Go to www.cygnuss.com to see the worlds first complete stand-alone software enterprise solution(SAAS) the digital super wealth adviser the system trades 125 assets 24/6 through the use automation, machine learning and artificial intelligence across a full macro strategy. The algorithm trades fifty indices and commodities futures and seventy-five FX crosses. The program aims for the highest risk average return possible while aiming for high alpha and always having our tail risk covered. Cygnus is the first of its kind super wealth adviser that runs a complete quantitative strategy with zero human assistance as a complete stand alone system. The software is available in SAAS format and can be white labeled to your specifics. The suite offers many features that are very difficult at times for humans especially when dealing with high volume contracts, including auto rollover, auto non correlate and precise risk management for every trade can be changed to your appetite at any given time. The system analytics make the past fifty years of human task’s in back office reporting completely obsolete while lowering overhead and compliance cost’s by 90%. Cygnus will be the disrupter of the active aggressive trader. Cygnus, risk’s protocol’s the system handles risk management to the millisecond and you can send all the humans home. The system handles unlimited cash and clients and is available for white label or lease. The savings on overhead alone will boost your firms returns instantly.

Just how important will the ability to write computer code be to a successful career on Wall Street?

According to R. Martin Chavez, an architect of Goldman Sachs Group Inc.’s effort to transform itself with technology, “It’s like writing an English sentence.”

As Chavez prepares to leave the company, the onetime commodities staffer who rose to posts overseeing technology and ultimately trading is reflecting on his “26-year adventure” in the industry. “The short, short description of it is making money, capital and risk programmable,” he said in a Bloomberg Television interview to be broadcast Friday. “There are certainly many kinds of manual activities that computers are just better at.”

Chavez, 55, outlined strengths that can help humans stay relevant, such as their relationship skills and ability to assess risks. Yet he predicted that longstanding career dichotomies on Wall Street, like trader versus engineer, will go away. To keep working, people will need both of those skills. Even money is going digital, a shift that goes far beyond cryptocurrencies, he said, pointing to the success of Stripe Inc. as an example of creating new ways to move funds.

Stripe, for its part, has become one of the most valuable companies in Silicon Valley.

Mom’s Advice

Once Chavez leaves the bank at the end of the year, he plans to focus on “programmable money” and spend time thinking about and investing in “programmable life.”

In many ways, his career to date illustrates Wall Street’s own evolution. He was early in combining traditional banking activities and engineering, honing skills in the 1990s that companies are now vying to bring into their top ranks.

He was also openly gay at Goldman Sachs at a time when it was virtually unheard of. And he’s Latino, a group particularly underrepresented across Wall Street’s leadership.

His mother used to tell him that he would have to work twice as hard to be successful — which he called “excellent, excellent advice.” Ironically, something that helped him in his career, he said, was being able to speak Spanish in his job.

His mom also coached him not to get wound up by what other people say. He said the lesson he took from one of her mantras, “Que digan misa,” was that “I’m just going to do what I know is right, I’m going to do my thing, and trust that it’s going to work out.”

These top Robotic Process Automation (RPA) companies are leading the sector in creating robotic automations that drive business effici

RPA (robotic process automation) is software automation technology that is programmable by users to accomplish business tasks. It enables users to create intelligent software bots that accomplish a wide array of workplace tasks – from adding input to data analytics software to completing forms – that allows human staffers to focus on higher value tasks.

RPA software robots can copy the actions of an employee. Having recorded the human’s routine, a software bot can then perform routine tasks like logging into a web site, starting downstream activities, and copying tracking numbers. RPA often uses artificial intelligence and machine learning to accomplish its work. Indeed, the mix of artificial intelligence and RPA enables the true promise of robotic automation.

The benefits of RPA are numerous: RPA solutions help out in call centers by offering selling guidance to service agents. RPA tools assist in healthcare by completing data entry. And RPA boosts the finance sector by distributing data across departments without human help.

Investment in the RPA sector is climbing at a rapid rate: RPA software is one of the fastest growing software sectors across the entire software industry. Totals sales of RPA software are forecast to climb from an approximate $1 billion in 2020 to $2.7 billion by 2023.

rpa companies

The RPA market is experiencing exponential growth as companies rapidly deploy automation to enhance employee productivity.

How to Select an RPA Software Tool

The market for RPA software is based on the emerging technology of user-programmed automation software. This is a new field with constant and rapid advances. The churning field of vendors is well-armed with complex marketing jargon about the wonders that RPA can achieve.

To help cut though the hype, examine these four factors when selecting a solution:

1. Depth of product roadmap

The challenge of RPA is that, while these bots are well suited for simple, routine tasks, moving beyond the “easy wins” requires an RPA solution with versatility and depth. Does the vendor have a product roadmap that looks likely to handle all the growth in your business?

Does the solution include process mining, an RPA tool that points out gaps and inefficiencies in the workflow that automation can address? Will the RPA solution integrate with your legacy applications?

2. User Interface

If a given RPA solution is advanced and has deep use of artificial intelligence, the dashboard user interface used to program it is typically complex. It often requires software developers to program the RPA bots.

If, however, you want a simpler user interface, you can find it. The RPA industry speaks of “robot design for citizens,” meaning non-technical staffers can set up bots. But with this level of simple UI, you’re often sacrificing some level of upper level functionality in bot capability.

3. Attended or Unattended

Once programmed, an unattended RPA robot does its work without human assistance. Many of the RPA bots in use are unattended; set them up to do a simplistic job and let them keep repeating.

An attended bot is also know as a “human-in-the-loop” bot. These unattended bot work in close partnership with staffers; for instance, offering real-time suggestions to a call center agent to close the sale.

4. Level of AI and ML in the RPA platform

The confusion in the RPA market is particularly deep around the issue of artificial intelligence and machine learning. A simple RPA automation does not need AI to perform a series of routine repetitions. And yet the long-term promise of RPA is that these basic bots will use AI and ML to develop into “super performers,” accomplishing a dazzling array of complex tasks.

Consequently, RPA vendors are always touting their products’ depth of AI and ML usage. The degree to which any RPA solution incorporates AI and ML “under the hood” is often hard to fully know – and it will change with time. It may be built in, but be limited.

Therefore, buyers need to ask probing questions about each vendors’ AI capability. Is it reality or marketing?

Jump to:



To the extent that there’s a market leader in the rapidly churning RPA sector, UiPath has a solid claim to the title. The company – with some 3,000 employees – is 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.

To speed deployment, the software includes an integrated library of pre-built automation components to augment custom automation. In 2019 UiPath acquired ProcessGold and StepShot, which enable it to build more process mining into its RPA functionality.

Key RPA Features

Extensive partner network: UiPath has built a rich network of alliances with technology partners, which offer supporting software. These related applications run the gamut from AI to business process management (BPM) to complex process mining. This well-developed network should help UiPath stay up with the curve as RPA grows rapidly.

Advanced user interface: UiPath clearly strives to offer an intuitive user interface to its bot dashboards. While it generally succeeds at this, UiPath is an advanced platform, which means not all deployment scenarios are low-code or no-code. Getting full use from RPA requires some machine learning expertise.

Newer cloud solution: UiPath unveiled its Cloud Enterprise RPA in June 2019, which is later than some of the top RPA vendors. 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.


  • Extensive product offering suitable for many verticals
  • Good for Windows-based users
  • Known for responsive customer support


  • User interface is complex
  • UiPath was late to the cloud

Automation Anywhere

Focused exclusively on the RPA sector, Automation Anywhere is a market leader, with a high profile in the market that makes it a “first look” for any potential buyers. 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.

The company’s community edition is free for small businesses, developers, and students – which is excellent as a learning tool. The Enterprise solution also has a free trial available. The company’s thin client architecture is well regarded, as is its ability to connect various automations.

Key RPA Features

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.

Like all RPA vendors, most of Automation Anywhere’s processes are unattended. But the vendor acquired Klevops in August 2019, which boosted the company’s orchestration for the more complex attended automation.


  • Known for responsive customer support
  • Flexible architecture allows for easy scaling
  • Typically an affordable cost structure


  • While simple bots are low-code, more complex automations require extensive knowledge and technical staff.
  • In some cases, API access could be improved.


EdgeVerge is a subsidiary of India-based IT giant Infosys Technologies. The company’s RPA software is best suited for major enterprise companies, including those with large reliance on consumer customer service. EdgeVerve is a strong player in RPA assistance for call centers.

In addition to its flagship RPA solution, AssistEdge Robotic Process Automation, it also offers a toolset of machine learning and AI tools called Infosys Nia.

Because of InfoSys’s many business relationships, EdgeVerve is expected to grow its customer base aggressively within this existing customer group; customer that don’t have an existing relationship with InfoSys are not as clearly targeted. 

Key RPA Features

Combining Automation and AI: Built in to the EdgeVerge product roadmap is a focus on RPA governance, and a sophisticated approach to automation that uses this governance to manage the union of AI and automation. Part of this strength comes from EdgeVerve’s Infosys Nia division.

Customer Engagement: InfoSys has a strong services component, so it makes sense that the company helps speed deployment and the overall ramping up of RPA projects. This includes support for build and design.

Attended Customer Service/Call Center With all of its offering available as on-premise or cloud-based, EdgeVerve has a particular strength in the call center. Its AssistEdge Engage solution offers support for customer service agents, which reflects the company’s strength in unattended RPA. This is a differentiator in an overall market focused on attended.


  • Strong graphical User Interface leads to ease of use.
  • Highly scalable solutions, with Business Intelligence dashboards. It includes enterprise-grade security that is GDPR ready.
  • The company’s AssistEdgeDiscover offers a big boost to process discovery.


  • While the company’s Nia division specializes in ML and AI, it is not completely clear how well these technologies are integrated between Nia and EdgeVerve.
  • Some users want better reporting on robot performance.

Blue Prism

Definitely a market leader in RPA, 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.

Blue Prism is the most established RPA vendor in the market, having been founded in 2001 by a team of software automation experts; it practically coined the term “RPA.” 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.

The vendor offers on-premise or SaaS cloud deployment; it integrates with Google machine learning workflow, which is arguably best in class. Top-tier security and audit trails include non-repudiation features.  Blue Prism is well known for its supporting documentation; this is key in the complex RPA market.

Key RPA Features

Strong vertical focus: Understanding that the sectors ranging from healthcare to manufacturing to retail have very different RPA needs, Blue Prism has launched scores of industry solutions, 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: The company’s 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. Blue Prism acquired startup Thoughtonomy – which built a cloud-based AI engine – in July 2019 to further its AI functionality.


  • Blue Prism is strong in unattended use cases, which allow human in the loop, augmented staff productivity.
  • The vendor’s large array of partnerships includes many consultants; this could help with launch and implementation.
  • The company’s long tenure and focus on security and encryption provide it with the stability needed for large enterprise deployments.


  • Some users say that reporting and scheduling could be more efficient in enterprise-wide deployments.
  • While many RPA vendors’ bots record human staff and mimic them, Blue Prism doesn’t use this function to build automation.
  • The company’s design studio generally requires a more technical user.


For SMBs looking for a more accessible start to the complexity of RPA, Softomotive is a viable choice. The company’s platform allows users to start with more modest automation implementations, then focus on a faster move toward tangible gain. Sofomotive’s dashboard is relatively intuitive, though more advanced automations will likely require more technical staff.

Softomotive’s focus on ease of use even makes it a suitable choice for one-person shops and startups with minimal tech staff. In some cases it could be a good choice for an enterprise team that wants to start small. Serving this larger market is a real strength, given that some large enterprises also need to start small and prefer a more intuitive UI.

Both of Softomotive flagship offerings support both attended and unattended automations. Included is a feature set to monitor execution and oversee concurrency in deployments.

Key RPA Features

Geared to scale up: Despite being a favored choice by midmarket customers, Softomotive has scalability built in – both in terms of overall size and vertical market. This includes the ability to reuse automations, so they can be easily duplicated to handle larger scale use cases.

Two levels of products: On a similar note to above, Softomotive’s ProcessRobot enables enterprise-wide use cases, with the ability to monitor diverse automations from a single location. In contrast, the company’s WinAutomation is a quick-launch RPA application that it leverages to best serve the SMB market.

Expected to grow: Higher end applications like optical character recognition are not typically what customers look for in Softomotive, yet the platform is expected to grow and even challenge larger enterprise offerings. But realize that to push the platform past the company’s drag and drop easy use will usually call for a programmer or a data scientist.


  • Developed to scale both vertically and horizontally.
  • Intuitive UI allows recording of human staff actions to more easily produce bots.
  • Known for affordable costs.


  • A smaller RPA vendor that may need continued growth to be a top player in the large enterprise market.
  • Could use improvement in analytics for mixed content types.


While some RPA solutions aim for simplicity and ease of use, WorkFusion strives to offer deep and robust AI and ML functionality into its RPA tools. It is this quality of fully leveraging AI and ML that will propel RPA into the future. The WorkFusion platform contains hundreds of advanced prepackaged use case solutions that drive performance in predictions, category classification and data mining.

WorkFusion’s key focus is unattended RPA bots. For companies looking for complex, versatile, high function unattended solutions that can traverse a large enterprise, WorkFusion is a top choice. The company has a strong presence in the financial industry.

Key RPA Features

Integrated BPM: WorkFusion offers an integrated business process management (BPM) system to manage robotic automations. This well-developed system enables versatile ML tools and help interoperability.

Developed analytics: To better understand the performance of your automations, WorkFusion’s analytic tools allow the ability to target single automations – helping fix inefficient processes before they go on too long.

Open source databases: The company has included a number of open source databases to boost data scalability and upgrade its data management capabilities. RPA requires plenty of data to fuel automations.


  • Known for solid customer support.
  • Top-rated development process to drive future product road map.
  • Sophisticated feature set for analytics.


  • Advanced platform that requires expert staff to fully implement.
  • Not focused on attended automations.


Kofax is well suited to companies that typically gather large quantities of unstructured data from myriad data pools. This includes sources like social media and mixed-data customer interactions.

While many RPA vendors require customers to run automations on all client desktops/terminals, Kofax has shifted this system. Its RoboServers tool shows the system’s interface in single, easily managed container – avoiding having to run the solution on every desktop. This ingenious method lowers infrastructure costs.

Potentially further reducing costs, the Kofax platform has such a strong offering in optical character recognition tools that the need to purchase an additional OCR solution from a third party is typically avoided. The company is strong in the logistic and transportation industries.

Key RPA Features

Easy data transport: An RPA tool uses automated bots to shift data from one web portal to another, saving staff time. Data transport is one of the most common RPA use cases.

Lower need for VDI: Because the Kofax RPA manages the UI from a central location, the system needs fewer virtual desktop infrastructures, which cuts costs and allows for easier scaling.

REST/SOAP interface: Kofax’s representational state transfer/simple object access protocol (REST/SOAP) interface allows easy embed of automations into third party applications. This is particularly helpful as companies use third party apps to enable scalability.


  • Very strong in data extraction from documents.
  • Can use APIs to mine and migrate data from any number of sources.  
  • Relatively easy re-use/re-deployment of existing automations, which enables faster deployments in new situations.


  • Some concerns with customer support.
  • Could use more focus on debugging applications.


NICE is well suited for a growing niche: large enterprise that need attended automations. That is, automations with human in the loop – the so-called “augmented” human staff member. These attended automations are particularly useful for large enterprise with a call center to maximize. For large enterprise clients, another plus for NICE is its long tenure as a company – almost two decades as an automation vendor.

Additionally, NICE has a solid record with unattended workloads, which remains the more common customer need in the RPA market. NICE is focused on analytics (yet another good tool for call centers), which allows close monitoring of how well a given set of bots/automations is achieving ROI.

Key RPA Features

Specialized attended: The company’s Advanced Process Automation offers vertically-oriented attended automations to serve various industries, including manufacturing, banking and telecommunications.

Real time call center: The NICE platform allows managers to track each individual sales rep in the call center, using a bot to offer suggestions, assistance and support as that rep handles sales calls.  

Automation coupled with analytics: The company’s NICE Employee Virtual Attendant integration and Shape Analysis (NEVA) tool uses an analytics grid to check for performance over time, both for unattended and attended automations. This kind a RPA governance tool is very useful for improving ROI.


  • Known for good customer support.
  • RPA solutions offered via virtually any format, from on-premise to SaaS, private or public cloud.


  • AutomationDesigned, a core platform component, requires developer skills to fully maximize.
  • Since it’s designed for large enterprise, the initial set up can require the expert staff that these larger companies tend to have on-premise. 

Another Monday

Another Monday will appear attractive to SMB and enterprise clients that are newer to the RPA market, because the company offers a usage-based cost structure. Clients pay for automation instances that are successful, a key selling point (clients pay a micropayment per successful transaction). Given that a percentage of the RPA market struggle for true ROI, this offer should continue to help Another Monday attract companies of various different sizes and verticals.

The company supports its platform as a managed service, which means that Another Monday can play a hands-on role with its clients. For larger clients, like banks and telecoms, Another Monday will very directly support the platform. This approach isn’t best for every client; many companies prefer to buy an RPA solution via the cloud or SaaS, and manage it only with in-house staff.

That said, Another Monday works for those clients that seek a comprehensive solution, as opposed to an “experiment and grow” approach that starts small and scales. The company is strong in manufacturing and utilities. 

Key RPA Features

Includes third party bots: Another Monday’s system is built to oversee and manage bots from third party sources, which is an approach to RPA that enables great scalability. 

Encryption: To bulk up security, Another Monday has built encryption into its platform. This is a key feature, given that bots transfer data across an entire organization. 

Diverse pre-built elements: The company offers a cohesive menu of automated bots, allowing an end-to-end solution that handles an entire company’s RPA needs with less development time.


  • Platform offers strong bot governance features.
  • Set up for the likely future environment of RPA, where a vendor offers an all-encompassing integrated services approach.
  • Built with a decentralized architecture that needs no single application to launch and operate bots.


  • Relies on services support from Another Monday and its partners; some users may not prefer this.
  • A robust customer-created bot strategy in addition to its “all enterprise wide” strategy would be a plus.


Companies looking for an RPA solution with extensive attended features may select Pegasystems. An overwhelming majority of the company’s clients deploy these human-in-the-loop robot automations, which serve everything from call centers to diverse sales forces.

That said, Pegasystems’ real focus is Digital Process Automation (DPA), sometimes referred to as BPM (business process management). In fact, the company’s RPA is available as a no-cost upgrade for buyers that purchase a DPA solution. Although experts disagree, DPA is a more all-encompassing system that assists everything from business rules to document support, along with robotic process automation. This all-encompassing focus will appeal to some businesses, especially large enterprise, which have the need for such a robust platform. SMB are likely less of a fit.

An additional plus: Pegasystems has a large market presence, fully global, which suits a multinational client.

Key RPA Features

Easy deploy: Clients can get operational with relative ease, using Pega’s lightweight architecture that supports fast stand-alone robotic automations.

Visual Studio Scripting: Using a system that watches and records human staffers use of applications on the desktop, the Pega platform will then send resulting operational data to Pega’s machine learning platform on AWS.

Multi-use robots: In an efficient methodology, Pega’s concurrent scheduling system enables unattended automated bots to assist in a variety of work situations, saving a customer programming time and virtual machines.


  • Growing commitment to unattended automations.
  • Advanced in NLP, mining email and chat interactions with user-programmed automations.
  • Strong presence in CRM, so a company sales staff will be assisted by its automations.


  • Customer support for RPA could be improved.
  • Bots can be programmed by a business analyst but that individual will need some scripting skills; not strictly low-code.

Additional RPA Market Leaders

The RPA companies in this Honorable Mention category are not any less worthy of consideration than any vendor than on this list. In a rapidly emerging market, with constant product evolution, these high profile vendors serve the automation needs of many customers – and are each likely to grow rapidly in the years ahead. Keep an eye on these companies.


The company’s ANTstein enterprise bot platform offers an array of automation modules, enabling the time-efficient list of reuseable services. This is a popular strategy in the RPA sector, and one that AntWorks takes up a step by incorporating NLP, extensive data capture and an ML engine in its solution. To its credit, the AntWorks approach stresses a wholistic approach to automation that incorporates several elements of automation and management rather than isolated tools.


Israel-based Kyron has RPA toolsets for both attended and unattended automations, and is focused on process discovery. This commitment to process discovery will likely serve its customers well – discovering when and where in the workflow to fill an inefficiency with a bot is critically important. The company is growing. With patents for machine learning and other core RPA tools, Kyron is a vendor to watch.


A smaller RPA vendor, Intellibot dashboard offers the full range of automation design, from chat tool and ML architecture to both attended and unattended bot. In an interesting twist, the UI offers a view of the software robots connected to one another, which allows designers a global view of the automated work process. Intellibot is known for significant capabilities at reasonable cost.


ServiceTrace offers RPA within a larger digital process automation (DPA) approach. With the goal of efficiency, the company’s automation process uses an assessment form that staffers use to describe the value and potential benefit of an automation. Additionally, oversight of both human staffers and automated bots is supported by metadata from a business process model.


Offering both on-premise and cloud-based RPA, AutomationEdge has a robust partner list (including large vendor BMC), which should aid product offering and growth over time. To ease deployment, the company offers hundreds of pre-built automations. Also in its favor: AutomationEdge offers a number of pricing levels to allow budget-strapped businesses to get on board with RPA.

RPA Vendor Comparison Chart

CompanyKey ProductsDifferentiatorsCost
UiPath ·   Intelligent Automation Cloud ·   A market leader in RPA sector· Acquisitions and diverse partnerships point to a growing portfolio·   Community edition is free. Other versions free for 60 days; then cost available upon request
Automation Anywhere·   Automation Anywhere Enterprise·   Discovery Bot·   IQ Bot·   Bot Insight·   Top RPA vendor known for fast implementation·   Extensive R&D staff suggests rapid product advances·   Community edition is free. Enterprise cost available upon request
·   AssistEdge platform ·   Owned by Infosys, so strong for Infosys clients·   Has division dedicated to AI and ML·   Community edition is free. Enterprise cost available upon request
Blue Prism·   Intelligent RPA Platform
·   Blue Prism Cloud
·   Digital Exchange
·   Discovery Tool

·   High profile RPA vendor, strong in unatttended·   Agile and MVP services·   Solid AI implementation·   Free trial available. Cost available upon request
Softomotive·   WinAutomation·  ProcessRobot·   Good RPA choice for SMBs·   BI Drag and drop user interface·   WinAutomaton is $89 per month; ProcessRobot cost available upon request
WorkFusion·   Intelligent Automation Cloud
·   Deep commitment to AI and ML·   Strong in analytics·   Express version (1 bot only) is free. Cost available upon request
Kofax·   Kofax Intelligent Automation·   Kofox RPA
·   Single container system helps lower costs·   Good for handling unstructured data·   Available upon request
NICE·   NICE Automation Studio·   NICE Robotic Automation·   Top choice for enterprise attended RPA·   Good tools for vertical integration·   Available upon request
Another Monday·   AM Ensemble·   Offers RPA as a managed service·   Strong in security·   Based on usage; micropayment per transcation
Pegasystems·   Pega Robotic Process Automation·   Pega Robotic Desktop Automation, Pega Workforce Intelligence·   Top offering for attended RPA·   Focused on Digital Process Automation
·   Available upon request

Traders, prepare to adapt.

Wall Street is entering a new era. The fraternity of bond jockeys, derivatives mavens and stock pickers who’ve long personified the industry are giving way to algorithms, and soon, artificial intelligence.

Banks and investment funds have been tinkering for years, prompting anxiety for employees. Now, firms are rolling out machine-learning software to suggest bets, set prices and craft hedges. The tools will relieve staff of routine tasks and offer an edge to those who stay. But one day, machines may not need much help. It’s no wonder most of the jobs Goldman Sachs Group Inc.’s securities business posted online in recent months were for tech talent. Billionaire trader Steven Cohen is experimenting with automating his top money managers. Venture capitalist Marc Andreessen has said 100,000 financial workers aren’t needed to keep money flowing.

This map of trading automation is based on interviews with about a dozen senior banking and investing executives on Wall Street, many of whom focus on adopting new tech. It offers a sense of their projects — some of them just starting — that will affect traders within big firms. ALREADY USING EXPERIMENTING POSSIBLE WON’T GET THERE ML = Machine Learning NLP = Natural-Language Processing RPA = Robotic Process Automation PA = Predictive Analytics


The art of dealing in bonds and more bespoke types of credit has proven far more challenging for computers than their much-faster takeover of stock exchanges. Infrequent or opaque trading left humans to negotiate prices, and banks must carefully juggle holdings to minimize the burden on balance sheets. Advancements in natural-language processing, data collection and machine learning are helping to overcome hurdles. CREDIT/SALES CREDIT/TRADING ML NLP ML

Justin Chin/Bloomberg


The long-running shift to electronic currency trading is getting an upgrade. Firms are tapping big data and machine learning to anticipate client demand and price swings. Software also is helping to design and manage banks’ inventory of more complex rate swaps and currency derivatives. RATES/TRADING RATES/SALES FX/TRADING FX/SALES ML ML NLP NLP RPA ML

Krisztian Bocsi/Bloomberg


From highly liquid contracts tied to assets like gold and oil to the physical commodities themselves, the diverse world of commodities doesn’t always lend itself to automation. So banks are working on cataloging trader and salesperson conversations to create profiles of clients to help better anticipate their desires. SECURITIZATION/TRADING SECURITIZATION/SALES COMMODITIES/SALES COMMODITIES/TRADING ML NLP ML

Hannelore Foerster/Bloomberg


Equities trading, which shifted decades ago to electronic platforms, is one of the first testing grounds for using artificial intelligence to execute orders. PRIME SERVICES/SALES PRIME SERVICES/TRADING CASH/SALES CASH/TRADING DERIVATIVES/SALES DERIVATIVES/TRADING NLP RPA ML ML NLP RPA

Michael Nagle/Bloomberg; Martin Leissl/Bloomberg


Hedge funds and asset managers are using predictive analytics for tasks such as timing stock purchases and assessing risk based on market liquidity. Computers are also digesting vast data sets — everything from car registrations to oil-drilling concessions — to help predict how stocks will perform. EQUITIES/PORTFOLIO MANAGER EQUITIES/ANALYST BUILD MODELON SECURITY ONGOING RESEARCH PITCH TRADE IDEAS IDENTIFYTRADES PORTFOLIOCONSTRUCTION MONITOR TRADES EXECUTION ML PA ML PA ML PA ML PA ML PA NLP ML PA NLP ML PA NLP

Fabrice Dimier/Bloomberg


Vast spreadsheets, such as breakdowns of mortgages packed into bonds, are nothing new for credit funds. But some are teaching computers to scan and understand a much larger universe of bond covenants, legal documents and court rulings. Still, fully automating analysis of contract and illiquid assets underpinning securities in opaque markets remains a challenge, for now.


James Leynse/Corbis via Getty Images


Firms are trying to build economists. They’re toying with natural-language processing to sift central bank commentary for clues on future monetary policy. They’re also experimenting with algorithms that scour far-flung data, like oil-tanker shipments from the Middle East or satellite images of Chinese industrial sites, to forecast growth. MACRO/PORTFOLIO MANAGER MACRO/ANALYST BUILD MODELON SECURITY PITCHTRADE IDEAS ONGOING RESEARCH IDENTIFYTRADES MONITORTRADES TRADEEXECUTION PORTFOLIOCONSTRUCTION ML PA ML PA ML PA ML PA ML PA NLP ML PA NLP ML PA NLP

Matthew Lloyd/Bloomberg

Just this morning, robotic process automation (RPA) firm Blue Prism announced enhancements to its platform. A little later, the company, which went public on the London Stock Exchange in 2016, announced it was raising £100 million (approximately $130 million) by issuing new stock. The announcement comes after reporting significant losses in its most recent fiscal year, which ended in October.

The company indicated it plans to sell the new shares on the public market, and that they will be made available to new and existing shareholders, including company managers and directors.

RPA Automation

CEO Alastair Bathgate attempted to put the announcement in the best possible light. “The outcome of this placing, which builds on another year of significant progress for the company, highlights the meteoric growth opportunity with RPA and intelligent automation,” he said in a statement.

While the company’s revenue more than doubled last fiscal year, from £24.5 million (approximately $32 million) in 2017 to £55.2 million (approximately $72 million) in 2018, losses also increased dramatically, from £10.1 million (approximately $13 million) in 2017 to £26.0 million (approximately $34 million), according to reports.

The move, which requires shareholder approval, will be used to push the company’s plans, outlined in a TechCrunch article earlier this morning, to begin enhancing the platform with help from partners, a move the company hopes will propel it into the future.

Today’s announcement included a new AI engine, an updated marketplace where companies can share Blue Prism extensions and a new lab, where the company plans to work on AI innovation in-house.

Bathgate isn’t wrong about the market opportunity. Investors have been pouring big bucks into this market for the last couple of years. As we noted, in this morning’s article, “UIPath, a NYC RPA company has raised almost $450 million. Its most recent round in September was for $225 million on a $3 billion valuation. Automation Anywhere, a San Jose Robotic Process Automation (RPA) startup, has raised $550 million including an enormous $300 million investment from SoftBank in November on a valuation of $2.6 billion.”

Automation Anywhere, a San Jose-based company that makes automation software, said Thursday that it has raised $290 million in Series B funding at a valuation of $6.8 billion.

The latest investment — just a year and a half after the San Jose-based company raised its first-ever venture round — turns Automation Anywhere into one of Silicon Valley’s highest-valued unicorn startups and follows on a $300 million funding round a year ago that valued the company at $2.6 billion.


“Never before has there been such a transformative shift in the way we work, with artificially intelligent software bots changing how people, processes and technology interact for productivity gains,” Automation Anywhere CEO Mihir Shukla said in a statement. “This new funding reinforces the promise of the RPA (robotics process automation) category and empowers our customers to achieve greater business agility and increased efficiencies by automating end-to-end business processes – bridging the gap between the front and back office.”

Series B

The Series B funding was led by Salesforce Ventures, the venture capital arm of San Francisco-based Salesforce.com Inc. “Automation Anywhere makes it easier for Salesforce customers to automate repetitive, manual tasks and focus on what matters most — the customer,” Bill Patterson, executive vice president and general manager at Salesforce Service Cloud, said in a statement.

Additional funding came from existing investors that include Goldman Sachs and SoftBank Investment Advisers, which funded the company’s November 2018 round. At that time, Shukla told the Silicon Valley Business Journal that Automation Anywhere envisions that “this technology will change how we work, across all industries and across every function.”

He said last year that Automation Anywhere, which changed its name from Tethys Solutions LLC in 2010, was profitable before, but that explosion of bots that are automating business processes created a big new opportunity. He also said that the SoftBank investment opened up “the possibility of partnering with many of the portfolio companies that SoftBank has invested in that are having the same sort of impact.”

The funding comes on the heels of massive layoffs at UiPath, Automation Anywhere’s New York-based rival. Shukla recently published a blog post assuring customers that, unlike Uipath, AA has been hiring and said the RPA market is “extraordinary,” adding that successful competitors are good for business.

“We want and need the market to be competitive because especially competitive markets are always better for customers and partners,” he said at the time. “Working with our vast partner ecosystem, we’re on a journey unlike any other. Journeys like this are never without their twists and turns. But, on this journey, we plan to set untold records.”

As lawmakers in Brasilia debated a controversial pension overhaul for months, a robot more than 5,000 miles away in London kept a close eye on all 513 of them. The algorithm, designed by technology startup Arkera Inc., tracked their comments in Brazilian newspapers and government web pages each day to predict the likelihood the bill would pass.

Weeks before the legislation cleared its biggest obstacle in July, the machine’s data crunching allowed Arkera analysts to predict the result almost to the letter, giving hedge fund clients in New York and London the insight to buy the Brazilian real near eight-month lows in May. It’s since rallied more than 8%.

Brazil's Lower House Votes On President Bolsonaro's Flagship Pension Proposal
Lawmakers supporting the pension reform bill wave Brazilian flags in the lower house of the National Congress in Brasilia on July 10.Photographer: Andre Coelho/Bloomberg

This is the kind of edge that a new generation of researchers are betting will upend the research marketplace. For Arkera’s clients on Wall Street and in the City of London, that means getting robots to filter through the noise in faraway lands.

“There’s too many people to follow on Twitter, too many websites, too many articles,” said Nav Gupta, the 48-year-old co-founder of Arkera, which says its software can process as much information as 1,000 human analysts. “That’s a very expensive problem and everybody faces it.”

The company raised 4 million pounds ($4.9 million) last year from investors including Alan Howard of hedge fund Brevan Howard Asset Management LLP.

Using so-called artificial intelligence to automate swathes of the research process is quickly gaining traction because cost-conscious investment banks are downsizing. In the U.K. alone, there was a 30% drop in research budgets last year, Financial Conduct Authority data show. At the 12 biggest banks, there’s been a 7% drop since 2015 in the number of front-office staff covering currencies, such as traders and researchers, according to London-based research analytics consultancy Coalition Development Ltd.

That means it’s even harder than it used to be to afford analysts on the ground in developing nations, about the only places in the world where investors can get yield right now.

Data-science companies like Arkera and New York-based Sigmoidal say they can solve this problem using machines that learn as they go to dredge through tens of thousands of news articles, government statements and social media accounts in languages as varied as Spanish, Arabic and Chinese.

After an initial investment of up to $100,000, banks can save $1 million over seven years using such systems because they don’t need to hire as many data analysts, said Marek Bardonski, who was chief executive officer of Sigmoidal when he spoke with Bloomberg in July. He has since left the company. Previously, Bardonski, 27, was a computer scientist at graphics chipmaker Nvidia.

Take this year’s protests in Hong Kong. Bardonski said Sigmoidal’s software was able to track developments in the Cantonese-language press and even identify the non-verified Twitter feeds of protest leaders to monitor the risk of further unrest. The technology is useful for far-away countries wracked by political turmoil, places where investors are keen to put money but don’t have easy access to information.

“The system can give an edge over traditional analysts working for financial institutions,” said Bardonski, who said typical reports will include charts on sentiment, key word statistics and short written summaries. “Instead of getting 100,000 news articles, clients can get all the insights on one page.”

Neither Sigmoidal nor Arkera would let Bloomberg see an example of an automated report to see how readable it is compared with one produced by a human, citing rules against sharing proprietary data. 

But the quality has gone downhill because mid-level analysts have left or been pushed out, leaving junior analysts to do the work so their more senior colleagues can go to client meetings. This is giving investors even more impetus to seek out bespoke research, like paying cash to speak with experts in the field or investing in automated research to support their senior fund managers and strategists.

“Asset managers now need to assess the value of every single research service to assess if it’s worth paying for, how much they should pay for it, and trying to filter the good from the bad,” Mahmud said.

relates to Robots Are Solving Banks’ Very Expensive Research Problem
Vinit Sahni and Nav GuptaSource: Arkera

Under MiFID II, asset managers must be prepared to demonstrate they’ve done due diligence on all investments they make for their clients, something that’s always been tricky in developing countries.

It was that very problem that inspired Gupta and his business partner Vinit Sahni, whose careers spanned firms including Citadel LP, DE Shaw & Co. and Goldman Sachs, to set up Arkera in 2015. During their 20-year careers in investment banking, trying to find information to substantiate something felt “like pulling teeth,” Sahni, 50, said.

So the pair set up a team of data scientists and engineers to design a search engine that investors can use to give them an edge in places like Turkey, Mexico and Egypt. It works kind of like Google, only it’s programmed to choose the most relevant sources from tens of thousands of articles, social media feeds and government releases.

As good as robots are getting at deciphering market jargon, even their developers admit they’ll never fully replace humans. In the next decade, Sahni said smart machines will significantly enhance the capabilities of human analysts.

“We will see advancements in cognitive abilities, communication and the physical potential of humans as we collaborate closely with machines and algorithms,” he said.

Local SEO strategies may seem like they’d rely less on AI (artificial intelligence), but in reality they need it to fully thrive. Here’s why.

AI barely played a role in thebeginning of online search engines. The earliest search engines accepted payments from companies that wanted to be at the top of the listings. However, AI is becoming more important than ever.

Google is using AI to assign SERPs more than ever. Savvy SEO strategists are also using AI too. AI is also important in local SEO, because businesses need to know how to automate essential functions and identify approaches that get the best results.

How AI is Central to Modern SEO

Search Engine Journal has discussed the role of AI in modern SEO. They said that AI has the following five benefits:

  • AI helps optimize keywords better
  • AI uses cluster analytics and predictive analytics to audit pages and identify search terms that will be popular in the future
  • AI helps leverage customer reviews
  • AI predicts customer needs
  • AI drives customers to sales with better engagement

All of these AI factors directly or indirectly help with SEO. You should know how to use AI to build an SEO strategy for a local business.

Creating a Local SEO Strategy with AI

Nearly everyone searches Google to find a local business or services near them. In fact, nearly 50% of all Google searches are for local business information. While SEO professionals know the importance of incorporating organic SEO services that utilize AI into their marketing plan, especially to businesses with a physical location, there are still a lot of small businesses who do not include local SEO tactics in their marketing plan.

Create a Google My Business listing.

AI can help you make a Google My Business listing more quickly. Google My Business is a free tool for businesses and organizations to help establish and manage a business’s web presence across Google. GMB connects your business with customers.

Go to Google My Business and claim your GMB page. If you haven’t done this already, Google will send you a verification code for your address. You will take that code and follow Google’s instructions to enter the code into Google My Business so that Google can verify your business, which can take 1-2 weeks. You can use AI to make sure everything is done properly, which expedites things.READHow To Use Artificial Intelligence To Create Websites That Thrive

After you have verified your Google My Business you will then need to:

  • Ensure your name, address, and phone info is correct
  • Select the right category for your business
  • Complete your business’ description
  • Add your hours of operation
  • Upload hi-res images
  • Ask your customers/clients to write reviews for your business

Make Sure Your NAP is consistent across all platforms.

Your name, address, and phone number should be the same no matter which page of your website or other social platform your business is on. Check your Contact Us page, Home page, Footer, and social media platforms. You can use AI to identify inconsistencies and rectify them.

Use a Google Map on your website.

AI has improved user engagement, which includes offering Google Maps. Having a Google map on your website for your physical location is a given. You want visitors to be able to find you. Embedding a Google Map onto your website is a simple tactic that a local SEO company would likely use.

Use Geotags to share your location with search engines.

AI also has made geotags possible. Geo tags assign a geographical location to a photo, video, website, and social media posts. Use geotags on your specific location websites. They let search engines where you are and help improve your local business search ranking. You can use a Geo Tag Generator and place it on each page of your website.

Use schema markups.

Schema markups help search engines better understand the content on your website so that your business will appear in more relevant local searches. You can use schema markups for NAP, events, specific categories and more. You can use a Schema Markup Generator or (JSON-LD).

Optimize your website and meta tags for local keywords.

You can boost your clickthrough rates by including your business’ city and state in your meta description tags on your website, page content, logo, and images.

If you have multiple locations, create a separate web page for each of them.

If you have more than one physical location, create a separate web page for each location. You can create a specific subdomain for your various locations. You want to make sure that the content for each location’s website is unique. You could include content like:

  • Testimonials from clients specific to the location the website is for
  • Highlight services or products that you offer in one location that are not available in your other locations
  • Create a location specific blog, making sure you use alt tags for images and videos.

READAre SMEs Equipped To Master Data Science?

Submit your website to local directories.

You want your business to be listed in as many online directories as possible. You can use a tool like Yext to find many, if not most, of the locations of your existing citations and update your info so that your NAP is consistent across all business listings. If you have multiple locations, you want to make sure your location-based subdomain corresponds with the right location.

Gather as many reviews from customers and clients as possible.

Customer reviews from credible review sites like Yelp, Google My Business, and Facebook can have a positive impact on your Google local search rankings.

Update your business’ social media profiles with your location.

Your business’s location should be on every single social media platform that your business uses. You should always include your contact information so that potential customers and clients know how to reach or where to go. Make sure the information you provide on your social media pages is consistent with the information on your website. Having active social media channels could have a huge impact on your local rankings because it’s a sign that your business is credible.

If you still don’t feel confident in handling your local SEO marketing on your own, seek help from one a top tier internet marketing company who offers affordable SEO services with more than ten years’ experience and hundreds of satisfied customers.


“If people design computer viruses, someone will design AI that improves and replicates itself. This will be a new form of life that outperforms humans.”

The rise of Machines and AI  is Fast Approaching

The rise of the machines was always going to come at a cost, as each wave of technology destroys what has been put in place. The frenetic pace of technological development in the areas of artificial intelligence and robotics is causing massive shifts in the finance industry.

The impact is already being felt on Wall Street, where there have been far-reaching and significant changes, with technology eliminating many jobs by replacing hundreds of humans with either robo-advisers or software.

Many investment banks and big institutions, in a bid to cut cost and improve efficiency, use AI to automate financial tasks usually undertaken by humans, such as wealth management, operations, risk management, and algorithmic trading.

One of the areas that the Wall Street has seen the greatest disruption of robotics and AI is the execution of buy and sell orders, where robots carry out between 50% to 60% market trades, according to CNN, citing data from Art Hogan. Hogan is the chief market strategist for B. Riley FBR.

The preference for technology over human traders is premised on the inability of the vast majority of traders to act consistently rationally when trading. Often, people fail to control themselves, allowing emotions to get in the way of their thoughts and actions.

Machines don’t suffer from these psychological issues when a major trading decision is being made. This is because they remove emotion from short-term trading activity, allowing for a more objective approach to trading.

Laying a Claim to the Future of Investing

Artificial intelligence and robotics is fast advancing into the investment sector, where its incredible ability to learn and think will eventually make them the most advanced and complex investment systems capable of helping corporations to make more efficient and effective choices.

For instance, advisory bots are increasingly being used by companies to assess investments, deals, and strategies in a fraction of a second, much faster than any human quantitative analysts using traditional statistical tools.

Such is the growing dominance of robots in the finance industry that former Barclays boss, Anthony Jenkins warned they could displace half of the workers in the banking sector, and lead to branch closures.

Activities such as calculations based on structured data and other repetitive support tasks are the most susceptible to automation because robots are well-suited for them.

There’s Still a Place for Humans in a Robotic Wall Street

While AI is probably the most robust technology there is today; its ability to perform complex tasks is limited. Trading machines can only learn historical data and trade patterns. However, stock market behavior changes all the time and computers can be less adept in the face of unexpected market performance.

Humans can easily adjust themselves to these changes. Getting the robots to do the same, however, will require changing their algorithms, which can be too expensive and time-consuming. For this reason, humans will always be a step ahead and remain relevant.

BNP Paribas takes a leaf out of Silicon Valley’s book with a Siri-style digital trading assistant, which is launching alongside real-time market analytics and interactive algorithms as part of major upgrade to the bank’s Cortex FX trading platform.

BNP Paribas has unveiled its newly revamped FX algorithmic trading platform following a major upgrade, which includes the launch of interactive algo­rithms and real-time analytics delivered via an artificial intelligence-based digital trading assistant known as ‘ALiX’.

ALiX, which BNP Paribas said is the FX industry’s first digital trading assistant, will deliver content and running commentary on execution to traders using the bank’s modernised Cortex LIVE platform. As market participants continue to battle with the onslaught of data volumes, ALiX will digest market information, presenting tangible options to improve algo execution mid-flight, effectively giving BNP Paribas’s FX algo­rithms a voice.

“We’ve taken a leaf out of Silicon Valley’s book with ALiX,” says Asif Razaq, global head of automated client execution (ACE) at BNP Paribas. “Similar to the concept of Alexa or Siri, ALiX was designed to focus on financial markets. These assistants are solving multiple workflow problems. ALiX is designed to be your personal trading assistant, where it will monitor multiple live market data feeds with­in our real-time analytics portal.

“ALiX will also intelligently inform the client of any key market events and present context relevant options to choose from, ulti­mately providing the client with an intelligent roadmap of what to do next.”

Alongside ALiX, BNP Paribas has launched its Cortex LIVE platform with a real-time market analytics portal, known as Insight LIVE, which the digital trading assistant will use to deliver market content, data and intel­ligence to traders.

Insight LIVE builds on the FX pre- and post-trade transaction cost analysis (TCA) that BNP Paribas already offers clients through its Insight portal, however, the Insight LIVE data has been expanded to in­clude real-time information to bridge the gap between the pre- and the post-trade TCA.

Navigating the execution flight path

BNP Paribas launched its FX single-dealer platform, Cortex, in 2013 at a time when FX markets were undergoing a major evolution following the introduction of algorithmic trading and electronic trading platforms. FX markets first adopted the basic TWAP and VWAP algo trading strategies from equi­ties, but as the arrival of multiple new FX execution platforms caused fragmentation in liquidity, second generation algorithms were rapidly developed to aggregate that liquidity across numerous venues.

Although slightly late to the party, BNP Paribas says it pioneered the third generation of FX algorithms. Razaq, who has been part of the FX algorithmic trading team at BNP Paribas for almost a decade, was tasked with developing the bank’s expedition into FX algorithms and soon developed what would become the third generation algo: adaptive algorithms.

Razaq says he deployed basic forms of AI technology and real-time insights so that the algos can self-adjust when working a trade. At that time, BNP Paribas launched two adaptive algorithms, Chameleon and Viper, providing clients with both aggressive and stealthy algo trading strategies when the Cortex platform first went live.

“Execution algorithms have grown in use in the FX space, whereas in the equities space, they’ve existed for a very long time,” Razaq adds. “FX algorithms were more difficult to build due to a lack of transparent data and fragmented liquidity. As we saw the elec­tronification of the FX market take place, we started to develop models to gather market intelligence and were able to build algos that adapt in real-time.

“It was a greenfield project in terms of having a blank slate, and this is where I really wanted to utilise my expertise in artificial intelligence. Can I build something using basic AI techniques that will give us an edge, to improve on what the market is doing with the first and second generation algos? We also wanted to simplify the algo journey, because clients think of algos and see complexity. Sim­ plicity was key. When BNP Paribas launched its algo platform, one of the key objectives I set myself was not to overcomplicate the algo selection process for the client.”

Simplicity has remained a key part of Cortex following the revamp, and rather than build­ing several different algorithms, BNP Paribas focused on enhancing its existing Chame­leon and Viper algo strategies. While clients found value in the self-piloting and adaptive algorithms BNP Paribas had developed, the bank says many wanted to be more involved in the algo execution process. To meet this demand, BNP Paribas has introduced what it is referring to as the ‘fourth generation’ of FX algorithms: interactive algos.

The interactive algorithms provide a view of the data it is processing and reacting to from the Insight LIVE market data hub, allowing clients to consider various options for order execution. But, rather than running the algo on autopilot, as has traditionally been the case, traders are handed the controls to turn off autopilot and navigate the execution path, based on real-time analytics delivered via ALiX.

The four pillars

The FX algo trading team at BNP Paribas focused on four pillars when building Cortex LIVE; user experience, market analytics, integration and intelligence. For user expe­rience and integration, market participants have a multitude of single-dealer platforms to choose from, all of which are competing aggressively for client desktop real-estate. Expert user interface developers were brought in by BNP Paribas to examine the Cortex platform and map out the user’s journey to identify where that workflow could be streamlined or simplified.

Simultaneously on the client side, firms are upgrading or implementing digital infrastruc­tures to automate their own workflow, so BNP Paribas says it is imperative that Cortex LIVE can be integrated within the whole FX ecosystem and with client workflows. In order to achieve this, BNP Paribas teamed up with financial markets operating systems specialist and industry disruptor, OpenFin.

 “Cortex Live is the first external platform that BNP Paribas has put on OpenFin, but the bank has been a supporter of OpenFin in terms of internal use cases for many years,” explains Adam Toms, CEO of OpenFin Europe. “With the development of the new major integration process platform, BNP Paribas adopted some of the best components that OpenFin has to offer, including the FDC3 interoperability stan­dards and user experience. It is a big step for­ward and, frankly, any organisation moving forward with interoperability is going to be a market leading vendor.

“It’s encouraging to see, given my back­ground and that FX has historically been an asset class with a lower level of transparen­cy, that BNP Paribas has made a real push with transparency, with real-time TCA and in-depth venue analysis. That is a powerful differentiation.”

Onboarding clients, particularly the more traditional asset managers, to a new plat­form can be a headache in terms of legal paperwork and getting the system onto client desktops. To address this, BNP Paribas has ensured that the Cortex LIVE platform is ac­cessible and downloadable via the app store on Bloomberg, which means that clients can download the platform within the Bloomberg ecosystem without having to go through a major integration process. For those that often struggle to keep track of usernames and passwords, Cortex LIVE clients will not even have to log in, as Bloomberg is able to authen­ticate a client’s system for easy access.

“Cortex LIVE being available via Bloomberg removes that integration barrier, and that’s why we expect the adoption rate of clients consuming Cortex LIVE to be significant,” says Nick Hamilton, head of EMEA eFX sales at BNP Paribas. “It streamlines and simpli­fies workflow. Clients just have to download the platform from the Bloomberg app store and they’ll have access to all of the enriched functionality.”


With the OpenFin integration, BNP Paribas claims it is ‘future-proofing’ the new platform and, eventually, systems on the client side will be able to communicate with BNP Parib­as’ digital trading assistant to create a fully automated and integrated trading platform. Although the FX industry is not quite at this stage yet technologically speaking, BNP Pari­bas has laid the foundations to take ALiX and Cortex LIVE a step further.

In the early days, ALiX will be fed with basic responses, but the machine will self-learn and be able to respond, building a rapport with the client as they ask the digital trading assistant more questions. As ALiX is given more data its intelligence will grow and broaden its remit from guiding clients not just through their FX algo trading execution, but for all FX activities.

“The vision is that we will broaden the ALiX concept as a holistic solution across the FX execution environment for all of your activities as a user on Cortex,” says Joe Nash, digital FX chief operating officer at BNP Paribas. “Then it becomes more than the execution control point for the algo business, it will be a digital trading assistant for every­thing you’re doing in the FX space. Pushing content, trade ideas, figuring out when you expect to put a particular trade on and provide pricing to you in that regard, there’s a whole sphere of functionality that we can bring to the FX space through ALiX.”

In the long-term, BNP Paribas aims to take the new technologies in the form of ALiX and the new functionality and analytics on Cortex LIVE to other financial instruments and asset classes. Cortex LIVE, ALiX, as well as interactive algos Chameleon and Viper, could soon embark on a journey into equities or fixed income. BNP Paribas says this expan­sion is being considered amid a trend towards multi-asset trading in asset management.

“We are now looking to extend our award-winning algo execution service to other asset classes such as fixed income, futures and so on. This is against the backdrop of the buy-side consolidating their activities and forming multi-asset trading desks. Eventually, we want ALiX to become that multi-asset BNP Paribas trading assistant,” Razaq concludes.