Operating financial services firms can be challenging and complex, therefore it is important to have the right tools for decision making and management. At Fuzzy Logix, our team has over 40 years combined experience in investment, commercial and consumer banking. Our experience is deep in areas such as:

  • Building an origination strategy for mortgage lending that led to 100% growth in 4 years to $22B.
  • Developing the strategy for portfolio optimization and re-balancing to manage one of the world’s largest portfolios of commercial loans.
  • Implementing a process and the corresponding models to value thousands of companies daily to assess lending risk. These tools included the valuation of cash flow, receivables, inventory, forward contracts and organic growth.
  • Building the technology platforms for modeling massive quantities of data using complex quantitative methods to simulate the behavior of financial instruments.

Based on our industry experience and customer feedback, we’ve developed a library of financial analytics models that are available to run in-database or inside NVIDIA GPUs. 

Video:  Using In-Database Analytics for Financial Services – Technical Overview


  • Financial measures – the same financial functions that are available in MS Excel
  • Fixed income – bond math, volatility, convertible security analysis, various risk assessments and spread measures, MBS and ABS analysis
  • Portfolio management – return, volatility and correlation analysis, CAPM and APT models, PCA, simulation of correlated returns, mean-variance optimization, CVaR based optimization, performance measures
  • Derivatives – futures and options, exotics, interest rate derivatives, credit derivatives
  • Time series – ARMA and ARIMA, exponential smoothing forecasting (e.g, Holt Winter’s), ARCH and GARCH, Cointegration, regime switch models

In addition to the models listed above, we build and deploy custom models.


  • Analytics for Hedge Funds – Hedge Fund Managers can gain significant competitive advantage from leveraging analytics using the massive parallel processing power of Graphical Processing Units (GPUs).  Read more here.
  • Simulate Interest Rates – Rather than simulating millions of interest rate paths in Matlab or c++, analysts can now do it in the database and save them to a table. These paths then could be used for various analysis or pricing purposes.
  • Options Pricing – Trading desks need to price various options contracts quickly and accurately, this can now be done in the database using different models, from Black Scholes to binomial trees to PDE grids.
  • Value at Risk – Risk management needs to calculate VaR or CVaR for the portfolio on a daily basis, and since all the positions and market data are already in the database, they would like to run the daily VaR process all in-database, which saves a temendous amount of time by not having to move large amount of data around.
  • Intra-day Risk Management – Given the need to calculate things like value at risk for trading portfolios at increasing frequencies, a new class of solutions is required.  Using GPU-based analytics, we illustrate how you can manage intra-day risk and perform scenario analysis for 1 billion paths in milliseconds.  We believe the problems of intra-day risk management can be solved effectively using GPU-based solutions at a fraction of the cost of traditional CPU-based blade solutions.