Preliminaries
As revenues and operating margins in the capital market industry continue to decline on account of evolving regulations, enhanced liquidity, capital requirements and new emerging technologies, the focus is on simplification and redefining business models to ensure growth and sustainability in this challenging environment. Most of the capital market firms work in silos and have a number of legacy systems. Replacing these legacy systems require significant cost and time, and with shrinking margins, most of the firms do not have budgets for huge investment in technology. RPA can help them bring those efficiencies without much investment. Further, there has been increased scrutiny by market regulators, high volume of data and difficulty in integrating different legacy systems with new technologies. RPA is enabling capital market firms to overcome these challenges by automating middle and back operations. Robots can handle seasonal increase in volume of data without adding additional headcounts. Further, an RPA platform maintains an audit trail for each step/activity of a process, the same can be useful for audit and regulatory requirements.
New technology such as robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML) — loosely grouped together and referred to as intelligent automation — promises to offer significant operational and efficiency improvements. Their scope and level of adoption in the mid-back office are rapidly expanding as new and domain specific use cases emerge by the day. Application of intelligent automation is already underway in many labor-intensive processes. The landscape of intelligent automation solution providers in capital markets consists of different types of players and is rapidly evolving. Almost every large capital markets firms and many mid-tier institutions are already using or running pilot projects involving intelligent automation. The benefits can be enormous and come in several ways, such as reduction of manual efforts, freeing up capital and human resources, productivity improvements, better performance monitoring and capacity utilization, and superior compliance. The RPA in Financial Services market revenue is estimated to be $319.6 million in 2018 and is expected to reach $955.2 million by 2023, growing at a CAGR of 24.5% during the forecast period 2018–2023. The services include consulting, implementation, and training and education.
How RPA Works
Contextual to UIPath RPA, it deploys “robots” or software agents that mimic how users interact with computers. These robots are programmed or configured to manipulate a user interface (UI) instead of humans. New generation RPA platforms include artificial intelligence (AI) and machine learning (ML) capabilities to handle high-volume, repeatable tasks and transactions. An RPA robot can log into an application, enter data, perform calculations, complete tasks and then log out. UiPath has of 3 main components:
- UiPath Studio: UI tool to visually design the process to automate.
- UiPath Orchestrator: Web application that manages the creation, monitoring, and deployment of all robots and processes.
- UiPath Robot: Runs processes that were built in UiPath Studio. Execution agent that is installed and executed in the actual machine.
In short, this is how it works:
- Visually design a process using UiPath Studio in a developer PC,
- Publish/deploy the process to UiPath Orchestrator to be managed, scheduled, and monitored.
- The actual process itself is executed by the UiPath Robots installed in the respective machines.
Robotic process automation tools offer two distinct modes of deployment: assisted/ attended automation and unassisted/ unattended automation. Hybrid RPA combines the two models.
- Assisted / Attended RPA. In assisted automation, the RPA automates applications running on a user's desktop typically for the purpose of helping the user complete an involved process in less time. This usually generates cost savings and helps deliver a better user and customer experience.
- Unassisted / Unattended RPA. Unassisted automation requires no human agent. The RPA software performs on its own, notifying the user only when something goes wrong. Unassisted automation can work 24/7 -- "an ideal scenario for optimizing a process,"
- Hybrid RPA. In hybrid RPA, the employee and bot essentially work as a team, passing tasks back and forth. Hybrid RPA automates the work that can be completed solely by the bot (unassisted) as well as work that that involves unstructured data or requires decisions by an employee (assisted). In hybrid RPA, the software bots and employee can work on different tasks at the same time for optimal efficiency.
RPA bots can use the operating system applications like a human user. Bots are capable of these but please note that this is not a comprehensive list. RPA is too flexible for us to provide a full list of bot actions:
- Launching and using various applications including: Opening emails and attachments; Logging into applications; Moving files and folders
- Integrating with enterprise tools by : Connecting to system APIs; Reading and writing to databases
- Augmenting your data by : Scraping data from the web including social media; Data processing
- Following logical rules such as “if/then” rules : Making calculations; Extracting data from documents; Inputting data to forms; Extracting and reformatting data into reports or dashboards; Merging data from multiple sources; Copying and pasting data
Top 3 Robotic Process Automation (RPA) softwares are UiPath RPA, Automation Anywhere and Blue Prism:
UiPath - Being a leader with 36% market share in the rapidly churning RPA sector, UiPath has approx. 3,000 employees dedicated to RPA, in contrast to some vendors that include RPA as part of an overall portfolio. RPA is well-suited for large enterprises with significant resources to commit to building RPA and associated AI and ML technologies into its business workflow. UiPath offers an Azure Cloud SaaS tool, an embedded analytics feature, and a mobile app for its Orchestrator tool. Significantly, it offers an AI integration fabric, which allows robust enablement of AI features. Key RPA Features are:
- Extensive partner network: UiPath has built a rich network of alliances with technology partners. These RPA related applications offer range of services from AI to business process management (BPM) to complex process mining. This well-developed network should help UiPath stay ahead of the curve as RPA grows rapidly.
- Advanced user interface: UiPath clearly strives to offer an intuitive user interface to its bot dashboards. UiPath is an advanced platform, means not all deployment scenarios are low-code or no-code. Getting wider usage from RPA requires machine learning expertise.
- Newer cloud solution: UiPath unveiled its Cloud Enterprise RPA in June 2019. The key advantage of this cloud version is that it serves companies while avoiding the added hardware-software data center expenses; it also adds scalability.
Blue Prism - Definitely a leader in RPA with 24% market share, Blue Prism has an elaborate product roadmap and a true commitment to using AI to advance its automation. While it does offer a free version as a trial, Blue Prism is squarely targeted at large enterprise companies with deep resources. It has built an extensive array of partners – including consulting partners – that have built a large library of complimentary automation, analytics, and decision management applications. The company’s length of market tenure results in a secure, stable automation product. It is particularly known in financial services. Key RPA Features are:
- Strong vertical focus: Understanding that the sectors ranging from healthcare to manufacturing to retail have very different RPA needs, Blue Prism has launched solutions across the industries, with active customers in each.
- Graphical user interface: To allow less technical staff create automations, Blue Prism includes drag-and-drop interface for building process automations.
- Commitment to AI: Blue Prism Labs is an AI laboratory focusing on computer vision and document interface, primarily for unattended use cases. The company’s roadmap suggests it will use this AI depth to support more attended, human-involved use cases in the future.
Automation Anywhere - Focused exclusively on the RPA sector with 16% market share, Automation Anywhere is fully cloud and SaaS-enabled, providing a low enough cost of ownership for SMBs, with a product depth and a developed roadmap that’s also suitable for large enterprise customers. Automation Anywhere is known for fast implementation. It has pluggable API integration for developers. The company’s mobile app allows customers to monitor and manage bots on the run. In a step forward, its IQBot includes support for handwriting. Key RPA Features are:
- Industry-leading partner network: Automation Anywhere has a vast partner network, offering support for RPA tools and solutions of every stripe. It boasts nearly 600 staff in R&D. The company has a market presence around the world, allowing it to serve even the largest multinational client.
- User interface: Automation Anywhere’s most routine bot automations are designed to be very easy to build. The solution is fully cloud-based (and SaaS accessible) so a web-based software robot is assembled with relative simplicity. The company’s flagship Automation Anywhere Enterprise A2019 offers a persona-based platform for developers and non-tech-savvy staff to use for easy collaboration.
- Accessible cost structure: AA’s scale and cloud-based approach allow it to offer a digital staff at an affordable cost. Additionally, the company’s bot store and its large community offer support and pre-built elements that can further drive an accessible price point.
RPA Application in Trade Lifecycle Management
Several processes in the trade lifecycle can benefit from application of RPA. Trade allocation, settlement, reconciliation and exception handling, corporate actions dissemination and processing are all labor-intensive functions and are ripe for application of intelligent automation. These functions are seeing emergence of intelligent automation tools that eliminate a significant amount of manual tasks by automating information extraction, assembly, enrichment, classification, advanced analytics, language processing and communication analysis, reporting, compliance, and audit trail.
Trade Allocation
RPA along with Artificial Intelligence can be used in several stages, such as triggering the process at scheduled time, extracting data from different sources in various formats, converting semi- or unstructured data into structured format, identification and classification of the extracted data, and sending the output to other applications for further processing. These can drive significant improvements in automation, straight-through processing rates, process efficiency, and infrastructure utilization, and save time, efforts, and costs.
Trade Monitoring & Investigation
AI systems can learn from previous patterns and red-flag probable mistakes, going beyond simple rules-based monitoring. Trade surveillance is driven by rules-based algorithms that generate high volumes of false positives. Supervised and unsupervised learning can be used for improving prediction accuracy, faster error spotting, loss prevention, and continuous improvement. AI tools can easily identify new and complex patterns, across asset classes. Meta data analysis, linguistic analysis, phonetic and behavioral analysis can improve communication surveillance. AI solutions can learn from how erroneous trades were resolved previously, and automatically propose if new breaches should be approved. These will boost productivity and optimize use of capital. Automating investigation workflows using RPA can reduce costs and human resource requirements.
Trade Settlement
RPA can streamline trade settlement by performing research on orders, resolving discrepancies, and resolving trades that are impacted by system processing issues. It sources and matches the trade component automatically. In case of multiple exceptions, bots can identify those by transaction ID and escalate them immediately. It can calculate funding needs in real time, generate reports and then distribute those report. With AI, it can automatically categorize the volumes of emails in settlement process by reading them, filling them and prioritizing them.
Trade Support
A blotter is a physical or digital record of all trades made over a period of time (usually one trading day) along with their relevant details. The details of a trade include the time, price, order size, and a specification of whether it was a buy or sell order. This serves as an audit trail of transactions. RPA tool can look at blotters to make sure entries satisfy requirement for conformation and requirements. NLP can capture trade details from other systems to fill in missing entries. RPA can pull allocation files from client messages, record details, and pass them to downstream systems.
Corporate Actions
Corporate Action processing can be rule based. RPA pulls out relevant information from standardized SWIFT messages to capture event announcement and entitlement calculation by checking positions on relevant dates in internal records. RPA tools can extract data from corporate actions announcements, classify and process those using NLP techniques. The tasks can be scheduled at specific times of day by using task bots which can also automate notification to counterparties and distribution of dividends, interest, etc. Some vendors report being able to improve processing time from hours to seconds and reduce manual efforts by over 80%.
RPA Application in Client lifecycle management
Client lifecycle management is another labor-intensive process. Many use cases are emerging in KYC, onboarding, account and trade supervision, due diligence, investigation, and reporting workflows. Intelligent automation empowered tools streamline information extraction and collation for efficient due diligence, while cognitive tools provide new insights for conducting risk assessment and case investigation.
Customer Onboarding
Regulations such as KYC, AML and FATCA resulted in capital market firms to collect, input and analyze client data to ensure they comply with regulatory and firm policies. RPA simplifies the process of customer onboarding includes executing KYC and other related checks. It can combine multichannel capabilities and automate workflows, thereby accelerating the onboarding process. RPA can gather and input a huge amount of structured data and maintain a complete audit trail. RPA with advanced analytics can help improve risk assessment methodologies and segmentation, incorporate dynamic updates, and enable frequent monitoring. Firms can use RPA in client onboarding process to improve regulatory compliance, improve customer experience, risk assessment & reporting at a minimal cost.
Investment Management
RPA can assess client’s portfolio and thus minimize the inherent risk of investing. The bots also help in improving the users’ investment decision making by offering information updated in real-time about fluctuations on the stock market.
Reconciliation
There are different types of reconciliations performed at various stages of the trade life cycle where huge amount of data needs to be reconciled across systems. RPA can be used to reconcile the data between multiple systems and identify mismatches across datasets during the reconciliations that are performed at various stages of the trade life cycle. Reconciliation involving SWIFT can be swift. But if third party isn’t SWIFT enabled, the Ops team have to download the information manually, lookup the details everywhere, and even review email to apply fund correctly. RPA can enable automated outsourcing and executing of reconciliation and create journal entries in system where necessary. These can help improve STP rates, efficiency, and accuracy, speed up trade processing, and free up resources.
Regulatory Compliance
RPA plays a critical role in improving governance and regulatory compliance. It simplifies compliance by keeping detailed logs of automated processes, automatically generating the reports an auditor needs to see, and eliminating human error.
Communication and Reporting
RPA can create and send annual and quarterly reports to regulators and in standardizing client reporting. Robotics can extract information from different internal and external systems, compare the information across systems and highlight the variances.
Internal Reporting – RPA can work across legal entities, functions and systems to pull together data and create reports in various formats. RPA and ML can handle the first level review, so management can concentrate its effort on addressing exceptions.
External Reporting – Even as firms develop longer term strategic platform, they can use digital labor as a relative quick hit to reconcile data and generate reports that reduce time and operational cost. Bots can generate and distribute intraday reports to help client monitor intraday account movement.
Collateral Management
Intelligent character recognition (ICR) tools could capture agreement terms from existing legal contracts and automatically enter details in collateral system. RPA can handle receiving, netting, matching and the posting of margin calls using ML and ICR to reconcile more quickly and accurately. These process automation solutions apply across the industry, including broker – dealers, custodian etc.
Client Service
RPA and ML can compile existing information to support new account opening and to avoid asking an existing client the same thing again and again. RPA bots can setup new counterparty details in reference data system based on standard emails from the front office. Bots can investigate client requests received from self-service platform to confirm and apply same day wire by automatically reviewing SWIFT messages.
Conclusion
Intelligent automation-enabled RPA solutions can greatly help capital markets firms improve efficiency in mid-back office operations, which has been a critical source of inefficiency and bottleneck for a long time. The promises are enormous, but there are critical considerations for realizing the full potential of the new technology. The whole landscape needs to be reviewed and re-architected wherever applicable for optimal benefit; otherwise savings achieved through automation tools may not be reflected in the overall processes. Skill and resourcing will be important consideration. New technology, especially RPA, advanced AI and ML techniques, can require a different mix of talent from existing ones. Firms should have a clear strategy whether building new teams to manage them or engaging with service provider would be optimal. Some firms have been early movers in this regard and are already using intelligent automation tools in capital markets mid-back office. Many others are currently at a stage of exploration, having recently completed pilots and proofs of concept. It is expected that many of them will be taken into production in near future.
