According to a report published by the research arm of financial messaging service SWIFT, industry companies need to re-imagine their role in post-trade processing.
The research paper found that blockchain or mutual distributed ledgers could significantly reduce the USD 40 billion per year spent in post-trade processing of securities around the world. But the report also highlighted the danger of unrealistic expectations as new technologies like blockchain are explored.
The reports authors argue that blockchain can be expected to replace the safeguarding function of third parties in the post-trade environment, an arrangement that seeks to prevent the proliferation of duplicate and fraudulent transactions.
The technology is also being positioned as a means to replace the current means of cataloguing transaction histories and dispute resolution applications, according to the report.
But a third function of a trusted third party in this context – the act of confirming or validating the existence of a thing being traded – cannot be replaced by blockchain, the report goes on to say.
In the current securities transactions framework, the responsibilities and access of various parties are understood by the participants, including institutional investors, asset managers, custodian banks, brokers, hedge funds, central counterparties and central securities depositories.
The shift from third parties that possess a combination of trusted documentation and institutional knowledge to a mutual distributed ledger could prove problematic because the semantics behind how third parties choose to use available services and data is never fully expressed in processing logic.
According to the report, the interconnected nature of the securities trade ecosystem means that altering any single part of the workflow will impact many other areas, not all of which stand to benefit from a blockchain. One example given in the report is syndicated lending, which can take 20 days or more due to legal complications not affected by the method of recording ownership data.
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