Invessed case

ML Roadmap to the Personality Engine


Invessed is a client-centric platform for Wealth and Asset Managers, where data and analytics together promise a personal experience for clients of Asset Managers. We interviewed Theo Paraskevopoulos, CEO of Invessed, on his experiences working with Enjins and founding his company Invessed.

An Interview with Theo Paraskevopoulos, CEO and founder of Invessed

Invessed emerged from the combined experiences of founders Theo Paraskevopoulos and Adam Weston. Theo and Adam founded their agency GrowCreate in 2012, and worked a lot for Wealth and Asset Managers over the years. They learned that, even though every company is different, all asset managers have quite some things in common. Invessed is the result of 5 years of hard work and experience: a client-centric platform for Wealth and Asset Managers, where data and analytics together promise a personal experience for clients of Asset Managers. We interviewed Theo to learn what his challenges were in his road to build Invessed, and how he foresees the upcoming years with Invessed. Read below Theo thoughts and his experiences working with Enjins.

Theo Paraskevopoulos GrowCreate Invessed

Why did you form the company? What was the motivation?

Invessed was founded about a decade after the financial crisis and the launch of the iPhone. Ten years later after these events, we found that many investment brands had not fully clocked on to the repercussions that these two pivotal events would have on the way they interact with their clients.

The financial crisis provided the push: the entire financial services industry was painted in a very negative light, clients were pushing for more transparency, more trust. The iPhone was the pull: the means through which clients perceive trust, in the form of instant, convenient access.

For various reasons – ranging from complacency to bread-and-butter issues like cost and legacy – many investment brands are slow to understand that this push/pull dynamic has reshaped their business, and so they have stalled to adapt to what is commonly called “digital transformation journey”. By 2017 we founded Invessed with the aim of filling this glaring gap, in part inspired by faster-moving sectors such as e-commerce, which had to deal with issues of trust and scale ages ago, and had already moved on to advanced analytics and personalization.

The firm was formally founded in 2017, after a period of incubation at GrowCreate, a digital technology agency. After working extensively with investment brands, we realized that the industry’s needs are best served through a platform, rather than reinventing the wheel with every project. Since then, we built dedicated client service and product development teams in the UK and Germany.

What are the biggest challenges for Invessed in the upcoming years?

Obviously, we are still a young company so our focus remains on implementing our aggressive agile roadmap. We are rapidly building new features, linking up with new partners and actively looking to engage investment brands with a growth mindset.

As Invessed is built on the experiences we gained over the past years, we strongly believe that there is so much valuable information in the data which is never used by asset and wealth managers. This is the reason we started building our platform, and we’re now on a stage where we can accelerate the use of this data and transform it to actions and insights. Of course we’ve got quite some knowledge and ideas to build from, but coming up with a detailed roadmap to develop the building blocks was an important step for us.

How did Enjins help?

Together with Tevhide (ML Engineer at Enjins) and Maarten (Co-founder at Enjins) we went through our platform, data systems and challenges. We discussed our ideas about how data and ML could help making our platform more client-centric and actionable for Asset Managers. Tevhide helped us transforming these ideas to concrete ML building blocks and a roadmap with the following components:

  1. Description and elaboration on the ML use cases/models which we are going to build
  2. Blueprint of the data infrastructure needed for our models in production
  3. Guidelines to effectively collect and store data for analytical purposes

With this plan in hand, we can move forward and implement the most important ML building blocks to make our platform more actionable and promise the best personal experience for Asset Managers.

Machine Learning Modules

What’s next?

We are planning to move forward with implementation of the ML building blocks, following the roadmap that Enjins created. The ML building blocks combined will lead to the personality core, which is one of our key features for the platform. For us, working together with Enjins to implement these building blocks would be a logical next step!

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