The conference at Money2020 has many tracks. Given the amount of questions I have seen from customers around distributed ledger technology (DLT) (Blockchain, Hyperledger Fabric, Ethereum, Corda, etc.), I attended that track. In the track, there were a fair number of panels staffed by either users of DLT, consortiums looking to make their use cases more ubiquitous, and implementers of DLT. While all groups had different lenses on what is going on, they tended to agree on what problems DLT solves and how to use the technology.
For finance, DLT helps eliminate a lot of verification/validation work with happens in the middle and back office.
This has been framed as the “Do you see what I see?” (DYSWIS diss-wiss) problem. DYSWIS solution attempts from the past have involved things like cryptographic signatures where two parties compare signatures of the data. Those solutions fail for the main reason crypto comparison always fails: normalization of data prior to signing. DLT solutions solve this in different ways, but they all have ways to sign facts and achieve consensus about the facts.
Anyhow, back to the main point about saving time confirming that what I see matches what you see. The finance industry has used a strong central authority for smaller, contract-less transactions in the form of Visa, Mastercard, and others. Then, we get to more complex transactions. How complex? Consider this scenario:
A European importer purchases some goods from an East African exporter. The importer prefers to pay when goods arrive. The exporter prefers to be paid when goods are shipped. Neither gets their preferred mode because of risk. The bank for the importer needs to issue a letter of credit. The exporter also insures the goods until delivery. The shipper, sitting between importer and exporter, will orchestrate the movement of the goods through several partners, who in turn may use other partners. Finally, the exporter will insure the goods in case of loss. It is normal for the shipment to pass through around 30 entities and have around 200 transactions [info from a presentation by TradeIX.com].
How does DLT help here? Using DLT, the importer, exporter, bank, shipper, and insurers can all see what is happening in real time (within minutes). Because facts are attested to and sent digitally, human transcription errors disappear. This means that humans may only to verify that the numbers and such look “right” before allowing their end of a transaction to proceed. This frees up human capital to do more valuable tasks.
So, one question you might ask yourself is “which DLT is right for me?” More than a few of the C-level folks on panels said a variant of “I don’t care. I just want something that works.” For those of you that care about the details and optimal choices, understand this: if you are joining a DLT consortium and it doesn’t use what you consider to be best, you need to just build something that works with the choice. If you complicate things by creating translation layers between something like Corda and Ethereum, expect to be looking for a new job tomorrow (because you’ve been fired).
The great news here is that the businesses now understand how to apply DLT. They have found that their normal transaction volumes of 200 TPS are already handled by most enterprise DLT solutions. They also understand the difference between on-chain and off-chain data, so don’t put PII and other GDPR prohibited data on the chain.
Over and over again, I heard the C-level folks say “I want DLT for the use cases where I spend a lot of time verifying that data was input correctly because that work costs too much time and slows down the business.” Then, using those facts, they want to drive cost savings elsewhere. The instant verification of the truth reduces financial risk. The reduced financial risk means the business can now make decisions sooner to further improve their ability to move money, settle accounts, and so on.
In 2018 you will hear a number of implementations of DLT in a number of markets. At this time, it seems prudent to be familiar with the leading contenders in the space. At the moment, these seem to be (in no particular order):
This will be an exciting year for DLT.
Karmic Code Monkey 