It’s time to look at the bigger lending picture

With continued advances in machine learning, credit providers are increasingly turning to technology to build more predictive scorecards. However, the development of these scorecards mainly focuses on the microeconomic environment which considers how individuals make decisions based on the distribution and utilisation of resources (income) For example will a consumer pay back a loan based on their past credit history? Do they appear to have enough income versus expenditure? Or, what is the purpose of the credit?  Although these individual factors are predictive, they only consider part of the picture and don’t reflect the wider scope of the economic environment we live in.

Macroeconomics is the study of economic factors that affect a country’s overall financial condition, such as interest rates, unemployment and productivity. The unemployment rate is always a good indicator of a country’s economic health. Higher unemployment denotes less people in work, which decrease’s productivity and therefore leads to a lower gross domestic product (GDP). As such, the lower the unemployment rate the better.

So how does this relate to scorecards?

I believe credit providers need to consider these macroeconomic factors when making lending decisions. In 2015 a paper published by the National Bureau of Economic Research titled “Can’t Pay or Won’t Pay? Unemployment, Negative Equity and Strategic Default” looked to examine the correlation between household finances and mortgage decisions. The study found that heads of households who defaulted on their loans demonstrated a 21% unemployment rate, while their spouses in the same household had a 31% unemployment rate. They then compared these figures to households that had not defaulted on their mortgage and found that heads of households had only a 6% unemployment rate and while their spouses had a 13% rate. This represents a 15% and 18% increase respectively. Therefore the conclusions of the study suggest that as unemployment increases, default rates should also increase. If true, credit providers could incorporate unemployment rates into individual scorecards to pinpoint job sectors that are affected disproportionately by high and low unemployment rates, e.g. the probability of that person losing their job in times of economic depression.  

So how do we predict changes in the unemployment rate?

As mentioned, unemployment is directly linked to productivity which is linked to GDP, therefore both monetary and fiscal policy set by government can directly affect a country’s unemployment rate. Two economists Robert Mundell and Marcus Fleming developed the Mundell-Fleming model which considers the short-term relationship between exchange rates, interest rates and output in an economy.  One factor of their model shows that as money supply increases (due to decreasing interest rates) overall output and income increases as a result. The model predicts that as interest rates decrease output will increase due to the cheaper cost of borrowing, which in turn encourages consumers to borrow and invest more. This increase in consumer spending results in an equal increase in demand. This is followed by an increase in supply which results in more hours of work needed, therefore leading to more people in employment. If the results from the study by the National Bureau of Economic Research hold true then the economy should experience lower default rates as a result of increased money supply.

So how well does this model hold up in the real world?

In the last decade, the US economy has kept its interest rates at an all-time historic low to try and encourage economic growth. This increased the country’s money supply which led to continued increases in GDP as well as continued decreases in unemployment. The S&P/EXPERIAN consumer credit default composite index, which measures default rates across autos, first and second mortgages and bank cards, currently stands at 0.88 which is low if we compare this to 2009, the height of the economic crisis, where the index stood at 5.50. So it would appear the model holds up well, however, we see a different picture emerge in the UK economy.  

The UK like the US has had historically low-interest rates which has continued the country’s money supply increase along with its GDP. As a result, the unemployment rate has been dropping, however unlike the US the UK is experiencing higher default rates than ever. So what’s gone wrong?

Well, the answer isn’t black and white, there are other economic factors at play such as Brexit and the concern over the fact that consumer credit is increasing faster than household incomes.  As such it makes it harder to paint a clear picture and therefore would require further research. However I firmly believe that credit providers would benefit by adapting their scorecards to keep up with the constantly changing economic environment of which the unemployment rate is one factor. One major disadvantage of many current score cards is that they fail to account for, and therefore predict, changes in the economy, which as shown do have a direct effect on consumer borrowing and defaults. As such, many scorecards pre 2007 failed to predict the unprecedented amount of consumer defaults on credit as they mainly focused on microeconomic factors. It is my opinion that if more credit providers applied more macroeconomic concepts into their scoring, they may find themselves more prepared for when the next crisis hits.