We are explorers of new possibilities

Kavout: /ka’vaut/ to find.

Kavout’s Differentiation

Research-Driven

Research-Driven

Our method is grounded in fundamental finance principles, rigorous quantitative research with tested models and strategies, and domain knowledge in FinTech, compliance and operations.

Our team is comprised of seasoned investment professionals, university professors and researchers in applied math and quantitative finance.

Track Record in AI & Big Data

Track Record in AI & Big Data

Close to 20 years of experience developing big data, natural language processing (NLP) and machine learning at a large scale at Google, Microsoft and Baidu.

Developing Kavout’s suite of machine learning and deep learning products and services since 2016.

World-class Engineering

World-class Engineering

Decades of combined engineering know-how, developing information and commerce platforms at the scale of billions of search queries per day, and more than 10,000 purchase transactions per second.

Experienced in the development of large-scale cloud computing architecture, data storage and processing, and algorithms and decision engines processing at 40,000 QPS per second.

Thesis

In the financial markets, over the past 150 years, many alpha sources have come and gone. In the 1950s, it was the invention of long/short equity strategies by the very first hedge funds. In the 1980s, mathematics and computers held an edge over calculators. In the early 2000s, it was high-frequency trading. These were all strategies or tools that gave those who had access to them an advantage. But as they became more and more widespread, their advantage dissipated. Investors are looking for the next big thing.

At Kavout, we believe that the application of artificial intelligence (AI) and machine learning (ML) is the next transformative force to change the investment and wealth management landscape.

Gartner predicts that in the next five years, AI and ML are increasingly a foundational component of all of the applications, services and things around us.* Financial markets are one of the early adopters, with companies such as BlackRock and Fidelity integrating ML in their investment strategies and process.

Meanwhile, job roles that require decision science and analytics skills (including data science, ML, and big data) are projected to grow at the highest rate yet are the most difficult to fill**. Unlike BlackRock and Fidelity, whose assets under management (AUM) is $6 trillion and $2.5 trillion respectively, most investment firms face insurmountable challenges on many fronts.

  • They may sit on a vast amount of data but not know how to tap into it.
  • They may lack an in-house AI/ML team or the know-how and experience to run it.
  • Even if they start building capabilities today, training machine models and algorithms require time and tuning, which Kavout has been doing since 2015.

On top of that, robo-advisory firms such as Betterment and Wealthfront are slowly chipping away AUM from traditional investment firms, offering lower fees while driving massive inflow to passive funds such as ETFs***. Companies are under pressure to demonstrate value to clients while lowering cost-to-income ratio.

Kavout’s mission is to democratize AI and machine learning, empowering institutions and investors with augmented intelligence to generate alpha, manage wealth, and do more with less.

Source: *Gartner Top 10 Strategic Predictions for 2018 and Beyond, 2018
** IBM, Burning Glass Technologies and BHEF joint research, The Quant Crunch, How the Demand for Data Science Skills Is Disrupting the Job Market, 2017
***Bloomberg Intelligence, Aug 2018

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