Russell Green, co-founder of Ravens Wood Capital, shares his experiences of building a strong foundation to credit risk management at a start-up.
Have you ever wondered how to decide how much and at what price businesses are charged for borrowing money? It's not a topic that tends to come up at a party or around the dinner table, but it’s a question lots of business owners, sales-people and risk specialists would benefit from knowing as it sends a signal to them about the perceived risk, whatever the product.
Prior to starting SME Finance consultancy, Ravens Wood Capital, I was responsible for risk management at alternative finance lender, Growth Street (2016-18). As the first risk employee after the Co-Founder, I had the unenviable position of growing lending volumes through underwriting and protecting the provision fund from unnecessary defaults. The price of debt was a key mechanism for protecting the fund, as part of the interest rate was paid into the fund each month. The fund would grow in line with the risk of the portfolio.
Implementing risk management tools and techniques in any company can be difficult, let alone in an early stage company with huge growth aspirations. We knew we had to use well-established data and traditional models to supplement our internal processes to engender confidence from our investors and give a reference point for the portfolio compared with more established assets, but we also needed an edge.
Our data providers were well-known names in the credit market giving us a great foundation, backed up by many years of data. They gave us the reference points; our people and processes gave us the edge. By speaking to all potential borrowers, we were able to apply qualitative scoring overlaid with our data. This served us well and provided a unique selling point to our borrowers who would once have had a relationship manager at their bank.
Underwriting now had an effective risk management process that would scale and provide ongoing data points for in-life monitoring. In-life monitoring is a very different risk management discipline to initial underwriting and is something which doesn't happen in all lending institutions. This is short-sighted and dangerous in my opinion, particularly on medium and long-term dated products. How can you possibly know how a company will perform over the three years of a loan? You simply can't and even with the best data in the world, proactive monitoring will win every time.
Technology is the great enabler here, and for the machine-learning and AI followers, there is a mountain of data waiting to be put to use in the credit risk space! We had access to real-time data from the majority of our clients giving insight into trading now, and not in a month or 3 months’ time when they were obliged to provide updated performance metrics. These insights alongside the traditional data sources help to take proactive action on deteriorating risks and informed our front-end underwriting of weakening sectors and borrower characteristics. We didn't need to spend endless funds on an AI model to predict defaults, however, I am excited by the benefits this will bring when the company is ready.
Using market-leading external data and borrower real-time data we were able to build a strong foundation to credit risk management without hiring an army of analysts that helped inform the company on credit matters and potential marketing opportunities. How many credit risk departments can claim that?
