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OpenRisk Semantic Blockchain Explained

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Introducing the Semantic Blockchain in capital markets

Technological advances have made a “Semantic Blockchain” feasible. This is the ability to map smart contracts on the blockchain to qualitative information about the underlying data. It’s a realistic solution to complex problems of automation and operational efficiency.

While creating a smart contract that embeds data sounds straightforward on paper, the reality is that the mechanics are highly complex. An effective smart contract needs information that connects it to context, allowing the end user and intermediaries to know who, what, where, when and why the contract will make or lose money. This robust understanding of context is at the heart of investments and risk management; even algorithmic trading computers are working from data that supply the context.

But we are not at the point where smart contracts can live up to their promise. The mechanics of smart contracts do not yet have the underlying backbone of information – the Semantic Web – that allows investors to capture knowledge about a contract in a reasonable span of time to make an investment decision. This is the next big leap forward, and with it come a number of important outcomes for capital markets

Understanding the Semantic Web
The Semantic Web, also called Web 3.0, can be thought of as a common language: terminology we can all agree on to then engage in business. It is the mapping and definitions of key concepts and details in context. For example, a user can make a Google search for ice and transportation, looking for a way to ship food. The search for ice will yield a set of results and transportation will yield other results. But by combining the two words together, the user receives more specific information for their needs. Now, consider taking dozens of words from a complex derivatives contract and looking to narrow down to what is most important for the search; this can be problematic.

I have a dream for a web [in which computers] become capable of analyzing all the data on the Web: the content, links and transactions between people and computers. A “Semantic Web”. – Tim Berners-Lee, Weaving the Web

In capital markets, a smart contract on the blockchain references a set of variables, each one with a rich and robust context of its own. A corn futures contract may contain information on delivery date, price, corn type and even the grower. A securities loan could reference the credit information of the lender, the term, and pre- and post-trade transparency. Each variable can be digested, packaged together, or can be unbundled for understanding risk and reward across exposures in a detailed context.

As another example of context, Standard & Poor’s has its own set of ratings, which is different than Moody’s or Fitch. A search for a short bond with an A-1 rating must by inference be an S&P rating. It cannot be Moody’s which would have a P-1 rating, or Fitch, which would have an F1 rating. By entering in A-1, a computer can understand that the user is referencing S&P based on the context of the search.

Conducting a Google search for A-1 is easy, but no data sets for bespoke derivatives contracts and peripheral market data to supplement it exist online. Certainly none that cross a wide range of margin, credit risk and bond holding information uniformly, that can then be tied to a trading price for a specific client at 2PM on a Thursday afternoon. This would require much more information, and that is where the Semantic Web comes in.

Industry organizations and language standardization
Two industry initiatives have already taken steps towards creating a framework for capital markets. ISDA’s Common Domain Model (CDM) seeks to provide standardized models for derivatives products to be used on smart contracts on the blockchain. According to ISDA, “The ISDA CDM is intended to provide an industry standard blueprint for how derivatives are traded and managed across the lifecycle, and how each step in the process can be represented in an efficient, standardized fashion. Establishing a common set of data and processing standards will facilitate interoperability between firms and technology platforms.”1ISDA is encouraging industry adoption of the CDM as a way to migrate paper and bespoke OTC derivatives technology platforms to a universal model.

The EDM Council has created the Financial Industry Business Ontology (FIBO) to standardize what words actually mean in finance. A brief review of language including blockchain, distributed ledger, artificial intelligence, machine learning and deductive reasoning systems can all yield overlapping results; FIBO sets out to solve this problem. As the Council notes, “Think of FIBO as a ‘Rosetta stone’ to help users create and explore connections and relationships between instruments, business entities and counterparties to determine expected cash flow, unravel risk and identify transitive relationships that were not previously possible.”2

By weaving together derivatives models for how smart contracts should operate with language that standardizes terms for machine reading, the building blocks come together for the Semantic Blockchain. This is a long-term project and we are just at the beginning. The leadership and support of industry associations is critical in this effort.

OpenRisk is building a market utility for ISDA contract rationalization
When looking at the potential for the Semantic Web in capital markets, the big question is where will all this reference data be held? It is great to say that a smart contract will map back to information that can be used in a firm’s analysis, but the information must reside somewhere.

At OpenRisk, we have taken on the challenge of building a mapping reference tool for holistic data management in derivatives risk management. We are creating a market utility that can be used by any smart contract user or developer; this is a golden source of data for any financial contract. Put another way, we are adding the product-specific elements of the Semantic Web for use by capital markets industry professionals.

We are focusing initially on rationalization of ISDA CSAs – a Semantic Smart Contract. A smart contract that emphasizes the meaning of legal and operational terms in a CSA, with embedded margin rules, can be negotiated and managed on the blockchain. End users will know the asset class, counterparty and even national regulations and Basel Committee/IOSCO associated rules. This is the first iteration of the Semantic Blockchain that will allow smart contract automation.

To deepen the data, we are creating an open interface to help with semantic mapping. Many smart contracts, just like derivatives contracts in paper form, have their own variations. A user interface allows individuals and firms to upload existing executed CSAs, which will automatically be rationalized, correlated with standardized meanings of contractual covenants, correlated with firm specific terminologies, and finally fine-tuned for manually curating any gaps that machines could not identify. The process is equally seamless for negotiating a new CSA and tying back to trades and peripheral market data required for day-to-day business operations.

Later, instead of a firm having to hunt down bespoke information from the originators of an OTC derivative, the data can be referenced using our utility. An added advantage of a Semantic Smart Contract is that it becomes a golden source for internal business operations, external communications to counterparties, regulators, other market participants and utilities.

The creation of an industry utility for Semantic Blockchain data is one piece of infrastructure that is recreating how capital markets work. Reference data must be read by someone or something of course, and we expect that machines will do the heavy lifting of utilizing reference data for risk management, legal analysis and business automation. After all, smart contracts may be smart, and artificial intelligence may be intelligent, but they both rely on good data to move from beta project to practical applications.

The future is only beginning for the Semantic Blockchain in capital markets, and market participants should expect substantial evolution in the next several years. OpenRisk is excited to be engaged in this process.

References:

“ISDA Publishes Digital Iteration of the Common Domain Model,” ISDA, June 5, 2018, available at https://www.isda.org/2018/06/05/isda-publishes-digital-iteration-of-the-common-domain-model/
“About FIBO,” EDM Council, available at https://edmcouncil.org/page/aboutfiboreview

Where AI and Post-Trade Meet

Artificial Intelligence (AI) is a technology that is rapidly becoming relevant in the post-trade financial services space. AI is a branch of computer science dealing with the simulation of intelligent behavior in computers. Operationally it’s taken as the capability of a machine to imitate intelligent human behaviors or human behavior patterns.

Intelligence is only as good as the underlying data, which is only fully usable when it is standardized in a market context. In the derivatives market, there is a large knowledge base, but in order to apply algorithms to put it into context, analyze, derive patterns, and make intelligent decisions, we need smart data across pre-trade and post-trade processes. This is where AI comes into picture; Convert your information into smart data and apply algorithms to drive intelligent business decisions.

Once you have smart data and algorithms to drive business operations decisions, the next question is how do you apply it to become tactically or strategically aligned, be regulatory compliant, and reduce your total cost of operations in both pre-trade analytics and post-trade processing? There are various tools and frameworks available in the market to address this but only a very few firms in capital markets are really taking advantage of AI techniques at this point. One prime example of such an initiative is FIBO (Financial Industry Business Ontology), an industry initiative developed by EDM Council members.

Common misconceptions about AI and FinTech
The biggest misconception is that people often think of AI as robots taking over everything that humans do. This is clearly not going to be the case. Robots will not replace humans completely, however machines will come to replace operations on Wall Street. This is only a matter of time.

A very common misconception about FinTech is that people think of it as being just about B2C and P2P payments. AI will drive the ability for exotic derivatives—currently only understood by investment bankers—to be made available to the common man as an investor. That gap can only be bridged when FinTech is viewed holistically as both B2B and B2C.

Market drivers for increased use of AI
The biggest driver is regulatory compliance. The capital markets overhaul is far from finished. Firms need to leverage sustainable, sophisticated technology, and AI is their best option from a proactive and strategic standpoint. In Collateral Management context, creating smart data models to capture bespoke nature of legal contracts and processes around it is by far the best solution for an ever-changing regulatory landscape.

The other major driver is the need to save costs. Recent paper undertaken by Pricewaterhouse Coopers (PwC) for DTCC-Euroclear Global Collateral Ltd shows the annual cost of operations and technology infrastructure for collateral management operations for a sell-side bank was about $7 million in 2015, and it is expected to grow to $27 million by 2020. This is an exponential increase in the amount of money market participants will spend. How do they control that? The only answer is advanced technology such as AI. Research undertaken by Morgan Stanley | Oliver Wyman indicates the wholesale banking industry can save $15-20billion if deep digital adoption with robotics and automation is embraced.

In the derivatives industry as a whole, the cost per transaction is very high, partly because counterparties don’t take a holistic approach in their communications. The only real way to reduce these costs is to have a sophisticated protocol, such as RDF and OWL, enabling market participants to communicate with each other directly. Imagine collateral management systems are speaking different languages, and they need the utility to serve as their interpreter.

Using AI helps participants reduce a lot of inefficiencies, enabling them, for example, to perform trade reconciliation before margin operations. This is seldom practiced by the market, but would make a lot of sense to do it in that order, and AI techniques can enable that.
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Where AI can be applied? Hint – collateral management
Payments is one of the more developed areas within FinTech and settlement management is a classic use case for both blockchain and AI.
Another application is tracking the changes to regulatory requirements, which is a constantly moving target. The rollout of variation margin requirements in March under BCBS/IOSCO was chaotic because both the market and the regulators were underprepared. The only way to advance these integral plans is to deploy sophisticated technology like AI and natural language processing, which means ISDA master agreements and credit support annex contracts (CSAs) need to be rationalized and digitized. Only approx. 5% of contracts in the market were amended for March 2017 deadline. Firms will be able to reduce a lot of operational overheads if they prioritize this shift.

Margin call processing and dispute resolution are another good use case for AI in the OTC derivatives space. Regulations are driving an explosion in the number of margin events, and disputes will be common. Firms could plausibly teach a machine the mundane tasks a manager performs to resolve them, saving firms large amounts of time, money and energy.

The application of innovative technology and including AI towards collateral management operations is particularly timely given the rapid evolution of this space as collateral moves from a back-office function more upstream to the middle and front offices. For instance, coupling up post-trade processing and pre-trade analytics is becoming a big focus for market participants. With massive data volumes, participants need to make very fast and accurate decisions. The front and back office need to come together, and there are already a lot of initiatives to address that. There is an argument that there will not even be a middle office a couple of years from now. As they begin to leverage AI technology, market participants need to break down the silos between front, middle or back office, and between different derivatives products.

Looking forward
The next couple of years will see significant growth in the number of concrete implementations of AI. Globally, 62% of the $544 trillion notional outstanding reported by dealer is centrally cleared. This provides a very ripe ground for applying AI techniques.
According to CB Insights, the top 22 FinTech companies were valued at $74 billion at the end of Q3 2016. FinTech innovations and investments are only bound to grow in coming years.

In the next few years, we will see a move towards more intelligent delineation and tracking of risk, so that we won’t again have a situation where a banking system collapses because various individuals are unable to pay their mortgages. Much has changed since 2009, but there hasn’t yet been enough effort to track that lineage and measure global financial risk. That is where the market is headed over the next few years.

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OpenRisk chosen to provide collateral management capabilities on new Blockchain eco-system created by CLS and IBM

Foreign exchange settlement institution [CLS Group](https://www.cls-group.com/) is collaborating with IBM to enable banks, buy- and sell-side firms, fintechs and software vendors to deploy, share and consume services using distributed ledger technology (DLT). The hosted platform, LedgerConnect, will allow access to services in areas such as, but not limited to, know-your-customer (KYC) and sanctions screening, collateral management, derivatives post-trade processing and reconciliation, and market data.

“By hosting these services on a single, enterprise-grade network and DLT infrastructure, organizations can focus on business objectives rather than building and operating the underlying infrastructure, enabling them to realize operational efficiencies and cost savings,” CLS said in a July 30 [announcement](https://www.cls-group.com/news/ecosystem-for-institutions-to-share-apps-and-services-on-a-common-dlt-network/).

One of the vendor participants, OpenRisk, is in proofs of concept with five global banks for its “semantic smart contracts” in derivatives collateral management, said founder and CEO Narasimha Kodihalli. It has been working with IBM and CLS for almost a year.

“We have a semantic approach to annotate and make sure there is no ambiguity and that both sides have the same understanding of the terms and legal structure in ISDA contracts,” Kodihalli explained.

“In any bank there are seven to 10 versions of the same executed contract. When you look at it from a counterparty perspective, on one single contract you have 10 to 20 different versions of that contract for different business purposes. What we are doing right now on the ledger is creating one golden source of the contract that can be used internally and externally.”

Of LedgerConnect, Kodihalli said, “Unlike previous efforts where customers created a silo solution, this will provide a broad solution. IBM is doing the platform, CLS does the policy, and vendors can provide their vetted solutions.”

[Full story over at GARP](https://www.garp.org/#!/risk-intelligence/technology/disruptive-technologies/a1Z1W000004B1sIUAS).

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OpenRisk Chosen As One of Twenty Most Promising FinTech Firms

CIOReview selected OpenRisk as one of their [twenty most promised banking technology solution providers in 2017](https://banking-insurance.cioreview.com/vendors/top-banking-technology-companies-2017.html).

As the year unfurls, it heralds a shedload of new bearings for the banking space. Standard technologies like artificial intelligence and robotic process automation are revived through convergence with new voice commerce models, IoT data, and robo-advisers to offer a more personalized and digitized banking experience for the end consumers. A relevant example is the Chatbot which, by unifying personalized attention and automation, is effecting the disruption of traditional customer service in the financial industry. Forward-thinking banks are already making headway toward ‘conversational commerce’—the trend of interfacing businesses through voice technology—by leveraging Chatbots.

Into the bargain, platformification is the new argot taking the banking sector by a storm. Open API will emerge as a trendsetter—provisioning open, unified, multi-channel integration—whereby it will empower banks to steer collaboration across all channels and partners. Through a mix of fresh and existing processes on API platforms, banks are on the move to kick off next-generation services that are crucial for drawing and retaining customers. In the same line, financial institutions are gravitating toward Blockchain experimentation— harnessing the technology in customer-oriented business processes—with a vision to procure a competitive edge on top of pricing benefits. Companies striving to mount higher in the banking value chain look for the best solution providers.

The market today abounds in a bevy of banking technology solution providers armed with state-of-the-art technologies that can help companies gain proactive ideas to tackle financial challenges. A distinguished panel comprising CEOs, CIOs, VCs, industry analysts, and CIOReview’s editorial board has reviewed the top banking technology solution providers and shortlisted the ones that are at the vanguard fulfilling the urgent demands of the industry.

We present to you CIOReview’s 20 Most Promising Banking Technology Solution Providers 2017.

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OpenRisk Selected for PlugAndPlay Spring 2018

OpenRisk Technologies was selected to participate in the [Spring 2018 round](https://www.plugandplaytechcenter.com/spring-2018/). PlugAndPlay is a 12-week accelerator program that runs twice a year connecting the best early-to-growth stage startups to the world’s largest financial institutions. Their goal is to facilitate the launch of pilots, POCs, and robust business development opportunities.

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OpenRisk Technologies Inc. Joins the Wells Fargo Startup Accelerator

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Wells Fargo & Company (NYSE:WFC) announced today that OpenRisk Technologies Inc. has joined the Wells Fargo Startup Accelerator, bringing the total number of companies in the portfolio to 21.

“Helping to advance innovative fintech startups plays an important role in our efforts to deliver the financial experiences of tomorrow that customers demand,” said Lisa Frazier, head of the Innovation Group at Wells Fargo. “We welcome OpenRisk to the Wells Fargo Startup Accelerator program and applaud the passion, agility and focus they bring to create innovative solutions and unique customer experiences. Innovation is critical within financial services, and we’re excited to invest in and collaborate with these companies, and all our accelerator companies, to reimagine financial services.”

The Wells Fargo Startup Accelerator program began in 2014 to advance emerging technologies in pursuit of breakthroughs for financial services. Startups are eligible to receive up to $1 million in funding, along with guidance from Wells Fargo business and technology leaders to help refine the launch of their ideas.

The virtual, six-month, non-exclusive program has received nearly 2,400 applications from innovative companies in 91 countries since the program’s inception. Applications can be submitted throughout the year at www.wellsfargo.com/accelerator.

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