When Is Credit Risk Too Restrictive?

Early in the year, T‑Mobile USA were interviewed by CNNMoney, and it became evident that 50 percent of T‑Mobile’s customers did not qualify for its top promotions… So how do you balance good risk management policies and limit financial exposure, whilst ensuring you can retain customers, upsell and grow revenue?

It is assumed that in North America, Australia and much of Europe, Communication Service Providers (CSPs) will undertake a credit check every time they sign up a new contract. They do this because they want to make sure the customer will pay the bill at the end of the month. Credit checks are also performed for existing customers that request new services. The exposure of any one CSP differs depending on a number of factors, including the cost of services, if they provide subsidised handsets, competitive promotions, target market, etc.

But it is restrictive to only consider the exposure via a third party credit bureau, as demonstrated by T‑Mobile suffering a rejection rate much higher than they wanted. So how should you define the threshold to accept or reject a customer? T‑Mobile decided to stop relying solely on traditional checking credit when existing customers renewed/updated their service. Instead, they will judge customers on their previous payment behaviour with T‑Mobile. This sounds like common sense, but it is not a new concept and is also used by some of our customers. However, this does highlight that credit bureau information is not the answer if the telecoms industry wants to move forward.

An holistic, comprehensive, deeper and richer analytical approach is required, but is this possible? When assessing the credit risk of existing customers, I think it is. There is a lot of data on existing subscribers, from how many times they have called customer care, when and how they pay their bills, what previous service requests have been registered, what previous campaigns the subscriber was interested in, where they were provisioned, the deviation in calling behaviour over a defined time, and more. All of this information can be collected from disparate sources and used for improved and refined customer segmentation, leading to more precise decisions.

In the case of new customers, collecting data from various sources in the business is also possible. Data may be obtained at the point of sale, from dealers, by identifying individuals who are already listed under a corporate account, from previous historic applications, from consortium data with your regional peers, and so on. All of this data will contribute to an improved risk assessment, and more insightful decision making.

As Big Data grows from just a theory, and formulates into a usable asset where issues relating to privacy have been solved, there will also be enhanced collection of information from third party marketing companies, from IoT sources, and more. This will further enrich decision making.

Adding Next Best Offer (NBO) to your provisioning strategy is a good way to manage risk, as it enables a hybrid approach where exposures can be managed by suggesting customers take lower-risk alternative offers. Offering customers tailored products would be a positive step towards proactive customer management.

It would be interesting to compare the current practice and future plans for how CSPs and financial institutions assess their credit risk. With mobile money growing year after year, both industries will need to open up to a collaborative relationship moving forward.

This article was originally published on the corporate blog of Neural Technologies. It has been reproduced with their permission.

Luke Taylor
Luke Taylor
Luke is the founder of Risk Reward Awards, an association whose goal is to encourage recognition of the best work done by risk professionals. Previously he was the Group Chief Commercial Officer and Deputy Chief Executive Officer of a risk management software developer. Luke now divides his time between Risk Reward Awards, RAG and Lateral Alliances, his consultancy business where he works with the likes of Symmetry Solutions, XINTEC, GBSDTech, Yates Fraud Consulting and Focus Data, to name a few.