Identity Verification Methods

🇫🇷 🤝🏻🇩🇪 : joint French-German proposals by our cyber agencies ANSSI - Agence nationale de la sécurité des systèmes d'information and the Federal Office for Information Security (BSI) Security on a decisive topic : the European digital Identity wallet 🇫🇷 ANSSI and 🇩🇪 BSI issued a new joint paper on remote identity verification ⭐️Following an initial joint publication in 2023, ANSSI and BSI are now releasing a new joint document aligned with the updated European regulatory framework. 🌍 Last month, Director General of ANSSI @Vincent Strubel & German counterpart Claudia Plattner reaffirmed the trusted relationship between #ANSSI and #BSI on the topic of remote identity verification. 📈 Since February 2024, the regulatory shift introduced by eIDAS 2 has brought forth the #EU Digital Identity Wallet, which may be issued based on remote identity verification. At the same time, cyber threats have continued to evolve, and European standardisation work on remote identity verification has progressed. Key takeaway =a secure and trusted EUDI Wallet depends on: 🔹Strong, harmonized standards 🔹Advanced defenses against remote attacks 🔹Cross-border interoperability and regulatory support. 🛡️ High Assurance is Essential for EUDI Wallet Onboarding. Remote identity proofing, particularly video-based methods, are being explored as alternatives to national eID systems but present significant technical and security risks. 🎯 3️⃣ Critical Verification Goals to ensure trustworthiness: 🔹Biometric genuineness 🔹Document authenticity (genuine, current, and physically possessed) 🔹Face matching (the face matches the ID document photo). ⚠️ 2️⃣ major categories of attacks: 🔹Presentation Attacks: use of photos, masks, or replayed videos in front of the camera. Exploit the fact that many ID document security features are not verifiable remotely. 🔹 Injection Attacks : Bypass the camera using pre-recorded or AI-generated data; Deepfakes and synthetic documents pose increasing challenges. ✅ Recommendations for Strengthening the Ecosystem 🔹Harmonise Evaluation Criteria -Establish pan-European test specifications directly mapped to LoA High. -Mandate biometric attack testing in evaluations 🔹Bridge the Document Verification Gap -Develop standards for remote verification of ID documents. -Promote chip reading over OCR where legally possible. -Ensure legal frameworks enable conformity assessment bodies to perform robust testing. #cyber #scybersecurity #Europe

🕵️♂️🚨 Take a look at the thread I came across on a dark web forum. You'll find countless similar and related offerings there: pre-verified accounts for sale, identity mules willing to rent out their accounts for quick cash, and repeated calls from fraudsters actively looking for such people. It got me thinking… The #𝟭 𝗺𝗶𝘀𝘁𝗮𝗸𝗲 most fintechs make when implementing biometric verification: They limit it to user onboarding. After that, they assume the account remains under the control of the same person. And if they notice something unusual (a new device, an unfamiliar location, or a suspicious usage pattern), most of the time they would simply ask a user to reverify via OTP sent to a phone number or email address. But these knowledge-based factors do not really verify identity! A bad actor can gain access to email, phone numbers, and credentials, pass the checks, and from an organization's perspective (relying on these trivial checks) appear as a legitimate user. So why do organizations keep using them instead of biometrics? → Some may hesitate to use it because they believe it will affect user experience. [In reality, it's hard to imagine a satisfied customer abandoning a service simply because of an occasional security check designed to protect them]. → Others may simply want to avoid paying biometric vendors for extra checks. [But long-term, which is more expensive: absorbing fraud losses or investing in additional biometric checks to keep a proper security level]. My point is simple: organizations need to stop blindly assuming the same person remains in control of an account throughout its entire lifecycle. In practice, this means making a liveness-proven biometric check whenever suspicious signals appear and on a periodic basis. Here's what that should look like✍️: 𝟭. Define high-risk triggers. New device. New geography. Password reset. Change of payout details. Unusual transaction velocity. 𝟮. Map each trigger to a proper step-up action. Not SMS / email OTP. A liveness-proven biometric check tied to the enrolled identity. 𝟯. Introduce periodic re-verification. For example, every 3-5 months for active accounts, regardless of visible risk signals. 𝟰. Bind high-impact actions to biometric confirmation. Withdrawals above a threshold. Adding beneficiaries. Changing KYC data. Enabling new payment instruments. 𝟱. Log and monitor biometric mismatches. Repeated failures should escalate to manual review, not fallback to weaker methods. 𝟲. Measure fraud reduction. Track step-up frequency, completion rate, and prevented losses to respond to changing risk dynamics. Done right, this helps prevent account takeovers caused by leaked or stolen credentials and mitigates identity mulling or account selling. ▂▂ Follow Ilya Vlasov 🕵️♂️ for more insights on #fraudprevention!

This month, deepfake candidates crashed the hiring process. An FBI reminder and several industry reports flagged fake applicants using AI video and voice to pass remote interviews. You probably noticed this too… the clips look real until something tiny breaks (lip sync, lighting, blink patterns). It’s not just a hiring problem; it’s a security problem that starts in HR. 🛡️ On a customer call last week, a CFO told me their “engineer” went silent when asked to tilt his head; the audio kept talking. That made me pause for a second. Here’s the thing: you don’t need fancy gear to lower the risk. Ask for a live ID check with random prompts in the same call, require a brief handwritten code held to camera, and confirm location signals match the declared country (and that’s the part nobody talks about). Follow with day‑1 device setup tied to the verified person, not just the email. If anything feels off, switch to an on‑site verification step before access is granted. For EOR, the risk multiplies across borders. At WorkMotion, we treat identity like a compliance asset: GDPR‑first consent capture, documented liveness checks, export‑control alerts for sensitive roles, and audit trails that actually get read. Earlier this quarter we tightened anomaly alerts on video interviews; it already saved a client from a costly mistake. In a remote‑first world, identity is the new perimeter.

𝗔𝗽𝗽𝗹𝗲 𝗪𝗮𝗹𝗹𝗲𝘁 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗣𝗮𝘀𝘀𝗽𝗼𝗿𝘁𝘀 𝗨𝗻𝗱𝗲𝗿 𝘁𝗵𝗲 𝗛𝗼𝗼𝗱 We've probably all seen the headlines saying “you can now store your passport in Apple Wallet.” But behind that simple message is a full identity-verification system built on hardware security, cryptographic attestation, and selective data sharing. In other words: this isn’t a photo of your passport. It’s Apple building identity rails Here’s what’s actually happening 👇 𝗛𝗼𝘄 𝗔𝗽𝗽𝗹𝗲’𝘀 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗣𝗮𝘀𝘀𝗽𝗼𝗿𝘁 𝗪𝗼𝗿𝗸𝘀 ▪️You scan the photo page of your passport ▪️The iPhone reads the NFC chip, pulling cryptographically signed data ▪️Apple runs liveness detection (movement + biometrics) ▪️The credential is encrypted and stored in Secure Enclave ▪️Every presentation event requires Face ID / Touch ID This creates a hardware-rooted identity credential, similar in spirit to how device PANs (DPANs) anchor wallet payments 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝘃𝗲 𝗗𝗮𝘁𝗮 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 When you present the virtual passport: ▪️A verifier (TSA, airport terminal, etc.) requests specific fields ▪️Apple shows you exactly what they’re asking for ▪️You approve with biometrics ▪️Only the requested attributes are shared, not the full passport This is minimum necessary disclosure, built directly into Wallet 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗜𝘀 𝗕𝗶𝗴𝗴𝗲𝗿 𝗧𝗵𝗮𝗻 “𝗣𝗮𝘀𝘀𝗽𝗼𝗿𝘁 𝗶𝗻 𝗮 𝗣𝗵𝗼𝗻𝗲” What Apple actually built is: ▪️A verified government-backed credential ▪️A hardware-secured container for identity ▪️A consent-driven sharing flow ▪️A standardized API for identity verification (ID Verifier) If payment tokenization solved “secure card reuse,” this solves secure identity reuse 𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗠𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝗙𝗶𝗻𝗧𝗲𝗰𝗵𝘀, 𝗠𝗲𝗿𝗰𝗵𝗮𝗻𝘁𝘀, 𝗮𝗻𝗱 𝗧𝗿𝗮𝘃𝗲𝗹 𝗔𝗽𝗽𝘀 Identity is often the slowest part of onboarding, this system changes that Benefits: ▪️Faster KYC → request verified fields (age, citizenship) without a doc upload ▪️Lower synthetic identity risk → tied to a real passport + device biometrics ▪️Higher trust at account creation → no more weak front-door checks ▪️Seamless travel flows → identity + payment could live in the same place Think of it like network tokenization, but for identity instead of PANs 𝗧𝗵𝗲 𝗕𝗶𝗴 𝗣𝗶𝗰𝘁𝘂𝗿𝗲 Apple started with airports for one reason: It’s the safest way to launch a verified credential at scale But the real impact will be in apps and merchants: → Age verification → KYC replacement → Account trust scoring → Travel identity flows → Marketplace onboarding The same way Apple Pay reshaped the checkout layer, Apple’s virtual passport will reshape the identity layer Source: Apple 🔔 Follow Jason Heister for daily #Fintech and #Payments guides, technical breakdowns, and industry insights

🚨 IAM Reality Check: Nation-State Actors Are Now an HR + Identity Problem 🚨 Amazon recently disclosed blocking 1,800+ suspected North Korean job applicants since April 2024. These weren’t random fraud attempts, this was systematic identity infiltration of the hiring and access lifecycle. From an IAM perspective, the strategy is worth dissecting 👇 🧠 The Adversary Playbook This wasn’t about credential stuffing or phishing. It was about becoming a legitimate identity: • Stolen or synthetic U.S. identities • Dormant LinkedIn profiles resurrected • Convincing resumes + interviews • Remote roles to bypass physical verification • “Laptop farms” in the U.S. to defeat IP & geo checks • RDP access so the real operator never touches the endpoint In one case, Amazon detected the fraud via keystroke latency, a signal that the “employee” was actually operating remotely from overseas. 🔐 Why Traditional IAM Controls Fall Short Most enterprise IAM stacks assume: • The user is already legitimate • The identity was verified upstream (HR, recruiting, helpdesk) • MFA protects against account takeover, not identity insertion But these attacks don’t bypass MFA, they successfully enroll into IAM as trusted users. Once issued: • A corporate identity • A managed device • Passwordless MFA …they look indistinguishable from a real employee. ✅ What Stops This: Multi-Factor Identity Verification (Not Just MFA) For IAM teams, the takeaway is clear: You need multi-factor identity verification across the identity lifecycle, not just strong authentication at login. That means combining: 🔎 Pre-Hire & Onboarding • Document + biometric verification • Liveness checks • Identity attribute consistency (name, geo, device, network) 🔁 Access & Credential Recovery • Step-up identity verification for helpdesk flows • No password or SMS fallback without re-proofing 🧠 Continuous Identity Assurance • Device binding + hardware attestation • Location, latency, and behavioral signals • Periodic re-verification for privileged access In Zero Trust terms: Never trust the identity just because it authenticated successfully. 🎯 The IAM Shift We’re Living Through We’ve spent years hardening authentication. Now attackers are attacking identity creation itself. For IAM leaders, this means: • Treat HR, ITSM, and IAM as one identity surface • Elevate identity verification to the same tier as MFA • Design for impersonation resistance, not just phishing resistance Strong auth is table stakes. Strong identity proofing is the differentiator.

93% of multifamily owners were victims of fraud in the past 12 months. So I spoke to 60+ operations leaders on what tools worked and what to avoid: There’s an arms race happening. And the fraudsters are getting better. Fake IDs. Falsified pay stubs. Synthetic identities. AI-generated documents that look flawless. With housing courts backed up and evictions taking months, the cost of being wrong is high. So we surveyed our Advisory Council of 60+ real estate operations leaders on what's working. Here's what we found: The two types of fraud you're fighting: 1/ First-party fraud: • Real identity • Fake information • Inflated income • Fake employment This is the most common. You end up with a resident who never pays after month one. 2. Third-party fraud: Stolen or synthetic identity. The person moving in isn't the real applicant. You discover it after chargebacks, law enforcement inquiries, or when the real identity holder disputes the lease. What's actually working: Identity verification stops third-party fraud. Some operators now require it just to schedule a tour. Income and document verification stops first-party fraud. But document verification isn't enough anymore. AI-generated pay stubs are too good. We evaluated 11 tools. 2 winners emerged. RentGrow: • Improved fraud filtering • Integrated with Yardi • Adaptable to changing regulations • Con: Some application drop-off Snappt: • Strong fraud prevention focus • Detailed reporting. Improved significantly in the last two years.  • Con: Not integrated with Yardi ScreeningWorks. Separate workflow creates friction. What else performed well: • RealPage Screening: Easy to use, strong integration. But background checks take ~10 days. • TransUnion SmartMove: Quick turnaround, renter-specific credit score. But limited detection. What's not working: Tools focused only on ID scanning. Fraud has evolved past fake IDs. One operator cancelled CheckpointID because basic ID verification no longer catches what's out there. The operator framework: • Integrate fully with your PMS • Prioritize fast prospect experience • Bundle verifications into application flow • Reduce tool fragmentation to avoid confusion The fraud arms race requires constant evolution. The tools that are best-in-class today may not be tomorrow.

𝗜𝗺𝗮𝗴𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮𝗹𝗹 𝗳𝘂𝗻 𝗮𝗻𝗱 𝗴𝗮𝗺𝗲𝘀 𝘂𝗻𝘁𝗶𝗹 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝗰𝗼𝗺𝗺𝗶𝘁𝘀 𝗳𝗿𝗮𝘂𝗱 The biggest thing that happened in AI last week was OpenAI releasing their new state-of-the-art image generator in ChatGPT, which went viral and flooded social media with cute Studio Ghibli-styled images. But if you work in financial crime compliance, this is the stuff of nightmares! For example, check out the image below where ChatGPT was used to create a synthetic ID in a single prompt. While this quick example won't get past today's ID verification solutions, a more finely tuned version probably will. This threat isn't limited to government IDs either. Any document used for KYC/KYB verification can now be forged in a similar way - e.g. incorporation documents, EIN letters, proof of address docs, bank statements. Here's how you can better protect against synthetic/forged documents: 1️⃣ 𝗚𝗼 𝗯𝗲𝘆𝗼𝗻𝗱 𝗢𝗖𝗥: Traditional document verification that only extracts text misses visual anomalies. Modern fraudsters can ensure the text is correct while tampering with visual elements. We use a combination of OCR, machine learning and multimodal models to analyze documents. 2️⃣ 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗶𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹: Every digital document leaves traces of its creation and modification history. At Parcha, we analyze document metadata to detect tampering attempts—examining everything from creation timestamps to digital signatures. These digital fingerprints reveal subtle traces that even sophisticated fraudsters can't completely erase. 3️⃣ 𝗠𝘂𝗹𝘁𝗶-𝗹𝗮𝘆𝗲𝗿𝗲𝗱 𝘃𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Deploy solutions that combine visual analysis, metadata inspection, and content validation. Each layer adds a barrier that fraudsters must overcome, exponentially increasing the difficulty of successful fraud. 4️⃣ 𝗣𝗶𝘅𝗲𝗹-𝗹𝗲𝘃𝗲𝗹 𝘀𝗰𝗿𝘂𝘁𝗶𝗻𝘆: The most advanced forgeries often show inconsistencies at the microscopic level. We've built systems that examine documents at the pixel level—analyzing font consistency, color patterns, and even subtle variations in image compression. As generative AI becomes more accessible, we'll see an arms race between fraudsters and compliance teams. The best prepared compliance teams will be those who leverage AI not just to detect obvious forgeries but to spot the subtle inconsistencies that even the most sophisticated AI-generated documents can't hide. The good news? The same technology powering this generation wave is also enabling more sophisticated detection. That's why we've focused on building multi-modal AI agents that examines documents the way human experts do—catching the subtle irregularities in seals, signatures, and formatting that traditional systems miss. Check out the link in comments to learn more!

A lot of people keep confusing biometric verification with identity verification. It's a dangerous misunderstanding - two completely different things. A biometric verification takes the initial biometric, uploaded by a user, and confirms it is that same person coming back each time. If a fraudster pretended they were me at onboarding, and uploaded a picture of themself, a biometric confirmation is only verifying that it is still the fraudster coming back to access the account. Why would a fraudster open an account with their own biometrics? One common ploy is creating a mule account - an account to which they can send stolen funds and extract them from the network. Identity verification is validating that when I open an account as Greg Kidd, I am actually Greg Kidd. The diligence that a financial regulator would expect a bank to be doing to stop mule accounts. ◼️ Does my phone number match? ◼️ Does my email match? ◼️ Does my PII match? ◼️ Does my device data match? ◼️ Do I have a matching, valid government ID? Identity verification is step one. Biometric verification is the recurring step. It is only useful if the initial identity verification was accurate.

What are the premier technologies for online identity authentication and why are they better? Most online customer authentication solutions today are probabilistic. I.E. - I have a 90% confidence level that this is Adam, based on everything I know about him and how closely that matches to how he is showing up in my on-boarding flow and usage data. This kills the customer experience for people like me running on Linux and VPN where I trip every risk flag. Probabilistic authentication technologies are getting better but not foolproof by any means. Their limitation is the inherent trade-off between security and convenience. While they do catch many bad actors, they also frustrate legitimate users with false positives and unnecessary friction. Zero trust identity verification - a digital signature key that can only be done by the person holding the credential - is the stronger standard. Digital signatures create stronger customer authentication because: They are deterministic, not probabilistic. With a valid signature, there is no doubt about the user's identity, eliminating the need for guesswork and risk scoring. They are tamper-proof. Any alteration to the data or signature will be immediately detectable, preventing fraud and unauthorized access. They offer greater privacy. Unlike knowledge-based questions or other factors used in probabilistic methods, digital signatures don't reveal any personal information about the user. While digital signatures might require an initial setup step, the benefits in terms of security, convenience, and privacy far outweigh the minor inconvenience. It's time for online platforms to move beyond probabilistic authentication and embrace zero trust principles for a more secure and seamless user experience.

Emma just opened a $50,000 line of credit. Problem is, Emma has no idea. That's the reality of modern identity fraud. Someone uploads a high-res photo of her stolen ID. The name is legitimate. The photo is clear. They take a matching selfie, maybe even coached through it by someone else. From the outside, everything checks out. But it's not Emma. It's someone using her identity to access a loan, a crypto wallet, or a retirement account. I've seen the data. This isn't edge case fraud. It's rather mainstream. So what do companies on the leading edge do instead? They run a waterfall of verification that adapts as new signals emerge. Each step goes deeper, shifting the confidence level based on what's discovered. 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥 𝐕𝐞𝐫𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐖𝐚𝐭𝐞𝐫𝐟𝐚𝐥𝐥 Step 1: 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐕𝐞𝐫𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 Is the ID real or fake? This gives us baseline legitimacy. Step 2: 𝐅𝐚𝐜𝐞 𝐌𝐚𝐭𝐜𝐡 Does the selfie match the ID? Adds biometric assurance, but still spoofable. Step 3: 𝐋𝐢𝐯𝐞𝐧𝐞𝐬𝐬 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 Is the person present, not a deepfake, mask, or screen replay? Now we're filtering out sophisticated fraud tactics. Step 4: 𝐆𝐞𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 Is the user logging in from the same country as their ID? A mismatch here downgrades trust fast. Step 5: 𝐀𝐝𝐝𝐫𝐞𝐬𝐬 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 Does the address exist? More importantly, does this person live there? That distinction changes the risk calculus significantly. Step 6: 𝐂𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥 𝐒𝐢𝐠𝐧𝐚𝐥𝐬 Are they on the phone? Is someone else in the frame? These subtle cues often indicate coercion, especially with vulnerable populations. Our data tells us this: 97% 𝐨𝐟 𝐬𝐨𝐩𝐡𝐢𝐬𝐭𝐢𝐜𝐚𝐭𝐞𝐝 𝐟𝐫𝐚𝐮𝐝 𝐚𝐭𝐭𝐞𝐦𝐩𝐭𝐬 𝐚𝐫𝐞 𝐬𝐭𝐨𝐩𝐩𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐫𝐞𝐚𝐜𝐡𝐢𝐧𝐠 𝐭𝐡𝐞 𝐟𝐢𝐧𝐚𝐥 𝐬𝐭𝐞𝐩, 𝐛𝐮𝐭 𝐭𝐡𝐚𝐭 𝐫𝐞𝐦𝐚𝐢𝐧𝐢𝐧𝐠 3% 𝐜𝐚𝐧 𝐜𝐚𝐮𝐬𝐞 𝐦𝐢𝐥𝐥𝐢𝐨𝐧𝐬 𝐢𝐧 𝐝𝐚𝐦𝐚𝐠𝐞 𝐢𝐟 𝐭𝐡𝐞𝐲 𝐬𝐥𝐢𝐩 𝐭𝐡𝐫𝐨𝐮𝐠𝐡. Each step isn't just a check; it's a recalibration of risk. And those recalibrations are backed by years of fraud modeling and behavioral insight. Most companies stop after one or two steps. The future of onboarding isn't about removing friction entirely. It's about knowing when and why to add it so that the right people get through, and the wrong ones don't. Let me ask you this: When was the last time you verified your identity in less than 10 seconds? If the answer is recent, you might want to reconsider how secure that process was.