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Fintech in the FICC Market

A closer look at trends in the FICC market:


Automation and AI are one of the drivers of competitive differentiation in capital markets—in the front and back office

The FICC business within the investment banking sector has been slow to experience disruptive technologies, largely due to the human based nature of the advisory work. The trading aspects has been awash with multiple algorithmic solutions over the past two decades, although most of these have been computational in nature using better processing capabilities of existing software and hardware. Many banks have also scaled back FICC desks due to post-crisis regulation, higher operating costs, and a shrinking revenue pool.

However the emergence of new technologies such as intelligent automation and AI can be a driver of competitive differentiation in capital markets—in the front and back office. Front-office technology innovation, especially cognitive automation, can bring efficiencies and possibly new sources of growth. In the back office, the industry can confront a smaller and unpredictable revenue pool by mutualizing post-trade overhead across participants. Banks that build the right capabilities and make the strategic choice to ride out near-term pressures to stay with the FICC business could see big pay-offs.

True externalization—in which the infrastructure and the operations are run by a third party—may be a solution due to smaller revenue pools Engaging specialized technology-enabled providers can be one way to more profitably manage these businesses, e.g. partnering with fintechs. The migration to electronic trading in high-margin products, like interest-rate swaps and greater price transparency with reporting requirements could result in increased margin pressure and new technologies could mitigate the increase in these costs.

Many investment banks and buyside firms are investing in artificial intelligence to aid with pattern recognition, with the goal of deriving alpha-generating insights. Hedge funds and asset managers have often looked at algorithmic trading and machine learning techniques. Intelligent automation is being used to create leaner front offices, e.g. bots to trade odd lots in corporate bonds, tech to consolidate e-trading assets and capabilities.

Financial institutions that invest in technology operate more efficiently and are more productive, particularly in less-commoditized business lines such as FICC. Classes such as Rates are still characterized by voice-dominated trade workflows – it is estimated that productivity gains from investment in FICC tech can be three times higher than the same investment in Equities.

Some of the fintechs (anonymous) that offer FICC solutions are:

  • Fully automated insurance for FX, interest rate and commodity price risks
  • Simplifies trade decisions using intelligent automation like machine-readable regulatory text and machine-executable implementation
  • AI driven company technology to allow financial professionals to perform complex, multicondition analysis of large-scale data sets
  • A fully-hosted platform for portfolio, order and execution management system for both the buy- and sell-side, and connectivity and risk solutions to provide a flexible, cost-effective platform for use across counter-parties, asset classes and geographies.
  • Uses machine learning to offer fixed income advisors smarter recommendations, enhanced client engagement, and accelerated trade flows.
  • Track, manage and analyze bond investments through intuitive, multi-device digital platforms.
  • Standardises, improves and automates traditional bond trading through a SaaS platform that maximises productivity and information distribution while meeting increasing compliance requirements
  • Provides cross-market information on liquidity and trade intent by giving the buy-side a real-time view, allowing them to see signal data from their dealers in bonds they are actively trying to trade
  • An AI engine to identify and map customer behavior by analyzing customer generated data and then using the data to match customers with products & services that would most likely be of value to them.
  • An eTrading technology company providing solutions for the Fixed Income and Derivatives Markets
  • Data analytics company with the aim of allowing financial institutions to unite all of their (FICC) trading data into one powerful, real time viewpoint
  • Platform that uses machine learning to deconstruct all trades into their underlying risk factors, suggesting alternative methods to gain the same risk factors in a potentially more cost effective manner.

Case study
Kensho helped Goldman Sachs to pivot from trading (a high-profile yet relatively small piece of big banks) to using natural language algorithms to the entire firm.

  • Kensho's technology is now used in Goldman's $1.5 trillion asset management division, where a custom built "cross correlation engine" tracks investors' correlations between various assets.
  • Kensho now also sends out a Sunday night report called the "Kensho Weekly," which offers A.I. driven contextual analysis of news flow, earning reports and analyst ratings changes. The open architecture of Kensho allows users to tweak variables as they like.
  • What made Kensho very well received by the Goldman Sachs population was that it made the user more powerful; it never disrupted the underlying business model. Staff still have to come up with their own analysis - it just makes them more powerful.
  • They consider it not so much as artificial intelligence as accelerated intelligence - doing very human things, which historically you thought were impossible for a human being to do, at a blinding speed.

For more information on any of the insights and/or Fintech solutions in this note, please contact Terence Singh at KPMG-Matchi.



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