With all the investment and experimentation financial institutions are devoting to blockchain technology, research organizations are getting a better idea…
With all the investment and experimentation financial institutions are devoting to blockchain technology, research organizations are getting a better idea of what the future financial services infrastructure will look like. Deloitte Consulting LLP and the World Economic Forum have taken a pragmatic approach to determining how blockchain, or distributed ledger technology (DLT) will help financial firms.
A recent collaborative study [PDF], “The future of financial infrastructure; An ambitious look at how blockchain can reshape financial services,” explores how DLT can change processes like liquidity management, regulatory compliance and internal/external reconciliation. Authors are Bob Contri, global financial services industry leader at Deloitte, and Rob Galaski, leader of the banking and securities practice at Deloitte.
DLT has seen more than $1.4 billion invested in three years and 2,500 patent filings. It is predicted 80% of banks will initiate projects by next year.
While DLT offers promise, it is not a panacea, but a tool, the study noted. It can shape the foundation of the financial service infrastructure in tandem with other tools like cloud analytics, cognitive computing and robotics.
But challenges remain. The regulatory environment is uncertain. Standards are only starting to develop. Legal frameworks don’t exist.
Updating the financial infrastructure through DLT will take major time and investment.
Applicable financial sectors include payments, insurance, deposits and lending, market provisioning, capital raising, investment management.
DLT is a technology that allows parties to transfer assets to each other in a way they can trust without requiring intermediaries. It enables transparency, immutable records and autonomous execution of business rules.
DLT makes it easier to verify that partners complied with their obligations. Dispute processes become easier as audit trails are readily available.
Other DLT benefits include: capabilities like digital identity are available for use with DLT; DLT solutions can scale; and data sources that DLT make accessible cannot be compromised.
Institutions can reconcile records and resolve disputes among themselves. Lenders gain greater visibility of assets pledged by borrowers.
DLT brings efficiency and simplicity. It removes much labor from reconciliation and dispute resolution. It also reduces locked-in capital.
DLT benefits depend on in the business problem. For compliance, DLT provides more accurate and faster reporting through automation, drawing on immutable data sources.
Digital identity, digital fiat and other capabilities can broaden DLT’s appeal. A digital system can integrate with a DLT-based infrastructure to verify counterparties and customers are who they say they are. Benefits include more accurate and faster AML and KYC processes.
It is one of many technologies to remake the financial services infrastructure. Change is coming from various converging technologies, including robotics, machine learning, biometrics, and cognitive, quantum and cloud computing.
The most critical DLT applications require industry collaboration, the study noted. Replacing existing financial infrastructure is a big undertaking, delaying DLT implementations in highly regulated markets.
Contract execution is currently laden with bureaucracy, based on the assumption that counterparties can’t be trusted. DLT infrastructure provides a level of autonomy making trust unneeded.
DLT will upend today’s business models. The immutability of DLT infrastructure removes information silos so everyone uses the same facts.
Following are key financial service industry use cases.
Global payment transfer fees are costly. By the second quarter of 2016, the average was 7.6% of the remitted amount. With DLT, the sender’s digital identity profile verifies identity. A smart contract containing the remittance data delivers the funds to the beneficiaries’ institution while notifying the regulator. The liquidity providers on the ledger take care of the currency conversion.
DLT can make small payments more affordable by removing much of the labor of the process.
DLT-automated claims processing can reduce fraud and improve assessment through historical claims data. Loss and claims processing are a source of friction, making up 11% of insurers’ written premiums.
DLT can streamline claim submission process by using smart contracts or smart assets. Business rules encoded in a smart contract relieves loss adjusters from having to review each claim.
Syndicated loans allow institutions to spread the risk of a single customer borrowing a large amount. Such loans are a significant volume; in 2015, it was $1.8 trillion in the U.S. The market could open up to more players if back-office operations were simpler. Selecting members is labor intensive, as is qualifying borrowers. Verifying settlement funds requires investors to wait up to three days to get their money.
DLT record keeping can simplify the process. The book runner uses the borrower’s digital identity to do KYC work, and investors’ digital identities to identify those with proper capital and risk tolerance. Smart contracts do the due diligence and automate portions of underwriting and credit adjudication.
Trade finance fills the gap between exporters who need payment guarantee before they ship and importers who have to know the goods they paid to get delivered.
DLT can improve import/export efficiency by providing streamlined access to trade documents, faster settlement and greater capital efficiency.
About $18 trillion in annual trade involves some form of finance; guarantee, insurance or credit. The processes are lengthy. The import bank has to review the agreement from the importer and send information to the correspondent bank. The export bank must do AML checks using the import bank’s financials.
With DLT, the purchase agreement between buyer and seller is codified as a smart contract that autonomously executes the terms of the agreement. Documents on the ledger allow parties to do due diligence for credit adjudication, trace the location of goods and check for AML.
A contingent convertible bond is a hybrid security that combines features of debt and equity. Investors earn dividends until the principal is repaid, but once a threshold is crossed, the bond converts to equity, saving issuers the cost of the remaining coupons and having to repay the security. The threshold could be a bank capital ratio falling below a certain percent.
The market for these bonds is largely untested and the instruments are volatile. DLT can address these issues by embedding regulation into the business process. When a bank issues a bond, it creates a token with the information about the loan. When the bank updates its capital ratio, the result becomes part of the tokenized record. If the capital ratio crosses the conversion threshold, a smart contract advises the regulators and bank management.
By making financial data available to auditors, institutions can remove error-prone manual work and reduce costs and strengthen trust in their financial condition.
Examiners can use DLT to access information needed to do an audit. After completing the report, auditors store it on the ledger for review. A smart contract then moves information from the report to reporting instruments.
DLT, as a way to distribute proxy statements and count votes, could improve retail investor participation and automate vote validation and allow personalized analysis. When someone buys shares in a company, the transactions are recorded in the ledger.
When the company completes the proxy statement, a smart contract notifies the owner and regulator. The shareholder’s vote appears as a tokenized asset on the ledger. Another smart contract matches the votes with the ownership record to determine validity.
Asset rehypothecation is when an institution uses collateral posted by borrowers to cover its own trades, also known as secondary trading. It is difficult to manage. If the institution mixes up who owns the asset, the trading partners’ risk increases. Hence, regulators limit the extent to which institutions can re-pledge an asset. But enforcement is not possible without a way to track the transaction history.
DLT could mitigate the risk surrounding rehypothecation by creating an immutable record of the asset’s transaction history. When an asset trades, a smart contract broadcasts the transaction information. If an asset reaches a regulatory rehypothecation limit, trading stops.
Equity post-trade processing allows seller and buyer to exchange trade information and change the ownership record and assets. The process starts once the exchange confirms the trade has taken place.
Applying DLT and smart contracts to post-trade activity can remove the need for intermediaries, minimize operational risk and provide a faster settlement.
A central securities depository works with custodian banks to match trades and validate investor credentials.
The central counterparty clearing house transfers the securities to the proper custodians, who store the assets in accounts. The process takes one to three days.
DLT could alleviate the process by assuming many of the intermediary functions. Once the exchange confirms a trade, a custodian bank sends details of its part of the transaction to the ledger. A smart contract then validates the data and matches it with the other parts of the trade. Once assets are stored for safekeeping, smart contracts initiate servicing processes while notifying custodians and investors in real time.
The different use cases covered in the report all include a shared repository and multiple writers to the repository. They also involve eliminating one or more intermediaries from the value chain.
The industry must work with business leaders to determine areas where DLT can present material gains or increase risks of disintermediation.
Business use cases can be developed for successful experiments. From these experiments, it may be possible to create a plan to commercialize the solutions and identify barriers to scaling.
Images from Shutterstock.
Last modified (UTC): November 13, 2016 12:11 PM