Q&A: Aro’s CTO on harnessing data intelligence in the lending process

Ellie Duncan,
19 Mar 2024

Nick Allen, chief technology officer at Aro, tells Open Banking Expo how the business uses data intelligence and machine learning innovations to improve the lending process, and how organisations can approach digital transformation projects.

1. When you joined Aro in 2017, was the business making use of technology to help lenders?

Not really. The business operated with some very old systems that weren’t designed with lenders in mind. The purpose of these systems was to support the brokerage services provided by our telephony agents.

2. How did you approach positioning technology at its heart and taking the business on a digital transformation journey?

Aro’s chief technology officer Nick Allen

Initially, I absorbed myself in the business’ activities and the key challenges it faced as a telephony broker. We re-envisioned the business as a 24/7 digital lending marketplace and defined the key principles of the business and its resulting technology. This resulted in the first iteration of our digital strategy, which we visually embodied in our platform blueprint. This kickstarted our digital transformation programme.

The principles positioned technology at its core, with three main pillars: Cloud, software engineering and data intelligence. To begin with, we had to get the basics right, so we invested heavily in creating capability through technical excellence in software engineering and cloud technology. This was followed by data intelligence, under which our machine learning capabilities would evolve.

Let’s not forget that, ultimately, to succeed, it’s about organisational culture, which is directly influenced by your team structures, people and processes. We implemented agile product squads aligned to value streams to improve speed, quality, and ownership to drive outcomes.

3. Why does Aro use data intelligence and machine learning in the lending process? How do Aro’s lenders benefit?

There are three main strands to this:

  1. We use machine learning models to predict the strength of the match between the customer and the lender’s offer. By presenting the customer with their offers based on this ranking, we aim to achieve the best customer outcome. This helps lenders gain precedence in the ranking, where previously they may have struggled to compete in a table of offers ranked by traditional means.
  2. Utilising the data we obtain to select the most appropriate lenders to search for the customer, reducing the costs incurred by lenders.
  3. We share data with the lenders on where they do and don’t offer, allowing them to understand where opportunities exist and how to improve.

4. How does this technology, as well as Open Banking data, help when it comes to financial inclusion in the UK?

Our goal is to offer the broadest coverage of credit products available. This gives the customer a simple way to view all their options in one place, knowing that there is breadth and depth in the available credit products.

To assist with expanding options beyond traditional credit file-based decisions, we then offer Open Banking as a way to share additional information with lenders. This aims to expand the offers available to customers who may have previously been rejected or had few other options.

By leveraging Open Banking data, lenders can more accurately assess borrowers’ financial history and affordability, reducing the reliance on traditional credit scores and providing a fairer and more inclusive lending process. Aro’s goal is to make it simple for customers to share this information with lenders, who will use it to make more inclusive offers.

5. As we know, digital transformation is not a ‘one and done’ investment. So, what’s next for Aro in terms of continuing to adopt new technologies?

If you’re doing this right, your technology never stops evolving. Leaving things to stand still just leads to degradation over time, until suddenly you’re facing legacy issues, or the advancements in tech have left you behind, and you end up needing a complete overhaul.

In addition, the cultural aspects of how the business works always evolve.

Here at Aro, we recognise a dynamic connection between the technology and product, the customer and our people, resulting in a fluid interchange of ideas, working practices and technology advancements.

Our immediate innovations centre on the concept that the business improves itself. By utilising AI technology such as machine learning and LLMs, and weaving this into the fabric of our modular software platform, we can improve the experience for our partners, lenders, and customers, while increasing the effectiveness of our internal organisation.

6. Do you have any advice or guidance for those firms that haven’t yet embraced technology such as Open Banking and machine learning?

I would advise you to develop a strategy for leveraging and integrating these capabilities into your current operations and aligning them with your business objectives. Consider starting with pilot projects to test and learn. Keep it simple to begin, so you can get working use cases off the ground.

Open Banking brings a lot of data, and knowing what or how to use it can be challenging. Work with a subject matter expert who can help make this data meaningful to your use cases.

Machine learning is easy to play with in a research capacity, but it’s much harder to deploy and operate in a production context when the models need to be regularly updated. Technical excellence is important to the long-term success of initiatives.

Integrating your people, product managers and data scientists with your software and DevOps engineers is a great way to ensure you’ll end up with a sustainable solution.

Watch Aro’s chief executive officer Emma Steeley discuss Open Banking use cases in the lending and credit space on Open Banking Expo TV