8 Trends for ’18
Terms like AI, blockchain, robotic process automation, neural networks and deep learning are now commonplace. Whether it’s to gain an edge on market condition triggers, create efficiencies, improve forecasts, or engage customers, it’s clear that firms are ready, willing and committed to exploring these technologies in the hope of enhancing their enterprise IP.
However, we need to ensure the underlying architectures and the data that will feed them are up to the task. What’s more, the infrastructure to support causal reasoning and machine learning tools is vastly different. Add to that the fact that this landscape is moving so quickly, and the challenge to stay current seems insurmountable.
There is no simple answer to any of this. However, we do know that companies will need to use multiple engines to support the range of AI tools and use the cloud to increase their flexibility and the speed with which they bring new products and services online. And they will need to focus on data, how they bring first and third party together so they can deliver more meaningful services and ultimately grow revenue and increase profitability in 2018.
2018 is likely to see further opinion from across the regulatory spectrum on the risks posed by new technology. The Financial Stability Board (FSB) stated in its first report on artificial intelligence (AI) and machine learning (ML) that the risks they pose need monitoring. Certainly, this is true, but it only gets at the surface of the three real needs of regulators driven by the digitalisation of intelligence within financial markets: Explainability, Accountability, and Action.
It is important to note that all of these systems are implemented by humans. While this has the potential to reinforce or shield the impact of human bias, it also gives an opportunity for us to examine current rules and regulations for existing implicit bias. If collective energy can be focused on methods of understanding, structures of understood accountability, and an aggressively active engagement on the part of regulators then there is an opportunity to drive deeper efficiency in almost all aspects of financial market systems while avoiding the pitfalls of reckless exuberance.
As streaming data becomes more accessible, firms will have the ability to create more powerful analytics with the likes of data visualization tools and predictive analytics through artificial intelligence and machine learning.
A crucial enabler of these emerging technologies is the public cloud. While some firms have been reticent to adopt public cloud due to security and reliability concerns but uptake will accelerate in 2018 as firms feel more comfortable with core applications in cloud.
Early adopters have a time-to-market advantage but others could easily catch up as they consider and assess which approaches align best with their business goals. And with the cloud in place across the organization, the door will be wide open for future innovations in the next twelve months and beyond.
RPA has grabbed attention as a potential solution for the management of repetitive processes that is cost-effective, efficient, productive and scalable. Where the application of RPA becomes very interesting, however, is when it is combined with more advanced, cognitive tools to deliver intelligent process automation, i.e., robotic automation that not only processes pre-programmed functions, but also makes autonomous decisions using a sophisticated rules engine based on learning algorithms.
Suresh Kandula, senior director, Sapient Consulting
There have been many successful Blockchain proof of concepts and 2018 will continue to see a shift from technology validation mode to enterprise features, scale, and commercialization. The work being done by smaller consortiums (Goldman/ JPMC/Axoni), consortiums like Digital Asset Holdings and R3, platform providers like Ripple, Chain, Symbiont, and intermediaries like DTCC, Nasdaq and others shows significant promise and over the next year more interesting work will undoubtedly take place.
2018 will be a transition year towards enterprise and industry scale platforms in both capital markets and commodities sectors. The Open Source alliances such as Hyperledger and Ethereum
There is a gap in procuring LEIs for each counterparty and client that a firm deals with. Post MiFID-II, the rule is going to be ‘No LEI, No Trade’. Although the registration of new LEIs has gone up substantially in recent months to around 800,000, it is still some way short of the conservative estimates of the total requirement of 1.5 million. It is likely that in the short term some firms will restrict their trading with parties without it, thereby forcing all firms to procure a LEI.
There also may be a short term drop in the number of OTC trades as not all firms may have established solutions for procurement of ISIN from ANNA before the go live. Firms can rely on manual workarounds till the time such automated flows are in place, however, the operational overhead for firms will increase in the short run.
Trade data reconciliation was already a challenging task under previous regimes. New requirements under MiFID II will add to the complexity. For MiFID II, it’s not only about processing a large amount of data but also scenarios such as delegated reporting, applying different reporting rules for different assetclasses, accounting the buy-side and sell-side counterparties after swapping party identifies and leg fields for dual reporting requirements and real-time reconciliation for executed and outstanding orders.
The significant amount of diversified data generated for transaction reporting, coupled with regulatory requirements such as front-to-back reconciliation, real-time reconciliation and ISIN, CFI and PII updates will stress and strain existing systems. In 2018 it will be crucial for banks and investment firms to reconstruct their reconciliation strategy to handle unconventional regulatory requirements and related tasks.
We are witnessing partnerships and collaborations occurring to provide end to end services for regulations such as MiFID II. Firms are looking for cleaner solutions and given the complexities involved they do not want to go to multiple service providers for each aspect. The market is grabbing these opportunities by consolidating and forging partnerships. Analytics firms are partnering with research management platforms to provide “full service” for research management, reporting solution providers are partnering with process automation firms to provide end to end data management and reporting services.
The network is driving adoption of standardized post-trade swap data models and workflows.
The market maker will contribute real-time crypto market data before expanding into equities.
Pyth is built on a blockchain to handle receipt and distribution of fast-moving data.
Interoperability with current capital markets infrastructure is a challenge.
Investors have more understanding on the operational side of crypto markets.