There are common problems we encounter around credit risk and fraud detection. These problems are dominant in industries like financial services, but are used in pretty much any company that extends credit by performing services in advance of payment, or where there is opportunity for people to financially take advantage of an organization via fraud.
Some prominent examples are in telecommunications, utilities, insurance and healthcare. The business objectives here can be pretty straightforward. First, we’re trying to balance the way we provide products and services to the market with the risk that we might not get paid for fully for those products and services. Based on the credit worthiness of an individual, an organization might structure its products differently.
For example, a utility might require a deposit, or a mortgage company might offer lower or higher interest rate to different borrowers. We might also want to try to detect when something fishy is going on. This can mean different things in different organizations. One familiar example is with credit cards, where mechanisms are put in place to detect abnormal purchasing behavior, and may even put an automatic hold on the account when certain activity is observed. Typically, a company extending credit will develop their own risk models based on both internal data and external credit bureau data.
The methods involved are generally predictive analytics methods like logistic regression, decision trees, or neural networks. For fraud detection, we might employ predictive models, but we may also use things like descriptive statistics, statistical process control or other data driven heuristics to trigger interventions based on observed activity.