Investment banks advice companies in their assessment of capital needs. Serving as a bridge between the enterprise and investors, they recommend the best option to raise funds, being one of them the borrowing from the public through a bond issue. Among many hard decisions, they must help its clients decide on the volume and timing of the issuance, which includes the estimation of market demand. Although this task has remained unchanged for almost a century , UBS is now rethinking it through a Canadian tech startup – Overbond – which applies machine learning to connect all bond market participants (i.e. issuers, dealers, investors) in a digital platform, providing estimates for the timing and prices of a potential issuance . UBS, as many other companies, is embracing technology with the aspiration of improving its process and enhance its value proposition. However, given that banks have positions themselves over time as sustainable and lucrative businesses, why it is important for them to even start thinking about technology? What approach should they take towards technology to stand out from the arising competition?
Arguably the most important factor for a client in the bond issuance process is the gauge of demand, as it defines the price and timing of the bond. However, investment banks execute this task by analyzing the liquidity of the bond market or simply having a chat with the asset manager 1. In other words, they are taking the most important decision based on the “experience” of the asset manager. Therefore, machine learning will drastically increase the customer experience, granting them the confidence of data-based decisions combined with deep market insights. In this sense, artificial intelligence will enable the client to maximize the value of the transaction and make sure that he/she is not leaving money on the table. Therefore, it is crucial for UBS to start developing these capabilities, as machines and humans will be more powerful together than either is alone1.
Although it’s clear that UBS needs to be on top of cutting hedge technologies to remain competitive, there are several alternatives on how to approach this. The financial industry is facing more competition than ever, since numerous start-ups are arising as potential threats. Therefore, banks have basically two alternatives: treat them as enemies or allies. In this case, UBS chose the latter by establishing a win-win relationship with Overbond. UBS levered on its core capabilities and innovation-driven strategy to make this fintech start-up part of its business. From the start-up perspective, it is essential to have the support of a big bank to further develop the subjacent technology (i.e. machine learning), as it provides financial stability, strong networking opportunities and overall guidance.
Although applying machine learning in the bond marketplace is a key milestone for UBS, it is just a tiny step towards the infinite world of opportunities that this technology can provide. For example, they could roll out to other areas within the Investment Banking division. In 2016, J.P. Morgan introduces the Emerging Opportunities Engine, which helps identify client’s best positions for follow-on equity offerings through automated analysis of current financial positions, market conditions and historical data. In addition, as described in Deloitte’s report, banks could leverage on machine learning insights throughout the whole M&A life-cycle process. At an early stage, artificial intelligence enables a deep dive due diligence through a detailed analysis of the target company’s risks finding out the main issues to focus on. Secondly, analytics could make deal teams more effective throughout the negotiation process by providing insights on whether to pursue a specific target or move onto another one. Thirdly, machine learning could support the post-merger integration process by helping identify risks, synergies and other opportunities of value creation.
As financial institutions get involved at a fast pace into the technological world, there are several points to consider when evaluating these strategic decisions, from both the opportunities and limitations perspectives. First, since the value proposition of investment banks is relying more and more on data instead of expertise or regulatory knowledge, are we going to reach a point where these actors will become completely useless and companies will build these capabilities in-house? From the limitations perspective, James Manyika from the consulting firm McKinsey mentioned that one of the main practical issues of machine learning is data labeling and availability. In this sense, is it foreseeable that technology companies and banks would agree to share their information to achieve a greater benefit through machine learning, especially in an era when information is money? Time will tell. (word count: 750)
 “A Canadian startup applies machine-learning to corporate bond issuance”, The Economist, May 10th 2018, accessed November 2018, https://www.economist.com/finance-and-economics/2018/05/10/a-canadian-startup-applies-machine-learning-to-corporate-bond-issuance
 “Primary Bond Issuance: Digital Price Discovery”, UBS and Overbond, November 7th 2017, accessed November 2018, file:///C:/Users/socha/Downloads/overbond.pdf
 “Show me the case for AI in capital markets”, Accenture, November 2018, accessed November 2018, https://www.news.overbond.com/show-me-the-case-for-ai-in-capital-markets/
 “Machine learning is now used in Wall Street deal making, and bankers should probably be worried”, Business Insider, April 4th 2017, accessed November 2018, https://www.businessinsider.com/jpmorgan-using-machine-learning-in-investment-banking-2017-4
 “Not using analytics in M&A? You may be falling behind”, Deloitte, accessed November 2018, https://www2.deloitte.com/ca/en/pages/finance/articles/analytics-m-and-a-ia.html
 “The real-world potential and limitations of artificial intelligence”, Podcast McKinsey Quarterly, April 2018, accessed November 2018, https://www.mckinsey.com/featured-insights/artificial-intelligence/the-real-world-potential-and-limitations-of-artificial-intelligence