In his role, Liberty IT solutions engineer Chris Brennan has used automation to reduce useless touches on vehicle insurance claim documents.

Automation can be used in almost any industry to do a variety of tasks more efficiently. For example, Liberty IT solution engineer Chris Brennan has used automation to reduce useless touches on vehicle insurance claim documents.

Brennan has worked in the business and emerging technology sector at Liberty IT for the past two years, focusing on automation and intelligent extraction.

He told SiliconRepublic.com what his role involves and what a typical day looks like for him. “If I had to try and describe it, I would say it’s a mix of software architecture (30pc) design, software engineering (40pc) and leadership (30pc), all with a twist of innovation!”

He said he could do anything from writing thousands of lines of code in one week, designing new solutions on a whiteboard, or doing research on new tools.

“I can develop pilots, prototypes and concept tests to test how we can further improve business efficiency, or I can just be in a series of phone calls with Amazon or another cloud provider to ask for new features. in service – it really is so diverse and interesting! ”

“I like it when we can find a use case for a new technology that has a tangible impact”
– CHRIS BRENNAN

What types of automation projects do you work on?

It varies, but if I had to choose a stellar project, I would say it is our AI-enabled Automatic Physical Injury Supplement (APD) project.

In this project, the aim was to reduce unnecessary touches on vehicle insurance claim documents due to classification and manual loading, by changing the swallowing of additional information into a ‘pull’ controlled by our customers through a web-based portal and easy to use.

Each client transaction flows through a reverse agent (for security) and interacts with our configurable RESTful web service in AWS, which facilitates the swallowing of their data at our end. This enables the extraction and understanding of their data across multiple document formats, providing a new set of additional data points for intelligent interrogation and automation.

These data points then flow through a feature engineering pipeline and are used as part of a feature set for our personalized artificial intelligence intelligent review model for automatic approval, enabling direct processing of claims for our customers.

What skills do you use on a daily basis?

The skills I use most often are coding and collaborating with the Agile Scalable Framework (SAFe) at the core of what we do. I work through each program growth with other agile, breakaway, and highly efficient teams, all working toward a common goal within the same agile launch train.

What this means is that it is imperative for me to collaborate both internally and with technical architects across the wider organization to design approximate solutions for the enterprise. This is a skill I often use more than I initially expected in my current role as a tech executive, but I’m happy with the knowledge I get while doing so.

Sometimes it shows that I draw tactical solution designs to quickly test concepts, and at other times it requires me to collaborate on larger design models across the organization in more detail.

Typically, once we have agreed on a solution design, I will direct the technical development of that solution. Most often, I am writing code in Python, JavaScript / TypeScript and deploying it in AWS using their Cloud Development Kit as a code-like infrastructure technique.

These solutions are often very complex, advanced, server-free pipelines that can be considered complete. They often include a front-end environment in React or Angular that sends data via a reverse representative in NestJS or Express to an API gateway in AWS, which connects multiple CloudFormation chains together, orchestrated through a combination of Step functions , Bridge of Events and Apache Skull, and of course registration in Splunk and Datadog.

What are the most difficult parts of working in automation?

I would say that the most challenging part of working in automation is managing inflated expectations for technology. These often stem from sellers selling over their products and over simplifying complex problems, which can lead our stakeholders to false claims.

That said, we have strong ways to evaluate these products to make sure they are appropriate for the purpose and can deliver the expected results. Often, we compare vendors’ tools to our personalized approaches, allowing us to select and / or develop the best technology or approach to use – and not just what is available to us.

While this can be challenging at times, it is this mentality that allows us to push the boundaries of what technology can do as we work at the intersection of business, technology and value.

Do you have any productivity tips that help you during the day?

Yes, I have some important advice. Protect your time!

I do this using Microsoft Insights, which uses data from my email activity and on my calendar to suggest the most optimal focus time blocks for me. What this means in practice is that I have three hours blocked each morning to focus, which I normally use as development time.

But it does not need to be something as complicated as this. Just do everything you can to ensure you work as productively as possible. These can be small things, like e.g. respond to a meeting invitation in advance and let the host know that you are available ad hoc instead, or you may leave an appointment early when your part is over. Work smarter and no more.

How has this role changed as the automation sector has evolved?

Automation in insurtech is no longer just about creating tactical solutions to quickly deliver savings in efficiency. Changing customer and stakeholder expectations, along with the emergence of new paradigms such as machine learning operations and hyperautomatization, have seen the sector evolve dramatically.

Instead of trying to automate the interaction with older systems to free human analysts, it is more about transforming and changing the whole business process by redesigning and re-engineering them in a data-driven way.

What this means for the role is that we are now developing new enterprise applications using the latest technologies. We went from incredibly extracting client data from free text to email and storing them in registration systems, to developing brand new web applications for our clients to use.

They can now give us their data in a single structured transaction and it is because of this type of data model and structure that we can now extract, manipulate and reliably store their data in ways that enable the operation of engineering and AI.

What do you like most about working in automation?

There are two things I really like. Innovation through the constant emergence of new technologies and the autonomy and flexibility we have to adopt them. I like to learn about them and I like when we can find a use case for a new technology that has a tangible impact and gives business value.

But what I think I can love more than that is the satisfaction I get from this kind of work. It is our insurance that really helps people, sometimes to buy a house or a car, other times for medical care and even to put things together as quickly as possible when things are not going so well for them .

So working in this field, when something we have designed and developed can have a massive impact on someone’s life, but that is simply incredible and makes it even more valuable.

What advice would you give to someone who wants to work in automation?

My advice would be to try not to get caught up in the exponential growth of robotic process automation (RPA) and understand that hyperautomatization, while a new term, dates back to before the modern RPA era.

Conventional software engineering tools and techniques and server-based architectures, based on cloud computing, will often go far beyond the scope of somewhat sold solutions along with efficiency, greater reliability, and reduced cost.

So when you aim for automation, think about what is the best way to achieve what you want. Is it an approach that interacts with legacy systems and inherits their shortcomings, or would it be better to re-engineer and redefine the process? And when answering this question, think about the tools, techniques, and skills you will need to achieve it, and then focus on building this skill set.

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