ASIC appeals Block Earner case

By

The crypto platform could still be on the hook for penalties.

The Australian Securities and Investments Commission (ASIC) has appealed the federal court’s decision to dismiss cryptocurrency platform, Block Earner from its liability to pay penalties for its ‘Earner’ product.

ASIC appeals Block Earner case

On Tuesday, ASIC appealed to the federal court its decision stating in the notice of appeal the judge “erred” in reliving Block Earner from liability [pdf].

ASIC first ruled the fintech company engaged in unlicensed financial services conduct when offering its crypto-backed ‘Earner’ product back in February this year.

At the time, ASIC said back in February  “offered consumers the Earner product which allowed them to earn fixed yield returns from different crypto-assets” between March 2022 to November 2022.

Block Earner is the trading name of Web3 Ventures, which found that by early June the federal court released the company from penalty liability stating “it acted honestly and not carelessly despite finding the claims to be “serious”.

In response, Block Earner argued in the proceedings its products “did not constitute financial products having regard to the technical definitions of the Corporations Act” and the Earner product was closed in November 2022 with all customers were restored, including any interest owed.

ASIC’s appeal will be heard by the full federal court at a later date.

Got a news tip for our journalists? Share it with us anonymously here.
© Digital Nation
Tags:

Most Read Articles

King & Wood Mallesons Australia to give Gen AI tool to 1200 lawyers

King & Wood Mallesons Australia to give Gen AI tool to 1200 lawyers

Case study: Warren and Mahoney adopts digital tools to reduce its carbon footprint

Case study: Warren and Mahoney adopts digital tools to reduce its carbon footprint

Transport for NSW expands SAP Ariba usage

Transport for NSW expands SAP Ariba usage

ANZ continues work on data "one-stop-shop" for its Risk function

ANZ continues work on data "one-stop-shop" for its Risk function

Log In

  |  Forgot your password?