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Case study

Preparing a FTSE 100 finance company to harness AI

We conducted a comprehensive data science readiness assessment to ensure a finance company’s trading division was prepared to adopt more sophisticated AI. 


Customer

A FTSE 100 finance company.


Problem

Advances in machine learning (ML) have benefited trading businesses hugely. Computer programs capable of storing, manipulating and analysing large volumes of data mean that traders can make informed decisions more quickly. In this case, the client believed that the market in which it operated had been slow to take advantage of the benefits of artificial intelligence (AI), and it spotted an opportunity to become a market leader through better use of ML.


Solution

Faculty was engaged by the company to conduct a data science readiness assessment. The aim was to ensure that the company’s trading division was well prepared to adopt more sophisticated AI in its trading operation.

The readiness assessment was conducted in three phases. First, we ran workshops bringing together stakeholders to give us a clear understanding of the specific trading market and how data-driven insights could give traders an edge. Second, we mapped out all data sets and conducted an assessment of over 1,000 data feeds. This was designed to measure the extent to which the client’s data, infrastructure and other capabilities were able to support ML use cases. Finally, we identified high-priority ML use cases and offered recommendations on how the client should build and deploy ML models systematically across the organisation.

 

case study energy providing a data science readiness


Impact

The client adopted all the recommendations from our data science readiness assessment. These included the creation of a Data Science Centre of Excellence, investment in Faculty Platform to support collaboration across geographies, and implementation of identified priority models.

Since then, Faculty has collaborated with the client to build priority models, which automate many of the analysts’ tasks and help traders to spot and capitalise on discrepancies in the market.

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