Data Analytics is a driver of growth and change.

Dilemma Management is a consulting company focused on data analytics.

We focus on the following areas.

Machine Learning

Optimizations

Financial Modeling

Natural Language Processing

Big Data

Crypto Currencies

It’s not hard to see how we added value to prior projects.

Machine Learning

Technology: Apache Spark, Scikit

  • Transactions Classification - applied machine learning to banking transactions descriptions in order to extract transaction category. Leveraged regulatory data sets to identify sender, recipient and beneficiary of transactions.
  • Email Analytics - analyzed email traffic (email size, recipients) and recommended measures to address data breaches resulting from employees sending “print screens”. Established attachment size threshold.
  • Anomaly Detection - leveraged unsupervised machine learning models (e.g. clustering) to flag outliers as a proxy for access anomaly and established a baseline for app permission levels.
Optimizations

Technology: Python, Tableau

  • Trade Processing - analyzed and identified drivers for straight-through processing (capture, process, repair) rates and recommended opportunities for improvement (e.g. broker data, asset data, internal pipelines).
  • Travel Spend - delivered exploratory analysis with the following scope: reduce unnecessary travel, improve advanced booking, increase adoption of online tools, increase economy class incentive rate, increase hotel attachment rate.
  • Transfer Agency - evaluated the feasibility of developing products that can be marketed to TA clients. Analysis focused on: investor profile (e.g. sources of health), investor behavior (e.g. transactions).
Financial Modeling

Technology: Python, Tableau

  • Assets under Management Flows - as a result of a Securities and Exchange Commission request, modeled annual change in AUM between flows and market movement. Calculated currency attribution to better understand market performance.
  • Prospect Fee Benchmarking - part of the bidding process leveraged public data sets (e.g. US Department of Labor 5500) and benchmarked the fees that a prospect is paying. Produced year over year market analysis.
  • At Risk Clients - developed model/prototype to identify at risk clients based on relationship profile (age, number of products), transaction patterns and interactions (e.g. number of emails).
NLP/NLU

Technology: TensorFlow, AWS, Tableau

  • Sentiment Analysis - extracted client sentiment at the document and sentence level using open source deep neural networks models.
  • Topic Modeling - identified patterns/topics present in text documents (e.g. client conversations).
Big Data

Technology: Apache Spark, Hadoop

  • Recommendation Engine - prototyped model to recommend products to client facing employees for cross selling opportunities
  • Real Estate Utilization - leveraged turnstiles and computer logs to calculate building and floorlevel utilization rates.
Crypto Currencies

Technology: AWS, MongoDB, Spark

  • Bitcoin Analytics - extracted and processed all bitcoin transactions to identify patterns. Predicted prices based on order flow.