Production-grade systems use machine learning models trained across broad, diverse transaction data. These models surface emerging patterns without constant manual intervention while keeping review queues manageable.

[elanтнιraιyan @elanthirian]

7/10
Post-authorization monitoring is still under-used but critical.
Fraud that passes initial checks often reveals itself through subsequent events: sudden shipping address changes, order modifications, or unusual fulfillment patterns. This layer matters most for high-AOV merchants or businesses with long fulfillment windows.
Decisioning should be dynamic: obvious fraud auto-declined, edge cases escalated to manual review or step-up verification, and friction applied only where the risk justifies the customer experience cost.

[elanтнιraιyan @elanthirian]

6/10
Prevention that actually scales works in layers across the full customer lifecycle, not just at authorization time.
Pre-authorization: device fingerprinting, IP reputation, velocity, and behavioral signals during browsing and form-filling catch card testing and ATO reconnaissance before a transaction is even submitted.
At authorization: a comprehensive risk score that weighs the full signal set. High-risk transactions are declined or routed for review; clean traffic proceeds with minimal friction.

[elanтнιraιyan @elanthirian]

5/10
Why most detection systems still struggle: individual signals are almost always ambiguous.
A shipping address that doesn't match billing can be a gift. A "risky" device might have been sold or shared. A disposable-looking email can belong to a privacy-conscious power user.
None of these are reliable red flags in isolation. Effective detection requires fusing account history, device intelligence across sessions, network-level linkages between transactions, and behavioral signals collected throughout the entire journey.

[elanтнιraιyan @elanthirian]

4/10
Two more that quietly destroy margins:
• Friendly / first-party fraud – legitimate customers disputing valid charges, either deliberately or because they don’t recognize the transaction. Looks identical to third-party fraud in your system.

• Refund/return fraud – order placed with stolen credentials, then returned for a refund before the chargeback arrives. You lose the goods and the cash twice.

[elanтнιraιyan @elanthirian]

3/10
The main attack types I see at scale:
• Classic CNP – stolen card details used directly
• Card testing – low-value probes from the same device/IP to validate cards before larger hits
• BIN attacks – systematic enumeration of card numbers from known bank prefixes that can flood queues
• Account takeover (ATO) – credential stuffing or phishing to access real accounts and use stored payment methods, often shipping to new addresses

[elanтнιraιyan @elanthirian]

2/10
The real cost is never just the order value.
Chargebacks carry $15–$100 processing fees on top. Cross network thresholds and you face higher acquiring fees, mandatory monitoring programs, and in worst cases, loss of the ability to accept cards at all.

Add the engineering and analyst time spent on disputes plus the revenue lost when good customers get declined. Industry benchmarks put the total cost at roughly 4.6× the direct fraud loss once everything is counted.

[elanтнιraιyan @elanthirian]

200,000+ scam sites from a single ecosystem should be a wake-up call.

Fraud has become productized.

Attackers no longer build scam sites one by one they buy templates, deploy at scale, rotate domains, and automate operations.

The challenge isn't taking down one website anymore. It's dismantling the infrastructure, supply chain, and monetization ecosystem behind them.

That's where defenders should focus.

#cybersec #scam

[SecurityWeek @SecurityWeek]

Chinese Framework Powers 200,000 Scam Sites https://t.co/ZaZfSOYhjb

[elanтнιraιyan @elanthirian]

From now on…

Any AI Lab: We built the world's most powerful AI.

Same AI Lab: We're not releasing it tho.
Safety first ✌️

[elanтнιraιyan @elanthirian]

#YouTubeDOWN 🥲

[elanтнιraιyan @elanthirian]

@YouTubeIndia is youtube down??

[elanтнιraιyan @elanthirian]

#YouTubeDOWN

[elanтнιraιyan @elanthirian]

@systemdesign42 @memdotai

[Satish Acharya @satishacharya]

Ask questions! #GST #Manipur #nirmalasitharaman

[elanтнιraιyan @elanthirian]

Do you remember when you joined X? I do! #MyXAnniversary #15years #twitterAge

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boys will be boys

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[elanтнιraιyan @elanthirian]

@Aravindselvaa @Im_Roops @I_aM_Pradeep @Petarappp @anuthapradeep Bigshot aayta apdi thaan pola @Aravindselvaa