Do you have billions of dollars to spare? If not, keep reading.
Telecommunications (telco) operators lose an estimated $40 billion to fraudsters each year, and it’s getting worse.
Fraud increased 28% from 2019 to 2021 and with rising costs of handsets, fraudsters are getting away with higher value products and services. It’s become harder than ever to identify fraudulent behavior as it becomes more complex – there are more than 200 types of fraud within the telco industry alone. The problem clearly isn’t going away any time soon.
But don’t throw up your hands in defeat just yet! Telcos can fight back with three highly effective tactics that together can reduce bad debt up to 69%. Just use the three As:
At the core of it all? Another A: alternative data. Feeding alternative data into each step of the fraud mitigation process is the key to recapturing billions in annual losses.
The first step to fight fraud is Access – accessing data, including alternative data, provides more thorough information for fraud and KYC checks during the activation processes.
A common kind of fraud at this stage of the customer lifecycle is subscription fraud, which can be very costly. Fraudsters use stolen IDs and credit card information to create accounts, buy expensive handsets, and either pocket the free merchandise or resell it. If the criminal is purchasing a state-of-the-art smartphone, that’s potentially thousands in lost revenue from a single scheme.
Access to a deep well of traditional and alternative data sources empowers you to identify even the most subtle abnormalities during fraud and KYC checks at onboarding. For example, synthetic IDs are commonly used by fraudsters to open accounts, which can be difficult to catch, since synthetic IDs use some legitimate elements to fly under the radar. Alternative data can give you the clues you need to spot fraud, even in cases like this. Check the email to see if there are any minor changes or see if the geolocation matches social media activity.
Step two is Analyze: accurately analyze all the data you’ve accessed. And don’t just analyze it the old fashioned way – integrate machine learning and AI into your analytics.
Say a phishing victim has had their phone breached and the criminal has text forwarding activated so they can receive a security code. AI/ML analysis of mobile data could alert a risk team that texts are being forwarded, and suggest further checks be performed.
Tactics like account takeover can cause damage even after onboarding. Imagine having to catch tiny inconsistencies for hundreds of thousands of subscribers throughout the entire lifecycle all on your own. It can be a challenge for legacy decisioning solutions to identify complex fraud indicators.
Having smart, automated technology that can pick out unusual data and analyze it quickly and accurately will make the difference for both new and active subscribers. Machine learning and AI gets smarter as it analyzes data and behavior, getting better at recognizing fraudulent patterns that would have otherwise been overlooked.
Optimize your fraud process with machine learning and AI technology that can analyze any kind of data and improves its accuracy with each analysis.
The final step to help you stop fraud is Action: when you have accessed all the traditional and alternative data you need and AI/ML has analyzed it, you are ready to decision.
If the first layer of checks don’t yet paint a clear picture of the legitimacy of a subscriber, your decisioning solution can look deeper into the data for further analysis. Depending on your model, you might instead offer them a plan for high-risk subscribers, or reject them outright. If everything checks out, on the other hand, your decisioning engine would then approve and onboard.
Advanced decisioning uses all of the data you’ve gathered to make the most accurate decisions- that protect you against fraud. It improves efficiency and saves you money by performing only necessary checks – you never have to take a one-size-fits-all approach.
Once decisions are made, the outcomes are fed back into the platform, adding even more valuable data and analysis to help the AI/ML technology guide your decisioning to more accurate decisions in the future.
We’ve seen some examples of how alternative data can fuel a decisioning engine to fight fraud, but what is it exactly? Check out the top three things telcos should know about this powerful tool.
Part 2: Three Things Telcos Should Know About Alternative Data
The financial landscape is vast, especially at a global scale. Telco spans that landscape, as wireless services and products like handsets and modems are in high-demand among people from all financial backgrounds. To reach them, you can’t only rely on traditional data like credit scores to determine risk of default. Collecting and using alternative data can help you impact countless lives, tapping into an enormous worldwide market.
1. What is alternative data?
It’s not data that wears eyeliner and plays guitar – it’s a powerful tool for financial inclusion.
Simply put, alternative data is all the information not maintained by credit bureaus that can paint a more holistic picture of a person’s financial health and overall risk. It can include financial information like rent, utility, or even telco payments, but also analyzes other information like social media activity, geolocation, and property records.
Alternative data can tell a more complete story than traditional data alone. There are 50 million “credit invisibles” in the US, joined by 70% of Latin America’s population, 60% of Southeast Asia’s, and almost one quarter of the entire world – there are nearly 1.7 billion people without banking or credit history. That’s an awful lot of people who wouldn’t be qualified to open a telco account via traditional methods alone.
And while credit scores have proven to be strong indicators of whether someone will pay their bills on time, doesn’t it make sense to actually take into consideration utility and other recurring payment patterns to predict the same behavior for telco? Over 90% of Americans make payments on financed mobile phones, but only 2.5% of consumer credit bureau files contain telco payment information. While you might have the payment records for your own subscribers, being able to access that information for those looking to switch operators would be a reliable way to determine risk. Layering in utility data on top of credit scores gives you highly relevant insights to provide even stronger indicators of risk.
Telco, utility, and lease/property information is often highly indicative of credit trustworthiness but just isn’t considered by credit bureaus. That’s why alternative data is so powerful.
2. How do you pull alternative data?
Telcos can access alternative data through public records, along with any data partners you might have integrated into your decisioning solution. These data partners could share social media activity, employment information, and more – what you can access all dependent on your region’s compliance rules and regulations around credit decisioning.
While this information may not have as direct a correlation with credit trustworthiness, it can give you a fuller picture of someone’s lifestyle. Social media, for instance, can be a very enlightening source of alternative data, giving you insight into activities and habits that may be relevant. As more social media companies begin to offer embedded payment options on their platforms, someone’s Instagram profile could provide you with a look into their transactional behavior. Understanding how often a person shops on Instagram, how expensive the items they buy are, and if these purchases relate to the timeliness of their bill payments could be helpful ways to analyze this behavior.
Make sure you have access to data integrations and partners that will offer you the widest lens within the required parameters to look at subscribers in order to get the best results from alternative data. Choosing technology that can accelerate partner integration and alternative data access will guarantee rapid ROI, connecting you with more subscribers, faster.
3. Does alternative data work?
Yes! Credit scores may not necessarily reflect a person’s current financial health, as the score heavily weighs past credit behavior in addition to current behavior. Even if someone is very responsible in the present, bad decisions from their past could still negatively affect their credit. If you ran that person’s profile through your traditional decisioning process, they might get flagged as high risk, leading to an inaccurate assessment. The same would be true of someone who never had access to credit due to past financial status or discriminatory lending practices. Alternative data solves that problem.
And there’s evidence to support it: 64% of lenders/credit providers that use alternative data see improved risk assessment, 48% have an increase in offer acceptance, and 64% see tangible benefits within one year of implementation. Other benefits include improved decisioning accuracy, better fraud protection, greater financial inclusion, faster speed-to-market, rapid onboarding, and overall maximized value.
We’re living in an era where information is as accessible as it’s ever been – it’s time to use it. The telco industry is at the forefront of innovation, so why keep assessing creditworthiness the same way we did decades ago? When you integrate alternative data into your decisioning, you’re making the world even bigger for millions of people who need telco services and inviting in low-risk subscribers that will accelerate your growth.