FinMason Offers Investment Analytics At Lightning Speed

The Benzinga Global Fintech Awards are a yearly showcase of the best and brightest in fintech. In preparation for its biggest installment yet in May 2018, we're profiling the companies competing for the BZ Awards.

Our next feature is on FinMason.

What does your company do? What unique problem does it solve?

Kevin Bruckenstein, marketing communications director, FinMason: FinMason operates one of the largest and most scalable investment analytics engines in the world. We deliver over 700 analytics on every single publicly traded asset in the world at both the portfolio and position level.

Our calc times are the fastest in the industry at around 20ms for a 50 asset portfolio and we deliver all of our analytics via FinRiver™, a simple to use, lightning-fast API. Unique Problem Solved: FinTech companies and financial institutions need to boost functionality and improve user experience. Our FinRiver™ API allows developers to rapidly prototype, iterate on functionality, and A/B test without having to build out analytical calculations and wrangle financial data. This enables faster time to market and a better vetted user experience. 

Who are your customers?

Financial platforms that are in build-mode and are looking to access a robust, flexible, and affordable analytics engine to power their tools.

How long have you been in business?

We launched our FinRiverTM offering in early 2017.

Where are you located?

Boston, MA.

Who is your company's leadership? What kind of experience do they have?

Kendrick Wakeman, CFA: CEO and founder. Brings 30 years of experience running a hedge fun and leading investment research. He has assembled a team of nine Ph.Ds and other industry veterans.

Who are your investors, if any? Internal and angel only.

Is there anything else Benzinga should know about your company? FinMason’s strengths are the robustness, speed, and flexibility of our analytics. We can help financial platforms get to market significantly faster, and more cost-effectively, than if they were to build their own analytics engine.