JUMO’s Chief Information Officer, Paul Whelpton, has spoken on many platforms about the important link between credit modelling based on machine learning — a branch of artificial intelligence — and how it can reduce systemic bias to make better crediting decisions and improve financial inclusion.
Millions of people are excluded from financial services and so far, traditional financial players have not been able to find a viable way to get credit into the hands of these financially excluded groups. Billions of people around the world still lack access to financial services. To fix this, we need to connect entrepreneurs and ordinary people to the financial tools everyone needs to grow and prosper. It’s a supply issue, not a demand issue. Using artificial intelligence in finance is the quickest route to this.
How artificial intelligence can help individuals receive credit
AI credit learning can play an important role in building a more inclusive economy, as it makes it possible for anyone with a phone and mobile wallet to make transactions and develop a financial identity.
Through AI, the relative machine learning credit models can be tailored to enhance and eventually replace outdated credit thinking that has left ‘others’ out over the years.
The problem with financial exclusion
One of the biggest issues when it comes to financial inclusivity is biased credit scoring. To illustrate the bias that can exist with credit scoring, we can look at the example of a small-time farmer in Uganda. With a drought predicted in the country, there is an expectation that her crop yield will be significantly smaller this year. For a credit model, the risk goes up because the probability of default is higher
How traditional lending views risk
Under traditional credit thinking, you have a higher probability of default and there is a price for risk (e.g., in this case, the price increases from 10 percent to 17 percent). Also, under traditional credit thinking, facility size is reduced and the annual facility fee is increased. Under this outcome, when it doesn’t rain and the crop yields less, interest expenses are high and there is reduced income, leading to default. A self fulfilled prophecy that further validates a flawed model and credit philosophy.
AI in the financial sector
However, AI could be used to help the farmer in our example to access a loan by providing a more accurate credit score, even for the unbanked. But how does Artificial Intelligence help financially excluded people? Through AI, the relative machine learning credit models can be tailored to enhance and eventually replace outdated credit thinking that has left ‘others’ out over the years.
How JUMO is working towards financial inclusion
In order to improve financial inclusivity, we need to work to eliminate biases. There are a number of ways in which we can do this. For instance, there is transactional bias. Is a thin file (a banking term for customers with limited or no credit record) really a measure of risk? On top of that there is gender bias, as well as behavioural bias. We need to understand that not all customers are the same, and to help with this issue personalisation is key. These are just some of the biases that have kept people out of the economy, as traditional credit thinking has created a less-than-welcoming atmosphere.
Ultimately, bias plays a sizeable role in creating a less inclusive economy. By using AI credit modelling, we can work toward eliminating bias and creating an economy that includes far more people who have been previously left out. AI can speed up the process of loan approvals and improve risk assessment processes to the benefit of all ecosystem players.
More about machine learning
View the recording of Paul’s presentation at the AI in Africa Conference