Sales and trading optimization for an investment bank

An investment bank was seeking data-driven solutions to:

  • Maximize the deal values by anticipating the clients’ needs more efficiently.
  • Assess and prioritize opportunities for business development.
  • Provide customized offers to strengthen client relationships.
Market data with charts shown on a laptop screen

What we did

We brought internal data (e.g., transaction history, dynamic inventory lists, client holdings and activity) and market data (e.g., interest rates, currency fluctuations, market volatility, TRACE, ratings, etc.) together.

We then applied predictive analytic techniques (e.g., clustering, GBMs) to identify clients most likely to be interested in trading specific bonds in the bank’s portfolio. Also, we built in a user feedback loop that continually updates the process to identify better opportunities. The whole process was seamlessly integrated into the existing sales platform and UI.


Our matching engine provides actionable insights that allows our client to stay competitive in the market.

  • The largest and most profitable clients are provided with fresh ideas and the best opportunities.
  • Mid-tier client calls become far more productive — sales spends less time researching client activities and strategic positions.
  • Smaller clients are managed in a cost efficient manner through automated communications.

Agile Analytics Lab for a bank

The client was seeking a streamlined method to organize their data into a single location to enable business insights. Some of their challenges included:

  • A massive amount of information stored in disjointed databases and systems.
  • The inability to present data in a unified view due to databases being controlled in silos.
  • The internal IT team was backlogged, and they needed to get up to speed on the latest big data tools.
People working in a modern office space

What we did

The client partnered with Fulcrum to utilize our Agile Analytics Lab and experiment with modern big data solutions.

  • We created reusable data processing pipelines, loaded the data into Hadoop and prepared them for analysis.
  • After establishing the central repository, we then cubed the data using Hive and Spark for fast querying of metrics.
  • We stored the output tables in HBase and used Impala for queries to gain rapid response, and we created a Tableau dashboard for the business users.


The lab proved to be the perfect instrument to bring agile analytics to this financial services company.

  • The client was able to gain a holistic view of their data and unlock new insights.
  • Additionally, the client was able to rapidly develop and tune machine learning models with our help.
  • Lastly, we were able to help modernize the bank's analytics team through our training and support on emerging technology.

HR analytics: Branch performance benchmarking and forecasting

A bank’s Human Resources (HR) were seeking a solution to incorporate market and competitive data to improve personnel evaluation and optimize resource planning.

Two people shaking hands with spreadsheets and charts on a table in the foreground

What we did

We integrated external data (e.g., FDIC competitor data, footprint IXI demographics, etc.) with internal data (e.g., staff experience, branch performance, etc.) and developed new data elements on branch market share and competitive landscape.

Then we performed deposit growth segmentation to benchmark branch growth and market penetration, controlling as many factors as available. And, we performed branch performance forecasting for each segment.


  • Our analysis helped our clients retain high performers using the improved compensation formula.
  • The HR was able to hone in on exceptional manager candidates for recruiting.
  • Furthermore, our branch growth forecasting identified branches with the greatest growth potential to optimize resource planning.