Marc

  • Student

Submitted

Developing Machine Learning and Proprietary Data Sets at Goldman Sachs
Marc
Last modified on November 13, 2018 at 2:27 pm
Goldman Sachs is a global investment bank, struggling to monetize customer data in a highly regulated environment. How are they getting around these restrictions?

Activity Feed

On November 14, 2018, Marc commented on Will 3D Printing Change the Future of Homebuilding and End Global Homelessness? :

Coming from San Francisco where homelessness is a huge issue, I think that construction is a top use case for 3D printing. However, I am curious to better understand the actual workflow of 3D printing a house (i.e. what raw materials are needed, where to rent/buy a machine, how long to transport it and set it up, etc.). It is hard for me to judge how realistic this use case is, and what the specific barriers to adoption are, without understanding the sequence of actions.
In addition, a significant proportion of the cost to build a new house relates to the land’s acquisition cost, and that trend seems to get stronger through time as more and more people more to the ‘mega-cities’. So to conclude, which niche would you choose to launch 3D construction?

On November 14, 2018, Marc commented on World War 3D: How success on tomorrow’s battlefield will depend on the agility and flexibility provided by Additive Manufacturing :

It sounds like the stakes to get additive manufacturing right are high, as it will provide the marines with enormous tactical and strategic advantages.
I am curious to understand how the military as a whole should look at ‘outsourcing’ vs. ‘insourcing’ the design of 3D printers, and how it can collaborate with the ecosystem to accelerate time to market of these technologies. Given that the military and the US government constitute a significant portion of the market opportunity, I assume that they have significant bargaining power to work with vendors and co-innovate.

On November 14, 2018, Marc commented on Teaching an Old Bank New Tricks: Open Innovation at Barclays :

Hi Annie,
Great job on a thorough and well-structured essay. I liked the graphs and examples of recent successes. In terms of how these startups can impact the bottom line at Barclays, I am curious about which use cases or topics need to be prioritized.
My takeaway is that all companies should look at instituting the “mod” model to crowdsource product enhancements from their customers.

On November 14, 2018, Marc commented on Future-Proofing Financial Services: Barclays Accelerator Program and Open Innovation :

Interesting read on the use of open innovation at a blue chip company.
I would argue that if Barclay’s incubator program is not accelerating the statups’ path to revenue through its own value chain, then it doesn’t offer a lot of value for the startup. Especially so because FinTech startups either want to bring down the current value chain (meaning they will not join the incubator) or want to partner with incumbents (meaning they are looking for co-innovation with Barclays).
Look forward to discussing further!

On November 14, 2018, Marc commented on JP Morgan: Expanding Machine Learning Capabilities in the Financial Services Arena :

Hey Mike,
I really enjoyed your post. Specifically, good job referencing market statistics and stakeholder quotes. I wrote my essay on the ML opportunity at Goldman so I see a lot of similarities in our findings. Two areas that I am still struggling with are the following:
1 – Do Goldman, JPM and the like have legal rights to their clients’ underwriting data? Yes, this is a perfect use case for ML, but so far I dont see any real-life success stories.
2 – Because of the inherent difficulty of monetizing B2B data (institutions tend to actuall look at data privacy agreements), I see the universal banks doing a lot more with ML in the consumer / retail segment. Did you get a similar takeaway?

On November 13, 2018, Marc commented on BlackRock: Is the Future of Investing in Machine Learning? :

Hi CG,
I find this to be a really exciting topic. Great job covering all the different angles for AI to benefit Blackrock (investing, process improvement, etc.). The use cases you shared such as text analysis on earnings calls transcripts are interesting. I would have been curious to know, in your opinion, which use case is most promising, and why would Blackrock be best positioned to win? It seems to me like Blackrock has little proprietary data, so barriers to entry for applying ML to publically accessible data are low (e.g. using Tensorflow).