The Role of On-Chain Data Analysis in Detecting Crypto Scams

Scam wallets typically show:

• Rapid, high-volume inflows from centralized exchanges or mixers

Scam wallets often receive large amounts of funds quickly, especially from centralized exchanges (CEX) or mixers (services that obfuscate transaction histories), making it harder to trace the source of funds.

• Circular transactions to obfuscate origin and ownership

This involves funds being moved between wallets in a loop to confuse tracing efforts. It hides the true source and destination of the funds, making it difficult for investigators to track illegal activity.

• Multi-hop laundering via bridges or layered transactions

This refers to using multiple platforms, such as cross-chain bridges, to transfer assets across different blockchains. It adds complexity, making it more challenging to follow the movement of illicit funds.

These behaviors are commonly seen in pig-butchering schemes (scams where victims are “fattened up” with promises of high returns before being swindled) or phishing theft (fraudulent attempts to steal information or funds via deceptive links or messages).

Chainalysis and TRM Labs report significant wallet anomalies tied to compromised seed phrases and address poisoning in early 2025.

On-chain tools can detect:

• Trapdoor tokens (Known az "Honeypot")

These are tokens that can be bought by users but cannot be sold. Often used in scams, these tokens are designed to trap users after they invest, making it impossible for them to recover their funds.

• Contracts with hidden owner-only withdrawal functions

Some smart contracts contain hidden functions that allow only the owner (the scammer) to withdraw funds. These functions are typically not visible to the users, making the scam harder to detect.

• Fake liquidity injections, quickly followed by drains

Scammers may create the illusion of a well-funded token by injecting liquidity, only to later drain it suddenly, leaving investors with worthless assets.

• High-frequency circular trades (indicative of wash trading)

This involves creating the illusion of trading activity by buying and selling the same asset between different accounts. Wash trading manipulates market prices and volume, often to mislead investors. 

Additional Examples

• 2025 arXiv study on NFTs: Analyzed 50,000 NFT contracts and found recurring rug-pull patterns. These included tokens without verified code (making it impossible to audit), large initial liquidity (which could be quickly withdrawn), and airdrop campaigns tied to manipulated tokenomics (token distribution strategies that were designed to artificially inflate demand).

• Ponzi schemes on Bitcoin: These are often exposed through network graph analysis, where investigators analyze the flow of transactions across the network. Ponzi schemes show a pattern of continuous inflows to certain addresses (typically high-tier nodes), but there’s no real product or service behind the flow, just a system of reinvesting the funds of new investors to pay earlier ones.

Each of these concepts can be detected using on-chain analysis, which allows for tracking suspicious behaviors, spotting unusual patterns, and uncovering potential scams before they can affect a large number of users.

Explorers like Etherscan, Tronscan, Elliptic, Arkham and Nansen provide granular visibility into:

• Wallet behaviour and address tagging

These features track how wallets behave over time, including identifying suspicious addresses that are tagged (e.g., flagged for involvement in scams or illicit activities), which helps analysts spot fraudulent patterns.

• Smart contract deployment and interaction logs

These logs show when and how smart contracts are deployed and interacted with on the blockchain. Analysts can see contract changes, token minting, and other activities that could indicate manipulation.

• Real-time token transfers and liquidity shifts

These tools let users monitor token movements and liquidity changes in real time. They can quickly detect sudden changes that might indicate suspicious activity, like large transactions or liquidity manipulation.

• Contract verification and proxy detection

Contract verification allows users to check if the code behind a smart contract is publicly available and verifiable. Proxy detection looks for contracts that might hide their true functionality, often used for malicious purposes like rug pulls. 

These explorers help forensic analysts and investors audit token mechanics and detect issues like unauthorized minting (creating tokens without permission) or liquidity lock manipulation (locking liquidity to deceive investors).

Modern platforms deploy AI and ML to automate detection:

• Chainalysis: This platform uses deep learning to build risk profiles for over 200 million wallets, categorizing them into over 150 types. The system automatically flags suspicious wallets, helping detect fraud, money laundering, and other illicit activities.

• Elliptic: It uses behavioral heuristics, or patterns of activity, to identify scams like deepfake-driven frauds (where fake identities are created using AI-generated media), address poisoning (deliberately sending funds to trap victims), and money laundering paths (tracking illicit fund transfers).

• TRM Labs: This platform specializes in anomaly detection by analyzing transaction behavior and sending real-time alerts for suspicious activities. It tracks cross-chain asset migration (the movement of assets between blockchains) and scam clusters, helping to identify coordinated fraudulent activities.

• Crystal Blockchain: This platform offers predictive intelligence to anticipate threats, such as phishing scams or attacks on smart contracts, by analyzing transaction flows and detecting emerging patterns of fraud.

All platforms incorporate transaction graphs, metadata enrichment, clustering models, and real-time alerting to identify threats early, sometimes within minutes of scam deployment.

As AML/KYC obligations increase, exchanges and DeFi projects are expected to implement on-chain analytics into their compliance stacks. According to the 2025 Chainalysis Crypto Crime Report, regulatory enforcement is driving transparency in:

• Stablecoin flows and CEX liquidity audits

• Token listing vetting

• Sanctions-screened wallet lists

Cross-jurisdictional collaboration, such as between U.S. FinCEN, FATF, and blockchain forensics providers, has helped de-anonymize scam networks and recover illicit funds through asset freezing and blacklisting.

Next-gen capabilities are making on-chain analytics more predictive and proactive:

• Cross-chain scam tracking across Ethereum, Solana, BNB Chain, Arbitrum, and even Cosmos zones

• Behavioural anomaly alerting, tuned by AI on liquidity flows, wallet activity bursts, and exploit pattern recognition

• Forensic dashboards enabling regulators, exchanges, and AML teams to track funds in real time

Ultimately, on-chain forensics is evolving from post-fraud investigation to live threat detection, marking a new era of security infrastructure for Web3.