When making important decisions about their futures, your digital banking customers want to make informed choices about how to manage their finances. But when financial assets are affected by market parameters beyond their control, how can you empower them to make well-informed decisions?
Analyses taking into account the uncertainties of values such as future interest rates, future tax rates, or future currency exchange rates, are traditionally computed using Monte Carlo methods, which are typically tedious to setup and time-consuming to run. By building your digital banking applications to take advantage of the Signaloid Cloud Compute Engine, you can provide your digital banking customers with the same kinds of detailed analyses previously only achievable through slow and expensive Monte Carlo methods, but at blazingly-fast speeds that enable interactive exploration. Transform the digital services provided by your bank and give your customers greater confidence when making financial decisions.
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Applications Templates
Try example applications on the Signaloid Cloud Developer Platform or view example source code in our GitHub repositories.
Building Blocks
Reduce your engineering effort by starting from source code of example application templates. Allow your applications to interactively specify input distributions using Signaloid's patented UX for distribution inputs, multi-modal text, and graphic output distribution visualization.
Solutions
Choose between running computations on our cloud-based compute engine, or your current on-premises infrastructure.
Example Performance Validation
Speed up your existing AWS EC2 or on-premises deployments by 2x–8x while achieving the same level of accuracy by deploying Signaloid’s UxHw virtualization layer on top of existing hardware. Achieve even greater speedups using UxHw accelerator cards. Give your existing optimized quant libraries an additional boost, by integration with your existing compilers and numerics libraries.
Technology Explainers
Signaloid's technology provides deterministic uncertainty tracking methods by utilising efficient arithmetic on probability distributions while integrating with existing CPU and GPU architecture. These technology explainer articles provide further details on how this technology benefits Digital Banking applications, from the underlying mathematics to the engineering implementations.
