Three Steps to Fight Telco Fraud
Minimize Risk, Maximize Activations:
Three Steps to Fighting Telco Fraud
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.
Last year, telco fraud increased 12%, worth an additional $38.95 billion lost and with the rising cost of handsets, fraudsters are getting away with higher value products and services. It’s becoming 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.
SIM swapping:
Where attackers manipulate providers’ security protocols to hijack users’ phone numbers, allowing unauthorized access to sensitive personal data and financial accounts.
- Access
- Analyze
- Action
Access
The first step to fighting 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.
Analyze
Step two is Analyze: accurately analyze all the data you’ve accessed. And don’t just analyze it the old fashioned way – integrate embedded intelligence like 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.
Action
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.
International Revenue Share Fund (IRSF):
Part 2:
Three Things Telcos Should Know About Alternative Data
1. What is alt 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 nearly 30 million “credit invisibles” in the US and close to another 10 million in Canada, joined by 70% of Latin America’s population, 70% of Southeast Asia’s, and almost one quarter of the entire world – there are nearly 1.4 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 to pull alt 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 alt 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.
Where does intelligent risk decisioning come in?
Intelligent, holistic risk decisioning solutions can play a pivotal role in empowering telco providers to combat fraud effectively. By leveraging real-time data integration (ahem, the three As already covered) and machine learning, these advanced fraud solutions can analyze vast amounts of data from multiple sources at every stage of the customer journey. This enables you to ensure that fraudulent activities are detected and prevented before they escalate, enhancing speed, accuracy in decision-making, and improving the subscriber experience. Provenir customer MTN was able to stop an additional 135% of high-risk transactions via fraud mitigation solutions, without adding friction to the application process. Implementing intelligent risk decisioning not only mitigates fraud but also improves operational efficiency and enhances the overall customer experience. Ready to fight back?
Discover how Provenir can help you maximize subscriber value, minimize risk, and enhance customer satisfaction.
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