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Etherscan has introduced an advanced filtering tool that allows users to better customize and refine their search results.

The “Advanced Filter” tool, currently in beta, permits users to extract data insights and conduct focused analysis by utilizing a set of criteria using public data from Ethereum

With the new filter, users can easily narrow down their search results based on specific criteria such as transaction type, function name, duration, amount, and assets to and from addresses.

This helps users focus on transactions, addresses, and activities most relevant to their research, monitoring, or investigative needs.

Per Etherscan, the Advanced Filter tool can help monitor non-fungible token (NFT) lending activities on platforms like Blur. By utilizing the filter fields, analysts can effortlessly monitor the top NFT collections used as loan collateral.

Additionally, this tool provides information on borrowing transactions, popular NFT collections used in NFT lending, and the demand for loans backed by NFTs.

The filter is still in beta and has several limitations. For example, it does not offer the option to hide tokens with poor reputations, and there is no ignore list available at this time.

Crypto sleuths welcome new tool

On-chain crypto investigators and data analysts on Twitter have reacted positively to the new development, with many stating it will make tracing the movement of funds involved in scams and hacks easier.

Some suggested adding features such as putting public name tags of addresses to Etherscan’s API to lessen the need for on-chain detectives to scrape the website.

The tool comes when the crypto sector grapples with hacks and scams.

In the first week of April alone, the blockchain security firm, CertiK, reported 27 incidents involving hacks and phishing attacks on crypto platforms, resulting in the loss of more than $52 million.

May was a much slower month, with data from on-chain analytics service Beosin EagleEye indicating only $19.6 million was lost. It marked a 79% drop from the previous month.  

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