Cookie Settings
Banking and Financial Services

Make Better Strategic Decisions

Improve operational efficiency, profitability, and regulatory compliance, while reducing risk.
 

Overview

Mathematical optimization is a well-established, essential technological tool in the financial services industry. For over 50 years, mathematical optimization technologies have been used by leading companies across the financial services ecosystem (including institutional and consumer banks, wealth management firms, hedge funds, insurance providers, and fintech players) to:

  • Address a wide variety of complex business problems including portfolio optimization, cash management, trade settlement, and asset-liability management.
  • Make optimal, data-driven decisions that deliver improved operational efficiency, profitability, and regulatory compliance as well as reduced risk and costs.

To see how it works, visit Gurobi Finance, our dedicated technical documentation for the finance sector.

The Solver That Does More

Gurobi delivers blazing speeds and advanced features—backed by brilliant innovators and expert support.

  • Gurobi Optimizer Delivers Unmatched Performance

    Unmatched Performance

    With our powerful algorithms, you can add complexity to your model to better represent the real world, and still solve your model within the available time.

    • The performance gap grows as model size and difficulty increase.
    • Gurobi has a history of making continual improvements across a range of problem types, with a 91x speedup on MILP since version 1.1.
    • Gurobi is tuned to optimize performance over a wide range of instances.
    • Gurobi is tested thoroughly for numerical stability and correctness using an internal library of over 10,000 models from industry and academia.

  • Gurobi Optimizer Delivers Continuous Innovation
  • Gurobi Optimizer Delivers Responsive, Expert Support
Faster Portfolio Optimization Compared to Other Commercial Solvers
1 %

Frequently Asked Questions

  • Mathematical optimization uses the power of math to find the best possible solution to a complex, real-life problem. It can be thought of as a way to make the smartest (and most optimal) decision despite having a multitude of variables and challenges.

    Mathematical optimization models contain three components:

    1. Objective Function: This is the end goal that you want to achieve.

    2. Decision Variables: These represent the items involved that you can control and change in order to reach your objective.

    3. Constraints: These are the rules and/or limitations that you must follow.

Additional Insight

OneChronos: Using Optimization and Combinatorial Auctions to Innovate Modern Trading

 Learn More

Guidance for Your Journey

30 Day Free Trial for Commercial Users

Start solving your most complex challenges, with the world's fastest, most feature-rich solver.

Always Free for Academics

We make it easy for students, faculty, and researchers to work with mathematical optimization.