Few industries are as ubiquitous and digitized as banking, which sits on massive amounts of clean, structured, financial data. Yet by and large, banks have maintained the same business model for centuries. The opportunity for disruption has enabled startups to flood the sector: Stripe, Apple and Square are changing how we pay for things, while digital currencies, peer-to-peer lending, ‘alternative-data’ providers and pay-as-you-go insurance platforms are threatening the universal bank model. In this essay, I investigate how an investment bank like Goldman Sachs can harness the power of its data through machine learning (ML). Due to its breadth of revenue streams, Goldman has the ability to address almost any area of ‘B2B’ finance, from investment banking, investment management, securities and lending. Nevertheless, monetizing B2B client data will be difficult given strong data protection and privacy agreements. As a result, I see Goldman Sachs making a strategic pivot towards consumer finance, where data constraints are less stringent.
In investment banking, strict NDA agreements limit the amount of proprietary data collected in any M&A transaction. As a result, the next frontier of process innovation will come from incorporating “alternative data” (e.g. credit card transactions, satellite images or weather forecasts) to develop proprietary insights on M&A opportunities. An investment bank armed with such insights would become a more strategic advisor to its clients and grow its market share as a result. A recent FinTech unicorn, Dataminr, is using big data from a variety of non-traditional sources to develop such insights, but it is struggling to incorporate these into the habits of professional advisors (i.e. 59% of Dataminr’s customers claim that ‘workflow integration’ is their biggest challenge). Given Goldman’s relationships with professional investors, where it is strongly embedded in their investment workflow, it has all the structural advantages to monetize this market.
When it comes to customer service, self-conversing bots are a major opportunity for ML to increase convenience and decrease SG&A cost. In private wealth management, Goldman’s clients are some of the wealthiest individuals in the world, but that is not a reason to overlook automation and bots. Using customer service bots would allow private bankers to spend more time with customers by automating basic support, data collection and automatic portfolio rebalancing based on customer requirement. Finally in lending, Goldman has proprietary data on millions of clients and their lending results. The potential of ML is to automate the underwriting process through stronger prediction algorithms that reduce bad debt expense, extend credit to higher-risk clients, and decrease administration costs.
In the short and medium term, Goldman will continue to expand its internal center of excellence of ML, set up in early 2018. So far, it hired the head of ML from Amazon, acquired dozens of FinTech companies with consumer data, and replaced its infamous equity trading team with computer engineers (i.e. The 600 equity traders from the early 2000s are all gone). In trading, ML algorithms predict which clients might be interested in specific investments and send quotes in real time. To succeed in the long term however, Goldman has recognized that it needs to create more proprietary data. Indeed, most machine learning algorithms are open source today, and anyone can use them free of charge against public data. Consequently, Goldman is incubating several initiatives aimed at collecting data from consumers, who are often less reticent than institutions to share data. It acquired three consumer startups in 2018 and developed an open trading platform called Marquee, which will be released to retail investors in Q4 2018. In addition, the firm recently released a new consumer lending platform, Marcus, to help consumers consolidate their credit card balances under one contract. Both platforms are run entirely by software with no human intervention.
For sustainable success in the next century, Goldman must not only develop proprietary insights from its proprietary data sets, but also incorporate these insights into the workflow of decision makers. Whereas machines are better at predicting very short term opportunities in reaction to tweets, earnings statements, or news, humans will maintain an edge in making long term predictions. As a result, humans will not be totally replaced and will need the ability to ‘overrule’ whatever the computer is saying.
To conclude, I would like to share some open-ended questions for the industry. We have seen how banks like Goldman are developing new consumer services in view of getting usage data, but how can they similarly leverage their B2B data? In M&A advisory for instance, Goldman mapped 146 distinct steps taken in any IPO, “some of which are begging to be automated”.
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