Scout InsurTech Interview with Velos

Velos is an intelligent automation platform. They seek to help non-technical operations teams in the insurance space, use AI to automate complex, manual back office processes. Andrew Daniels sat with Founder, Oscar Martinez, to learn more about how Velos is impacting the industry.

Who are Velos’s clients?

Our clients are typically those who are responsible for data processing, which is usually the commercial carriers, but it can also include Third Party Administrators (TPAs) or software vendors working with these carriers and managing data processing.

What does your product do?

We are an intelligent automation platform that uses AI to automate manual data processing tasks, such as data analysis, data cleaning, data extraction and data entry. Essentially, we help people train an AI workforce to handle the most tedious parts of their jobs, so they can focus on higher level tasks.

How much capital have you raised?

We don’t have that information to disclose at this time.

Was the company born from within or outside the industry?

The company’s origin is a mix of both inside and outside the industry. Much of our perspective was informed by my time in the actuarial department of a mid-sized carrier, but by the time we started the company, it had been a while, so I’d say we were definitely outsiders.

What growth metrics have you accomplished over the last 12 months?

A year ago, we were starting from zero—just entering the space and addressing this problem. Today, we’re growing, profitable and engaging with some of the largest carriers and software vendors in the industry to explore how we can help them. That’s the growth I’m most excited about over the past 12 months.

Within your domain, what is the current challenge that the industry is facing?

The industry faces a two-part challenge: 

(1) Insurance is an operationally intensive and messy business with a retiring workforce that traditionally handled a lot of the manual work. The new generation isn’t as willing to take on these tasks.

(2) At the same time, existing tools, like robotic process automation (RPA), often fall short. Robotic Process Automation solutions, like UiPath and Microsoft PowerAutomate, rely on structured inputs and outputs and fail when faced with the idiosyncrasies and complexity of insurance operations. Alternatively, outsourcing through BPOs has limitations in scalability, accuracy and cost. 

This combination of a shrinking workforce and inadequate software alternatives creates a pressing need for better solutions.

How does Velos take a unique approach to providing value?

Our approach is unique because we focus on leveraging the latest AI technologies–such as large language models and natural language processing–to match the flexibility and decision-making capabilities of a human performing these tasks. 

Instead of relying on structured inputs, we position our software to work like an assistant, using the same tools and data as a human would. This allows for end-to-end automation that is both flexible to complex situations and also easy to configure, eliminating the need for large teams of maintenance engineers. 

In other words, compared to traditional RPA, our approach covers a broader range of tasks and is more adaptable to the nuances of insurance operations.

What inspired the team to start this company?

The inspiration for the company came from my early experience as a data scientist in the actuarial department of a mid-sized commercial carrier. I saw firsthand the inefficiencies in processes reliant on Excel, PDFs and manual entry. Many data challenges fell to the actuarial team, and solutions like overseas BPOs often felt inadequate. 

Later, while working on machine learning technologies for the Securities and Exchange Commission and the Department of Defense, I realized that the modern AI algorithms that were used to teach a computer to play computer games could also be used to teach a computer to complete manual data tasks with the flexibility and nuance needed for insurance operations. 

Can you share any goal(s) for the next 12-month?

Our primary goal for the next 12 months is adoption by mainstream insurance players. We want to validate whether the insurance industry is ready for this change today. Artificial Intelligence Automation in insurance will undoubtedly transform operations within the next 25 years, but the timeline for widespread adoption is uncertain. Could be 2 years, could be 10 years.

The next year will be about demonstrating that insurance companies are investing in and ready to embrace these changes now, which will guide our growth strategy and validate our focus on this vertical.

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