AI-Based Wallet Audit: How Blockchain History Becomes Your Personal Brand in Web3

X Space #22 — AI-Based Wallet Audit: How Blockchain History Becomes Your Personal Brand in Web3. Watch the full recording on YouTube ↗ · Listen on X ↗

X Space #22 is the most philosophically deep session in ChainAware’s series — and also the most practically actionable for anyone operating in Web3 daily. Co-founders Martin and Tarmo start not with technology but with social psychology and game theory: why does fraud flourish in blockchain ecosystems specifically, and what structural features of anonymous systems make bad behavior rational rather than exceptional? Only after establishing this foundation do they introduce the AI-based wallet audit as the designed countermeasure — not a compliance checkbox, but a trust infrastructure that solves the human-to-human trust problem that blockchain consensus algorithms were never designed to address.

Two Distinct Trust Problems in Blockchain

Tarmo makes a distinction at the outset of X Space #22 that clarifies why the fraud problem in Web3 is so persistent despite blockchain’s reputation for transparency: there are two completely separate trust problems in blockchain, and technology has only solved one of them.

The first trust problem is consensus trust — the question of whether transactions are valid, unaltered, and resistant to manipulation by adversaries. Blockchain’s consensus mechanisms (proof of work, proof of stake, and their variants) solve this problem elegantly. Even if 49% of network participants are malicious, the majority maintains transaction integrity. This is the trust that fills blockchain whitepapers and academic literature. It is genuinely solved.

The second trust problem is human-to-human trust — the question of whether the person or entity behind a wallet address is honest, reliable, and worth transacting with. This is the trust that matters for every practical decision in Web3: should I respond to this service proposal? Can I trust this counterparty? Is this person who they claim to be? Blockchain consensus algorithms say nothing about this question. The address is valid — but the human behind it is completely unknown. As Tarmo explains: “If you have two anonymous people in blockchain, can I trust this participant or can’t I trust this participant? This is completely different from the original consensus algorithms based trust. So we have two kinds of trust in blockchain. One of them is solved. And the second is what we are talking about.” For the broader context of how this relates to ChainAware’s full product vision, see our ChainAware AI agents roadmap.

Web2’s Trust Infrastructure: What Web3 Is Missing

To understand what Web3 lacks, Martin and Tarmo describe what Web2 has built over decades to address the human-to-human trust problem. The infrastructure is extensive and largely invisible to users who have always operated within it.

In business-to-business (B2B) contexts, trust is established through legal registration, credit information systems, contract law, and verifiable trading histories. Companies know their counterparties’ names, addresses, financial histories, and legal status. Additionally, if a contract is breached, court systems provide recourse. The entire framework creates strong incentives against fraud because the cost of getting caught is real and permanent.

In business-to-consumer (B2C) contexts, platforms like Amazon access credit scoring systems that assess customers’ financial reliability. Consequently, a customer with a strong credit history can purchase on credit seamlessly, while a customer with a poor history faces additional friction. The credit card itself is a trust mechanism — it links every transaction to a verified identity with established credit accountability.

The Web3 Contrast

Web3 has none of this. As Tarmo describes the founder’s daily experience: “In Web3, all you know is the blockchain address. You don’t know name, you don’t know address, you don’t know birthday. All you know is the address of your potential partners or clients. And as a founder you get daily 20 scam messages — we want listing, we want marketing, we want Twitter calls, we want developers. You are scammed all day long, and which of these anonymous guys can you take seriously?” Furthermore, even verification attempts fail: LinkedIn profiles can be faked, emails can be spoofed, identity documents can be falsified. The absence of a reliable trust infrastructure is not a minor inconvenience — it is a structural feature that shapes every Web3 interaction. For more on how ChainAware addresses this, see our guide to how ChainAware is building Web3’s missing infrastructure.

The Social Psychology of Anonymity: Why Fraud Is Rational

The most intellectually distinctive section of X Space #22 is Tarmo’s analysis of why fraud is not just possible in anonymous systems but structurally incentivised. This is not a technological observation — it is a social psychology observation supported by decades of experimental research.

The key finding from social psychology experiments on anonymous environments is consistent and striking: when participants are anonymous and receive no feedback from peers about their behavior, they rapidly begin behaving below established social norms. The timeline is short — in controlled experiments, this shift often occurs within 20 minutes of the anonymity condition being introduced. The mechanism is straightforward: social norms are maintained partly by the expectation of social consequences — reputation damage, disapproval, exclusion. Remove those consequences, and the norms lose much of their enforcement power.

Blockchain Anonymity and Norm Collapse

Blockchain provides precisely the conditions that social psychology identifies as norm-collapsing: complete anonymity (multiple addresses, no identity linkage), zero feedback (no social response to bad behavior from the community), and no punishment mechanism (fraud victims can only warn others, not recover funds or impose consequences). As Tarmo explains: “If you have systems where participants are anonymous and they don’t get feedback — then it goes very fast into a direction that participants start behaving below established social norms. It happens very fast.” The design of permissionless blockchains, in other words, inadvertently creates an environment optimised for fraud by removing all the social mechanisms that normally discourage it.

Importantly, this is not a claim that most blockchain participants are dishonest. It is a claim that the structural conditions of anonymous systems produce more dishonest behavior than those same people would exhibit in environments with social accountability. The same person who would never commit fraud in a face-to-face business context may behave very differently when anonymous, unmonitored, and materially incentivised to do so. For more on how this dynamic creates the trust problem that ChainAware’s products address, see our guide to Web3 transaction monitoring.

Game Theory and the Positive Feedback Cycle of Fraud

Martin extends Tarmo’s social psychology analysis with a game theory perspective that explains not just why fraud starts but why it escalates. The logic is a positive feedback cycle: bad behavior is rewarded, bad behavior is not punished, therefore bad behavior increases in scale and sophistication over time.

Martin traces the path of a small-scale scammer entering Web3: “A small scammer joins the sector. Because he has certain personality traits, he starts scamming. He does one scam and looks — he’s earning and he’s not getting punished. He will do a second scam, maybe a little bit bigger. He’s earning, he’s not getting punished.” Each successful scam that goes unpunished provides both a financial reward and a confirmation that the risk-reward ratio favors continuing. The rational response, from a purely financial perspective, is to scale up the operation.

From Individual Scammers to Scam Farms

The escalation from individual scammer to organised “scam farm” follows directly from this game theory logic. Scam farms — the sophisticated, professionally organised fraud operations that run Telegram social engineering campaigns, create fake tokens with manufactured hype, and employ social psychologists to design manipulation strategies — are the natural endpoint of a system where small-scale fraud repeatedly succeeds without consequences. As Martin notes: “You’re getting to this scam farms and so on, with very advanced telecommunications — anticipating more advanced because the bad behavior is honored.” The professional sophistication of these operations — social psychologists, engineers, dedicated marketing infrastructure — reflects the substantial profits available when the target environment has no effective countermeasures. For more on how ChainAware’s predictive tools counter this, see our guide to Web3 AI agents.

The Ecosystem Cost: How Fraud Inhibits Web3 Growth

Martin and Tarmo are not merely describing fraud as a problem for individual victims — they frame it as a structural constraint on the growth of the entire Web3 ecosystem. This framing matters because it changes the urgency calculus: solving the trust problem is not just about protecting individual users, it is about unlocking an enormous potential growth trajectory that fraud is currently blocking.

The mechanism is straightforward. New users entering Web3 encounter fraud, scams, and rug pulls early in their participation. Many of these users — particularly those who are new to crypto and don’t yet have the experience to recognise manipulation — get burned. Some recover and stay. Many leave permanently, warning their networks to stay away from Web3. Twitter saw a user who had been scammed 128 times — an extreme case, but illustrative of the attrition problem. Every departing user represents not just a lost participant but a negative word-of-mouth signal that makes future recruitment harder and more expensive.

The Unit Cost Paradox

Martin identifies a paradox at the heart of Web3’s growth problem: Web3 platforms offer dramatically lower unit costs for business processes compared to Web2 equivalents — transactions are faster, cheaper, and more efficient. This is genuine technological superiority that users who successfully adopt Web3 recognise and value. However, the trust problem prevents most potential users from ever reaching the point where they can experience these benefits. As Martin explains: “The Web3 ecosystem could grow much faster if the trust issue will be solved. But if the trust issue is not solved, we’re getting this scam farms like a cancer eating away the energy of the Web3 ecosystem, and Web3 ecosystem is growing much less than it will grow in the opposite scenario.” Solving the trust problem does not merely reduce fraud — it unlocks the growth premium that Web3’s superior unit economics should be generating. For more on how this connects to ChainAware’s growth tools, see our guide on AI marketing for Web3.

Why KYC and Permissioned Blockchains Fail

The obvious proposed solution to anonymous system fraud is to remove anonymity — implement KYC (Know Your Customer) requirements or use permissioned blockchains that require identity verification. Martin and Tarmo address this approach directly, explaining why it fails on multiple levels despite appearing logical at first glance.

Permissioned blockchains with mandatory KYC have been tried. They exist today on CoinGecko alongside public chains. The adoption results are instructive: users gravitate overwhelmingly toward permissionless, anonymous blockchains because those are where innovation happens, where new protocols launch, and where the genuine technological promise of blockchain is being realised. As Tarmo notes: “Users go into blockchain where is innovation. Users don’t go into blockchain where they just are restricted by KYC forms.” KYC blockchain adoption data confirms this pattern — the market has consistently rejected heavily permissioned systems.

The Falsified Document Problem

Even where KYC is implemented, it provides weaker protection than it appears. Tarmo identifies the core issue: “If you go with falsified documents into the KYC process, you have already falsified KYC. So what was the benefit? No benefit.” Identity documents can be forged, deepfakes can defeat liveness checks, and professional identity theft operations specifically target KYC-gated platforms because the identity they acquire grants access to systems that trust them implicitly.

KYC Doesn’t Predict Future Behavior

Furthermore, even a perfectly executed KYC process only verifies identity — it says nothing about how someone will behave. A person’s legal name and address do not predict whether they will honour agreements, act honestly in disputes, or avoid fraudulent behavior. Tarmo makes the point directly: “Crypto AML with KYC doesn’t tell anything about how the guy will behave in the future. So it’s only this initial hello. What you need is a stamp on your behavior.” The question that actually matters for trust decisions is not “who is this person?” but “how has this person behaved, and how will they behave?” On-chain transaction history answers this question far more reliably than a verified government ID. For more on why behavioral prediction is superior to identity verification, see our predictive AI for Web3 guide.

The Blockchain as a Trust Engine: Data Quality Advantage

Having identified what doesn’t work, Tarmo presents the foundational insight that makes ChainAware’s approach viable: blockchain data is exceptionally high quality for behavioral prediction, and this quality advantage makes AI-based wallet auditing more accurate than any Web2 trust mechanism.

The argument starts with a comparison to the data sources that power Web2 trust systems. Google’s behavioral targeting uses search history and browsing behavior — signals that reflect momentary curiosity and passive information consumption. A search query carries weak predictive signal because it requires no commitment, no deliberation, and no financial stake. Tarmo explains: “Financial data has enormously high accuracy in doing predictions. It is not like data from some social network or search behavior data where you have maybe not such high accuracy. Financial data has enormously high accuracy.”

Why Financial Transactions Signal Behavioral Truth

Every blockchain transaction is a financial decision. Borrowing $500 on Aave, purchasing an NFT, providing liquidity to a pool — each of these required deliberate thought, wallet approval, and real financial commitment. The person behind the transaction considered it carefully before executing it. This deliberateness means the transaction carries far more information about the person’s values, intentions, risk tolerance, and behavioral patterns than any equivalent Web2 signal. Furthermore, blockchain data is permanent, public, and tamper-proof — it cannot be selectively deleted, strategically edited, or hidden behind privacy settings. The entire history is always available for analysis. For the full explanation of blockchain data quality, see our predictive AI for Web3 guide and our analysis of Web3 behavioral user analytics.

How AI-Based Wallet Audit Works

With the theoretical foundation established, Martin walks through what ChainAware’s Wallet Auditor actually produces for any given address. The output is a comprehensive behavioral profile drawn entirely from public on-chain data — no personal information required, no identity verification needed.

The profile contains two categories of information. The first is predictive data — forward-looking assessments of what the wallet address is likely to do in the future. This includes: fraud probability (will this address engage in fraudulent behavior?), rug pull association risk, future behavioral intentions (is this wallet likely to borrow, lend, trade, use leverage, collect NFTs, play games?), and risk willingness (does this person accept high risk or prefer conservative positions?). These predictions derive from the same AI models that power ChainAware’s fraud detection, which achieves 98% accuracy in predicting fraud before it occurs.

Descriptive and Forensic Data

The second category is descriptive or forensic data — a historical record of observable on-chain behavior. This includes: experience level (how long has this address been active, how many transactions, how diverse are its protocol interactions?), protocol categories used (DeFi, NFTs, gaming, centralized exchanges), transaction volume patterns, asset holding behavior, and which specific protocols the address has interacted with. Together, the predictive and descriptive components produce a complete behavioral identity profile — the on-chain equivalent of the credit and behavioral data that Web2 companies access through credit information systems.

All of this is calculated in real time, typically within seconds for most addresses. Additionally, the profile updates continuously as new transactions appear on-chain — so an address that was clean yesterday can show elevated risk signals today if new behavioral patterns emerge. For the complete guide to what the Wallet Auditor reveals, see our behavioral user analytics guide and our Fraud Detector guide.

Share My Wallet Audit: C2C Trust Without KYC

The most innovative feature in ChainAware’s wallet audit product is Share My Wallet Audit — a mechanism that creates cryptographically proven, shareable trust credentials without requiring any personal identification. This feature solves the C2C (consumer-to-consumer) trust problem that Web2 trust infrastructure never adequately addressed.

The process works as follows: a wallet owner connects their wallet to ChainAware and signs a message with their private key. This signing proves cryptographically that they control the wallet — the same proof-of-ownership mechanism used in every blockchain transaction. ChainAware then generates a unique shareable link that displays the complete wallet audit for that address. Anyone who receives this link can view the full behavioral profile without the wallet owner needing to reveal their identity.

Why This Is More Powerful Than KYC

Tarmo identifies why this combination — cryptographic proof of wallet ownership plus AI-generated behavioral profile — is actually more useful than traditional KYC for the decisions that matter in Web3: “Actually we don’t need KYC here. What we need is behavioral profile of the key owner. And this is what is Wallet Audit. If you do KYC, it is actually what you are interested in — how the user behaves. You are not really interested in their exact KYC. You are interested in their behavior.” The question KYC tries to answer is “who are you?” The question that actually determines whether to transact is “can I trust you, and how will you behave?” The wallet audit answers the relevant question directly.

Furthermore, the Share My Wallet feature solves trust combinations that Web2 systems never addressed at the individual level. Web2 provides B2B and B2C trust infrastructure — businesses can access credit information about other businesses or about individual customers. However, C2C trust — individual-to-individual trust between private parties — is essentially absent from Web2’s trust infrastructure. Martin notes: “We have C2C trust, C2B trust, B2C trust — trust in all these B and C combinations. And we offer this in ChainAware for free.” For the complete walkthrough of how to use this feature, see our behavioral analytics guide.

Your Blockchain History as Your Personal Brand

Martin introduces a framing that extends the wallet audit concept from a security tool to a broader identity infrastructure: your blockchain history is your personal brand. This is not a metaphor — it is a precise description of what the wallet audit enables.

In Web2, personal and professional reputation is built and maintained through LinkedIn profiles, portfolio websites, email history, published work, and social media presence. These signals help others assess whether someone is reliable, competent, and trustworthy before engaging with them. For businesses, credit ratings, registration records, and trading history serve similar functions. All of these mechanisms share a common structure: they aggregate behavioral and outcome data over time and make it available for others to evaluate.

Blockchain transaction history does the same thing — but with higher data quality, greater permanence, and stronger verification. As Martin describes: “If you want to make some deals — C2C, C2B — it’s your personal brand. You say, hey, here is my blockchain history, here is my card. You want to deal with me or don’t you want to be with me? Freedom of contract. Freedom of choice.” The longer and more substantive someone’s on-chain history, the richer and more trustworthy their behavioral profile becomes. A wallet with three years of active DeFi participation, consistent repayment history, and diverse protocol usage carries a genuinely valuable credential — one that no amount of fake verification can replicate, because the underlying transaction data is immutable.

Furthermore, this creates a direct incentive structure that Web2 reputation systems often lack. Because the blockchain history is public and permanent, bad behavior has lasting consequences on the actor’s own reputation — not just on their victims. Each scam attempt that gets recorded on-chain deteriorates the scammer’s own behavioral profile, making future interactions more difficult. For more on how this connects to ChainAware’s broader product vision, see our DeFi credit score comparison and our Web3 credit scoring guide.

Countermeasure Dynamics: Why the Asymmetry Favours Honest Actors

Scammers will not simply accept the imposition of behavioral accountability. Martin directly addresses the obvious counter-argument: won’t they just create new addresses? Yes — and this is precisely why the countermeasure is effective.

When an address exhibits fraud patterns and gets flagged, the scammer faces a specific cost: they must abandon that address with its accumulated transaction history and start fresh with a new address that has zero history. A new address is immediately suspicious in any context where behavioral credibility matters. If someone sends you a wallet address for a business proposal and that address shows two transactions, the appropriate response is straightforward: ask for the real address. A legitimate service provider has a real address with a real history. The requirement to demonstrate behavioral history is the countermeasure — not a perfect one, but a significant friction that raises the cost of fraud.

Clustering Technology as the Second Countermeasure

Martin identifies a second technical countermeasure that addresses the new-address evasion strategy: address clustering. Clustering algorithms track the flow of funds between addresses — deposits, withdrawals, and patterns of fund movement — and identify clusters of addresses that are likely controlled by the same entity. A scammer who creates ten new addresses but moves funds between them in recognisable patterns can be identified as the same actor regardless of which address they are currently using. As Martin explains: “Even these countermeasures could be implemented against this new address switching to another address or building up addresses — with the clustering, you will find out even more.” Combined with AI model retraining on newly identified scam patterns (which goes from confirmed scam events directly into training data for the next model version), the adversary faces a continuously improving detection system that becomes more expensive to evade over time. For how this connects to ChainAware’s fraud detection methodology, see our Fraud Detector complete guide.

The Twitter Community Notes Parallel

Martin draws an illuminating parallel to Twitter’s Community Notes feature — a mechanism that adds factual context to misleading tweets through crowd-sourced verification. The parallel illustrates a broader principle: every successful large-scale communication platform eventually develops a feedback and verification system to counter the norm-collapsing effects of anonymous participation.

Twitter allows anyone to post anything — free speech, no prior verification. This creates enormous value but also creates exploitation opportunities for bad actors. Community Notes provides a partial countermeasure: users with sufficiently diverse ideological backgrounds can collaboratively annotate misleading posts with factual corrections, creating a visible public record of the dispute. The notes do not prevent bad behavior, but they create feedback — visible social accountability that shifts the incentive calculation for bad actors who value their reach and credibility on the platform.

Blockchain needs an equivalent — a feedback system that makes behavioral history visible and consequential without requiring identity disclosure. ChainAware’s wallet audit is that system. As Tarmo summarises: “In every system that you are creating where we are dealing with social psychology, where we’re dealing with game theory — you need some feedback system, you need a verification system. And so in blockchain so far it’s not there. And what we are saying now with ChainAware wallet auditor, it’s there.” For the broader context on how trust infrastructure connects to Web3 growth, see our guide on why AI agents will accelerate Web3.

Comparison Tables

Web2 Trust Mechanisms vs AI Wallet Audit

Property	Web2 Trust (Credit Scores, KYC)	AI Wallet Audit (ChainAware)
Data source	Identity documents, credit history, bank records	Public on-chain transaction history — tamper-proof
Predicts future behavior	Partially — credit scores are backward-looking	Yes — 98% fraud prediction accuracy, forward-looking
Requires personal identity	Yes — KYC, real name, address	No — wallet address + cryptographic signature only
Accessible to individuals	No — only businesses can run credit checks on others	Yes — free for all users, any address
C2C trust	Not provided	Fully supported via Share My Wallet Audit
Can be falsified	Yes — fake documents, synthetic identity fraud	No — on-chain data is immutable and public
Updates in real time	Slowly — credit scores update monthly	Yes — recalculates on every new transaction
Reveals behavioral intentions	No — only financial capacity	Yes — borrowing, trading, lending, gaming, risk profile
Free for individuals	No — paid service	Yes — free to check any address
Preserves anonymity	No — requires identity disclosure	Yes — behavioral profile without personal data

KYC / Permissioned Blockchains vs AI Wallet Audit

Property	Permissioned Blockchain + KYC	AI Wallet Audit (ChainAware)
User adoption	Low — users avoid innovation-restricting platforms	High — works on any public blockchain
Document integrity	Weak — falsified documents bypass KYC	Strong — transaction history cannot be falsified
Predicts future fraud	No — only verifies current identity	Yes — 98% accuracy, predicts before fraud occurs
Protects against new addresses	No — new identity = new KYC	Partially — clustering + new-address suspicion signals
Preserves decentralisation	No — central authority controls access	Yes — fully permissionless, public data
Cost to implement	High — legal compliance, identity verification infrastructure	Low — free pixel integration, 2 minutes setup
Self-improving over time	No — static rules	Yes — AI models retrain on new fraud patterns

Frequently Asked Questions

What are the two types of trust in blockchain?

The first type is consensus algorithmic trust — whether blockchain transactions are valid and unaltered, maintained by proof-of-work or proof-of-stake mechanisms. This is solved. The second type is human-to-human trust — whether the person or entity behind a wallet address is honest, reliable, and safe to transact with. This is not solved by blockchain protocols and requires a separate trust infrastructure layer. ChainAware’s wallet audit addresses the second type. For more on this distinction, see our Web3 transaction monitoring guide.

Why does anonymity lead to fraud in blockchain specifically?