Enova Innovation & Technology Culture

Enova Employee Perspectives

How do your teams stay ahead of emerging technologies or frameworks?

We stay ahead of emerging technologies through a thoughtful mix of research, experimentation and smart use of industry-leading tools. A dedicated group helps guide our focus, keeping close watch on new developments by reading the latest research, reviewing industry papers and attending conferences. This gives us a clear view of where the field is heading, such as the move toward more agentic AI and helps us understand how these shifts could shape our future work. Alongside this, we maintain an active experimentation pipeline where we explore new capabilities and push our systems beyond basic search. For example, we test advanced methods like retrieval-augmented generation and different ways of structuring data so our AI can better understand the relationships within our code, leading to more accurate and helpful assistance. To move quickly from ideas to real solutions, we build on top of strong vendor platforms such as Google Gemini and AWS Bedrock, giving us the ability to prototype and launch new tools faster without reinventing the wheel.

 

Can you share a recent example of an innovative project or tech adoption?

A great example of a recent project is our AI-powered code reviewer, ArchBot. We built it ourselves using Go on AWS Bedrock and it’s integrated directly into our GitHub environment to handle many of the routine tasks in the code review process. The benefits are clear: it keeps our code clean and consistent, speeds up development by giving senior developers more time back and can be customized for different team workflows. ArchBot is a prime example of how we use AI for “human assistance” — creating practical tools that help us today while also building our understanding of AI for the future. It’s a real, in-production tool that our engineering teams rely on every day.

 

How does your culture support experimentation and learning?

Our culture is built on encouraging new ideas and learning and we support this in several key ways. We have a clear path for taking ideas from small experiments to full products: We identify a specific problem, build a small test — like a tool for financial document processing — and if it works, we roll it out for everyone to use. At the same time, we strike a balance between quick wins and bigger bets. Tools like ArchBot deliver real value, build trust and give us the freedom to explore more cutting-edge research. Above all, our approach is people-focused. We develop AI tools to augment our teams, helping them work smarter on tasks like diagnosing complex issues and modeling financial scenarios, always with a focus on real-world value.

Greg Lacy
Greg Lacy, Enterprise Architect

Diversification matters. Operating across products, customer segments and geographies gives us resilience and flexibility to invest where we see the most opportunity.

Steve Cunningham
Steve Cunningham, CEO

Enova’s approach to tech innovation is grounded in using data, machine learning, cloud technology and mobile tools to improve both business performance and customer experiences.

“Enova continues to lead our industry in developing great tools for our business and our customers. In particular, we have access to a wealth of data and experience from over 5 million customers and over $18 billion in loans and leverage machine learning to build better risk and fraud models. We leverage the cloud to scale quickly and we use mobile to reduce friction for customers. We are also forward-thinking and are researching technologies on the horizon that have the opportunity for continued disruption.”

Dan
Dan, Head of Software Engineering

Data science plays a central role in Enova’s innovation strategy, with teams building machine learning capabilities that support internal brands and extend impact through Enova Decisions clients.

“As Enova’s data science team (internally known as RAP: Research Architecture and Platforms) we are excited to be one of the core drivers of our machine learning initiative–and, we are excited to address these challenges. We are confident that we can have a significant impact on all internal Enova brands as well as on external Enova Decisions clients.”
 

Hakan
Hakan, Sr. Manager, Analytics

Enova Employee Reviews

We stay focused, yet move very quickly. We empower teams to make critical decisions — to 'Be Bold and Move Fast.' It's how Enova innovates at scale, and how our work can quickly translate into value for our customers.
 

Enova
Enova

My passion is innovation. It’s what I dream about and why I get up in the morning. Innovation at scale is even more exciting. There are very few companies in Chicago that are successfully innovating at scale, or actually need to innovate at scale. Enova is one of the few.
 

Dan
Dan, Head of Software Engineering
Dan, Head of Software Engineering

One of Enova’s key values is “Best Answer Wins.” We believe innovative ideas and solutions can come from anywhere and anyone. This is never more apparent than with machine learning models. I will often test multiple types of algorithms side by side to test for the optimal performance. The data-driven answer determines the model that gets put into production. 
 

Emily, Data Scientist
Emily, Data Scientist

What People Are Saying About Enova

  • Process Innovation: Company filings describe a fully integrated, real-time decision engine that automates marketing, fraud, underwriting, customer contact, and collections with sub-second assessments and large-scale ML orchestration. Materials also indicate growth in originations and receivables that imply this system operates at high throughput and complexity in live markets.
  • Product Innovation: Disclosures note that Enova productized its in-house decisioning as Enova Decisions for external clients and expanded capabilities via acquisitions like OnDeck and the pending Grasshopper Bank deal. This reflects converting internal technology into market offerings while broadening product scope and distribution.
  • Experimentation Culture: Company content describes structured programs such as the Fellowship that give teams dedicated time to build and prove new ideas, reinforcing a culture of continuous innovation. Examples like the ArchBot AI code reviewer and ongoing exploration of emerging tools underscore practical, applied experimentation.

Enova's Tech Stack

Golang
Golang
LANGUAGES
Java
Java
LANGUAGES
JavaScript
JavaScript
LANGUAGES
MongoDB
MongoDB
DATABASES
PostgreSQL
PostgreSQL
DATABASES
Python
Python
LANGUAGES
R
R
LANGUAGES
Ruby
Ruby
LANGUAGES
Ruby on Rails
Ruby on Rails
FRAMEWORKS
SQL
SQL
LANGUAGES
Vue.js
Vue.js
FRAMEWORKS
Illustrator
Illustrator
DESIGN
JIRA
JIRA
PROJECT MANAGEMENT
Photoshop
Photoshop
DESIGN