Week 3 Case Study 7.2 – Goldman Sachs

W3 Case Study

Read “Case 7.2: Goldman Sachs” and write an essay that answers the following questions:

1. How is Goldman Sachs using machine learning to improve their decision-making process in the investment banking sector?

2. What are some of the benefits and challenges of using AI in the financial world, particularly in the investment banking sector?

3. What are some of the key AI technologies and processes that Goldman Sachs is using in their investment business, and how do they contribute to their overall success?

4. How has the availability of non-traditional data sources, such as internet web traffic and satellite imagery, affected Goldman Sachs’ investment decisions, and how has AI helped them to make sense of this data?

5. How do you think the role of AI in the financial world, particularly in investment banking, will continue to evolve in the future, and what impact might this have on the industry as a whole? 

Requirements:

· There is no specific page requirement for your analysis. Instead, your work will be evaluated based on how thoroughly it addresses each of the questions that have been outlined for you.

· You must utilize proper APA formatting and citations throughout your paper. If you use any supporting evidence from external sources, it is imperative that you provide accurate citations for each reference.

· You must include a minimum of two sources from scholarly articles or business periodicals, aside from the course textbook. 

· Include your best critical thinking and analysis to arrive at your justification.    

 

 

Case 7.2: Goldman Sachs

Goldman Sachs is one of the largest investment banks globally. The investment banking division is at the forefront of Goldman Sachs’ client franchise. The com- pany is using machine learning to analyze different data sets to come up with rec- ommendations to make richer and more informed decisions.

Machine learning in the investment banking sector is more complex than in the IT world. Artificial intelligence has been heavily advertised in large companies such as Google’s AI computer program that is leading the board; it is well established and has a set of norms and regulations it is programmed to follow. However, in the finan- cial world, analytics are constantly changing. “ML models provide statistical tools that help identify patterns of relevance from the data, and in doing so, can guide practitioners to a place that would have otherwise taken them much longer to get to” Goldman Sachs uses ML in their Fixed Income Clearing Corporation (FICC) busi- ness for business intelligence to aid them in understanding how their business is going to help their clients.

Through the usage of big data analytics, Goldman Sachs can track their perfor- mance in addition to being able to identify different outcomes of their client’s pref- erences. In equities, ML models at Goldman Sachs are being used in areas such as business intelligence and flow analysis to inventory management and derivative pricing. The investment company is also looking to add natural language processing to its list of AI processes. Language processing is “an AI technique that translates human language into structured data sets – to automate all sorts of tasks that are used to require human intervention like chat-based quote requests.” With more and more data being publicly available from other private investment bankers, there is more data out there to analyze and turn into larger data sets to aid in their successes.

Goldman Sachs is heavily involved in investing in machine learning which can be proven by doing background research on all of their new hires. It is not to say that Goldman Sachs has it all 100% figured out, while they can use ML to create differ- ent results with given data, the quality of their results depends on the quality of the data they are using. The models they create through AI, and the outcome can be distorted or not accurate, and that is one of the unfortunate downfalls of predictive analytics. Although consumers can trust that Goldman Sachs is a reputable com- pany that is consistently delivering great results and is backed by the numbers. If they continue to keep investing in their machine learning systems, the company will continue to deliver quality work.

The report shows how AI and big data are helping Goldman Sachs to create, acquire, and process large amounts of data, in turn enabling them to use insightful outside of the box analytics to generate value and enhance their overall competitive advantage. A Goldman professional states, “With the growth and availability of non-traditional data sources such as internet web traffic, patent filings, and satellite imagery, we have been using more nuanced and sometimes unconventional data to help us gain an informational advantage and make more informed investment deci- sions” (Goldman Sachs, 2016). Data like internet web traffic, patent filings, and satellite imagery are very vast and nontraditional sources of data to be used when it comes to making investment decisions. Though with the help of AI technology, an institution like Goldman Sachs is for the first time being able to acquire and process these unusual and large amounts of data that through provided analytics can tell a story about a company, industry, or any other type of security they may or may not want to invest in. Goldman Sachs is rapidly adopting AI technology within its investment space. “Research success for us is not finding a new stock to invest in, but rather finding a new investment factor that can help improve the way we select stocks. Investment factors should be fundamentally-based and economically- motivated, and the data enables us to empirically test our investment hypotheses” (Goldman Sachs, 2016). Goldman Sachs can apply these emerging technology tools to not only acquire large amounts of information but also to test their findings empirically to come to an investment decision. Though the role of AI is completely shifting how Goldman Sachs interacts and uncovers data to eventually come to an investment decision, it is important to note, “We do not have a computer in the cor- ner simply shooting out trades with no human interaction” (Goldman Sachs, 2016). Overall, the commentary provided by decision-makers from Goldman Sachs in the financial management space goes to show how AI is enabling top financial services firms to acquire and process large amounts of data that has never before been con- sidered to aid investment decisions. This new access is enhancing efficiency across the board, creating larger returns, and aiding advantage against the competition.

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