Companies need to receive payments on time. If they don’t, it makes it challenging to keep cash flow positive. But of course, in today’s cash-strapped society, that’s always a challenge.
To receive payments on time from some clients can seem impossible. That’s where this post can help. We look at some of the ways machine learning makes it easier to predict when a customer won’t pay and take corrective action.
Here’s everything you need to know:
How Machine Learning Predicts Customer Behavior
Machine learning is exceptionally good at predicting customer behavior. It does this by training a model on an existing dataset and then testing it on unseen datasets. The algorithm with the best performance is then selected.
Modelling professionals look at all sorts of variables when trying to determine whether a customer will make a payment. For example, they might look at their payment history or demographics to decide whether payment is likely. Algorithms then crunch the numbers and look for patterns in the data to indicate a higher or lower probability of a missed payment.
Of course, these systems don’t teach how to ask customer for payment. That’s another story. But they do provide an indication of when non-payment is likely, helping companies to reduce risks.
The Benefits Of Using Machine Learning
There are numerous techniques companies use to determine whether a customer is unlikely to pay. Most of these traditionally were based on statistical methodologies with assumptions like random variance and no serial correlation.
However, machine learning algorithms can bypass many of these methodological limitations. Programs can combine the data and work out what’s most likely to happen given the available information, even if it is poorly behaved. This means that firms can better allocate resources and reduce the risk of false positives and negatives. Instead, they can direct their resources toward the customers who are most likely not to pay and make better credit decisions. Furthermore, they can use the information to target customers who can provide a consistent revenue stream. Not everyone can.
The Drawbacks of Using Machine Learning
Of course, machine learning techniques aren’t perfect. These are always issues.
Machine learning, for example, can’t overcome biased data. If the sample isn’t properly randomized, then the results will not reflect the true probabilities of non-payment events occurring.
The only way to overcome this issue is to improve the methodology for sampling data. Machine learning systems must only receive data that makes sense to them, given the reality of the non-payment situation. Training data must be robust to outliers.
Furthermore, it is unrealistic to expect perfect predictions. Machine learning models can only say what’s likely to happen, not necessarily what will happen.
Therefore, you will need to take their output with a grain of salt. Sometimes, it’ll be right, but not always. The best way to use machine learning for credit assessment is to use a model built by an existing team. You want something that will provide accurate predictions and already contains corrections for things like bias.
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