For years, Lenvi Riskfactor has helped lenders understand what’s happening across their portfolios - surfacing trends, highlighting risk, and supporting better decision-making.
Now, with our latest machine learning (ML) developments, we’re extending Lenvi Riskfactor into providing forward-looking intelligence.
By identifying patterns in client and debtor behaviour and forecasting potential outcomes, we’re applying new technologies where it genuinely matters - enabling lenders to act earlier, operate more efficiently, and uncover new opportunities for growth.
Harnessing real-world data
Machine learning hasn’t suddenly appeared on our roadmap. It’s something we’ve been exploring for many years.
What’s changed is our ability to bring it to life in a meaningful way that provides users with insights they can act upon.
Historically, like many in the market, we were constrained by access to the right data. Without sufficient real-world data, proving out models and demonstrating value was difficult.
However, thanks to our fantastic client base, that changed more recently, when we were able to work with real customer datasets which gave us a clear proof point for what was possible.
Why machine learning?
Machine learning also is not a departure from what Lenvi Riskfactor already does - it’s a continuation of it.
Lenvi Riskfactor has always been built on deep industry expertise, translating lender knowledge into measurable indicators. What these new machine learning capabilities enables us to do is to scale that expertise. Scaling it into identifying patterns, behaviours, and signals at a level that simply isn’t achievable through manual analysis alone.
In other words, this is not a reinvention - it’s an evolution.
Why receivables finance and ABL lenders should care
In today’s market, lenders are navigating increasing complexity.
Portfolios are often larger or more complex,
Customer bases are more diverse, and
The speed at which decisions need to be made continues to accelerate.
At the same time, expectations are rising; lenders are under pressure to:
Improve efficiency,
Sharpen risk management, and
Drive a competitive edge to enable portfolio and client growth
Currently, the tools available within Lenvi Riskfactor to support this use established metrics and indicators that are highly effective at identifying emerging issues, enabling lenders to trigger interventions quickly and prioritise hot spots, while improving the efficiency of their teams. These tools are critical to maintaining portfolio discipline and effective risk management.
However, the introduction of our ML capabilities offers an additional layer of insight. Through forecasting behaviours, lenders are able to shift the timing of the insights they get into client and debtor patterns earlier. In doing so, improving the prioritisation of interventions at scale.
The time shift from reacting to events, to anticipating them, is where the real value lies.
How Lenvi Riskfactor have made machine learning work in practice
Our approach to machine learning has been based on the ongoing industry conversations about better utilising AI, and our knowledge of our clients’ and the broader receivables finance industry’s need for an application of these evolving technologies that works in their environment and works now.
At the heart of our development is the concept of client and debtor profiling.
Using machine learning, Lenvi Riskfactor analyses the characteristics and behaviours of clients over time. It looks at extensive combinations of factors – such as trading patterns, cash flow movements, utilisation trends - and learns what “normal” looks like for different types of clients. When similar patterns begin to reappear elsewhere in the portfolio, it flags them.
Sometimes, those signals point to emerging risk. Other times, they highlight opportunity.
For example, the same modelling can identify clients who may be approaching stress scenarios, or it may spot those who are well-positioned for growth and could benefit from increased facility limits.
Further, the model doesn’t operate in isolation - it works within the wider Lenvi Riskfactor ecosystem, combining its outputs with the metrics, alerts and workflows lenders already rely on.
That means the insights are not abstract - they’re actionable.
Machine learning functionality that’s built around your receivables finance business
A common challenge with machine learning solutions is that they are often presented as “ready-made” - trained elsewhere, established over time, and applied universally with the expectation that they’ll fit to every lender in the same way.
We’ve taken a different approach.
Our models are built using each lender’s own data. That means they reflect the reality of your portfolio, your processes, and your client base.
It also means the capability adapts to the client’s needs. Every lender has a slightly different way of working, different data points that matter, and different objectives. The machine learning engine within Lenvi Riskfactor is designed to allow models to be configured and improved over time as more data becomes available.
The result is something far more aligned to the way lenders actually operate.
Unlocking efficiency, growth and competitive advantage
While the initial conversation around machine learning is often focused on risk, the impact is much broader.
By surfacing where attention is needed (or not needed!) lenders can deploy their teams more effectively. Time spent on manual analysis and routine monitoring can be reduced, allowing resource to focus on higher-value activity.
At the same time, earlier insight opens up new opportunities.
Understanding where a client is likely heading allows lenders to act proactively. This could be by supporting them through a dip in working capital, identifying opportunities to extend facilities, or exploring additional funding options in advance of need.
Machine learning in this context can become a driver of growth as much as risk monitoring and control.
Machine and human working in harmony
We’re also very clear about what this capability is and is not.
Machine learning within Lenvi Riskfactor is not designed to replace human judgement. It is not a black-box decision engine that lenders are expected to follow blindly.
Instead, much like with the rest of the Lenvi Riskfactor system, it acts as a decision support layer.
It highlights patterns that may not be immediately visible.
It prompts further investigation.
It strengthens the quality of decisions being made
But always alongside the expertise of the user.
Our goal is, and always has been simple: to help lenders make better decisions, with greater confidence.
See it in action
Machine learning is often talked about in abstract terms. At Lenvi, we’re focused on making it real.
If you’re already using Lenvi Riskfactor, this is a natural next step in unlocking more value from your data.
If you’re exploring new solutions, it’s a clear indication of where the platform is heading - and how it can support more proactive, intelligent portfolio management.
Speak to our team to find out more and arrange a demo of our machine learning capabilities.
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