Wavestone Modernizing Post Trade

MODERNIZING POST TRADE

HOW BANKS CAN LEVERAGE F INANC I AL TECHNOLOGY TO REJUVENATE THE I R LAGG ING BACK OFF I CES

INTRODUCT ION

Post Trade Processing: Context and Issues

When it comes to innovation, big banks have historically doubled down on investment in their front offices, attempting to optimize and transform the areas that would have the most significant impact on their bottom lines. Meanwhile, over the last decade, back-office operations have been burdened with complex and manual processes, increasing overall costs and inefficiencies. Today, given the increasingly strong regulatory headwinds, banks must rethink their approach to running some of the veiled albeit most essential parts of their value chain. Post-execution operations is an area that has received tremendous attention recently from banks as well as innovators in the financial technology (fintech) space. Driven by the need to maximize capital efficiency and mitigate operational risk, there is a push towards automating the heavily manual processes that exist within the settlement, clearing, and collateral management functions of a bank.

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WHY POST-EXECUTION OPERATIONS ARE LAGGING

Legacy Technologies Heavy reliance on manual processes and interventions at different stages from allocation to settlement.

Complex System Architecture Banks are divided into business lines leading to fragmented architecure and limited interoperability.

Limited Automation Lack of real-time data limits real time notifications, automated escalations, real-time reporting and pre- to post-trade support capabilities.

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Interestingly, unlike the retail banking space where incumbents are ceding market share due to fintech firms’ disintermediating effects, the post- operations realm is one that incentivizes collaboration between the two. In a market where high capital requirements are barriers to entry, fintech firms are working alongside banks to overhaul their legacy systems. Wavestone sees massive potential for fintech firms to cultivate synergies with banks on areas ranging from using blockchain to simplify clearing and settlement processes to using RPA to expedite reconciliation. THREE KEY AREAS TO LEVERAGE EMERGING TECHNOLOGI ES

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Clearing and Settlement

Over the last few years, there has been quite a lot of speculation regarding the rise and use of blockchain in the banking industry, with a focus of many of the use cases residing within banks’ back offices. The hope is that the underlying technology of blockchain, distributed ledger technology (DLT), will wipe out the need for clearing and settlement operations entirely. In more ways than one, blockchain undermines and eventually renders redundant the underlying infrastructure of a bank’s clearing and settlement processes. The concept of an immutable ledger held by all participants of a trade serves as a source of truth for any executed trade between a buyer and seller. DLT can both guarantee payment from buyers and ensure delivery of assets from sellers thus eliminating settlement risks on both sides.

The diagram below shows a traditional clearing process vs. the clearing process using DLT.

TRADITIONAL CLEARING PROCESS

DLT CLEARING PROCESS

Counterparty A

Counterparty A

Counterparty B

Counterparty B

Clearing House

Distributed Ledger

Centralized Ledger

Counterparty C

Counterparty C

Counterparty D

Counterparty D

Settlement time: 10 minutes Settlement risk or default risk reduced by 99%

Settlement Time: 3 Days

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THREE KEY AREAS TO LEVERAGE EMERGING TECHNOLOGI ES

Collateral and Margin Management

In light of regulations such as the European Market Infrastructure Regulation (EMIR) in Europe and the Dodd-Frank Act in the US, banks need to rethink how they manage their collateral. From increased collateral demands to central clearing mandated for certain types of derivatives contracts, these regulations are prompting banks to overhaul their collateral management processes. One area of the collateral management function where banks can leverage artificial intelligence is margin call management. Currently, in the collateralized over-the-counter (OTC) derivatives market, parties exchange margin call notices and confirmations of collateral settlements without the use of any centrally defined standard message format. These notices—usually delivered by email—require a high degree of human intervention and, as a result, are susceptible to errors. Fintechs such as Synechron, which are leveraging robotic process automation for margin call management, use machine learning for margin call email automation1. The solution uses natural language processing (NLP) technologies and automation techniques to process email communications related to OTC derivatives margin calls and automatically transmit that data into collateral management systems as an authoritative data source for human verification, thereby speeding up collateral management, increasing accuracy and positively impacting collateral costs.

1 https://www.synechron.com/fin- labs/artificial-intelligence/automat- ed-margin-call-management

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ASCERTAINING MARGIN DETAILS

COLLATERAL MANAGEMENT INTEGRATION

EMAIL PARSING

The system parses through emails and looks for margin details in the body of the email or attachments

The RPA engine determines a margin response email from an anticipated margin call from the counterparty. This could be partial agreement, full agreement or dispute

Having identified relevant margin factors (eg. MTM value, margin call value, currency etc.), the RPA engine can pass this information on to the collateral management system

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Reconciliations

Most trades are settled via straight-through processing, which means they are cleared without any intervention from back or middle offices. Failed deals2 are put in an “unconfirmed” bucket and need to be settled manually—a difficult and tedious process; banks need to confirm settlement instructions over the phone with trading desks and counterparties. The AI solution performs three essential tasks that reduce settlement errors, and duration, and lead to far greater flexibility overall. Identify AI can analyze vast volumes of previous trades to understand exactly where issues lie, and build a clearer picture of the problem. Analyze Spot gaps in the settlement process such as inaccurate details, settlement errors, outdated accounts. AI can do this much faster than employees manually could. A process that may take up to five to 10 minutes manually can be completed within a second. Increased Flexibility Above all, the end-of-day checklists that banks run to confirm that trades are confirmed by counterparties can be run intra-day as well with AI, allowing the relevant personnel to troubleshoot and fix exceptions by end of day before there is any impact on the P&L or the client’s account. Predict Algorithms can predict which settlements are most likely to fail based on past data. They can then propose steps to remediate the issue which reduces the need for exception processing situations, driving down operational costs.

2 A trade fails when a buyer doesn’t transfer the funds, or a seller fails to deliver an asset by the agreed settlement date. Missing, late, or mismatched information can also lead to failed trades.

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C HA L L E NG E S AND WORKA ROUND S

Lack of expertise

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The biggest challenge that banks may face in leveraging these technologies is the lack of domain-specific experts within their organizations. As discussed, some of the processes and platforms that even the most modern banks use were developed and hardcoded decades ago. In times of rapid growth, more tools and solutions were piled on without considering the long-term effects of these jarring systems. In many cases, the engineers that developed the initial processes no longer work at those banks, or worse, have long retired from the industry. On the other hand, current experts within the organization may lack the understanding of the modern methods and tools discussed. In both cases, one possible solution is to collaborate with external parties. In both cases, one possible solution is to collaborate with external parties. In the past few years, banks and non-banking financial institutions have shown a keen willingness to work with small fintech providers from small proofs-of-concept (POCs) to large scale implementations. Most recently in 2017, Nasdaq and Citi’s Trade Solutions Group announced their collaboration with Chain.com, an enterprise blockchain firm.

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The partnership leverages Chain’s blockchain infrastructure platform to enable straight-through payment processing and automate reconciliation by utilizing DTL to record and transmit payments3. Such an initiative is not only a huge milestone in the financial sector but also a harbinger of further widespread application of blockchain technology.

Resource commitment

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Yet another obstacle for banks is the cost of implementation. Carrying out a major transformation is hardly cheap, and it’s even harder to justify when it’s not actually going to make any money for the company. Furthermore, the costs don’t stop at purchasing the underlying hardware and software components; they are far more profound than that. The change management process that follows to ensure a seamless integration can not only cost a fortune but can also be a nightmare in terms of governance. An overhaul of even a small bank’s back-office would likely take months if not years and diverting full-time resources to such projects can be inefficient (if at all possible). If there is a lack of internal resources and project management capabilities, there are firms that offer deep expertise and experience in the transition management process of large-scale transformations. These third-party contractors make it possible for banks to complete such projects in record time.

3 https://www.citigroup.com/tts/about/press/2017/2017-0522.html

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CON C L U S I ON

When organizations learn that to modernize and grow, they need to innovate and leverage new technologies, they often do one of two things. The first type of organization dispenses with the cost- benefit analysis and takes a quick and dirty approach. To prevent disrupting business-as-usual (and to avoid the large bill), they implement a couple of small yet trending projects. One example of this would be to hire a team of blockchain engineers to develop a POC for a DLT platform. This project would then be publicized and give outsiders the impression that this company is working on cutting-edge technologies. However, the truth is that these shiny bells and whistles rarely add any real value to the firm as the only way to create lasting change is to start from the ground up and take a more in-depth look at one’s current infrastructure before tacking on more things. This leads us to the second camp—the overzealous box-tickers. This group of firms takes a less myopic view of the situation and realizes that they need to transform multiple parts of their organization to drive change sooner. This type of firm develops a long-winded checklist of activities to complete to achieve their desired goal. They commit extensive resources including time and funding to this project as if it were a magic bullet. Unfortunately, often the executives at such a firm take a top-down view and rarely stop to build consensus and commitment at the grassroots of the organization. The result is an overwhelmed and disenfranchised workforce that fails to see the value in the strategy or worse, disagrees completely. In light of these challenges, this article outlines just the tip of the iceberg of the new developments financial institutions can take advantage of to modernize their post-execution processes. The key takeaway, however, is not to implement all of them simultaneously or, in some cases, at all . The assessment should begin by looking at a bank’s priorities and identifying the processes that will deliver the highest value for the organization. Post-execution processing might be a veiled part of the organization, however, in many ways, it operates at the transversal of the organization and enables many core functions, and it ought to be treated as such.

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ACHI EVE TRUE BUS INESS AGI L I TY WI TH THE RIGHT BALANCE OF METHODOLOGY AND ENTERPRI SE-READY TOOLS

Visit us at wavestone.us or give us a call at (610) 854-2700 to see what we can do for you.

About Wavestone US

Wavestone US is the North American arm of global management and IT consulting firm Wavestone. We have supported the transformations of more than 200 Fortune 1000 companies across a wide range of industries, leveraging a strong peer-to-peer culture, offering a practitioner’s perspective on IT strategy, cost optimization, operational improvements, cybersecurity, and business management. It is our mission to help business and IT leaders successfully deliver their most critical transformations and achieve positive outcomes. We drive change for growth, lower cost, and risk, and create the trust that gives people the desire to act.

Authors

Gaurang Gala Head of Regulatory & Transformation

Atharva Bhandarkar Management Consultant

www.wavestone.com

In a world where knowing how to drive transformation is the key to success, Wavestone’s mission is to guide large companies and organizations in their most critical transformation projects, with the ambition of a positive outcome for all stakeholders. That’s what we call “ The Positive Way ”.

Wavestone brings together 3,000 employees across 8 countries. It is a leading independent player in the European consulting market. Wavestone is listed on Euronext Paris, and recognized as a Great Place To Work®.

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