Get the Data Superpower: X-Ray Vision
EXPAND YOUR LENDABLE MARKET
credit scores alone! So, what’s the alternative?
To engage the low-risk population of this potential market for both wireless service and handset financing you need to expand beyond the traditional credit score as an indicator of creditworthiness. Instead, integrate alternative data sources into your decisioning processes to look at alternative indicators of credit risk such as utility and rent bills, spending habits, and employment history. When combined with AI/ML capabilities this aggregation of data types can empower you to confidently assess risk of those who may be credit invisible or thin-filed. To enter the unbanked/thin-filed markets and activate low-risk subscribers, choose solutions that can: • Access alternative data sources • Analyze alternative data with predictive machine learning models to create risk scores • Action the alternative data to make instant decisions
A traditional approach to decisioning relies on using credit scores as the primary method of determining creditworthiness. Why? Because it’s reliable, the processes are already in place, and legacy technology can make it a challenge to try anything new! Let’s get one thing straight: credit scores are an effective predictor of risk. But when it comes to expanding your lendable market, using only credit scores means you often end up filtering out the low-risk applicants that could be a valuable subscriber. There are millions of unbanked and thin-file customers across the globe that usually include new to credit, immigrants, and other underserved communities. In the US, there are nearly 50 million credit invisibles 5 who do not have access to handsets or mobile service or mobile service, and north of the borther 15% of Canadians are classed as underbanked 6 . In Latin America, where cash is still the preferred payment method, 70% of adults are unbanked or underbanked. Globally, over 1 billion people have no credit history - that’s almost ¼ of the world who wouldn’t be approved by
Having the right data at your fingertips is like having x-ray vision: you can see deep beneath the surface to discover what others can’t. From the first point of contact, data unlocks insights into a potential subscriber’s risk and authenticity. The more data points your technology can gather, the easier it is to verify legitimate applications and flag bad actors.
The same goes for engaging the unbanked - new sources of data allow telcos to create scorecards from alternative credit signals. Use x-ray vision to look at data like: • Social media presence: it’s a red flag if social accounts are new, don’t exist, or don’t have activity • Rent payment history: if the applicant has paid on time consistently, it could indicate creditworthiness
MINIMIZE LOSSES – DEVELOP A SIXTH SENSE FOR BAD DEBT
Predicting danger is a superpower most of us would love to have. Wouldn’t it be great if your risk management team could sense payment delinquency before it occurs? Bad debt can leech up to 2% of your revenue 7 . While that may seem like a drop in the bucket, for a company like Deutsche Telekom, which produced over $80 billion in 2020 8 , 2% is over $1.5 billion lost. Even with so much at stake, many traditional risk management models only monitor payment status. If a subscriber’s creditworthiness changes in between billing cycles, you don’t get alerted until after they miss a payment.
So, what’s the alternative? Along with using robust credit risk analysis at onboarding, as mentioned above, risk monitoring should continue across the full customer lifecycle. Implementing early warning signs gives you that sixth sense urging you to take action and reduce credit exposure and defaults. There are two highly effective tactics that will optimize your proactive risk management strategy: • Incorporating behavioral data and insights into your models • Regularly monitoring account health regardless of payment status
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