DATA SHEET: Fraud for Telco
Provenir: Application Fraud for Telcos
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Parsippany, NJ – February 12, 2025 – A new survey shows nearly half of all financial services executives are struggling with managing credit risk and detecting and preventing fraud. The survey also shows many are revamping their credit risk decisioning and fraud prevention strategies in 2025, with AI playing a prominent role.
These are among the key findings from the survey of nearly 200 key decision makers at financial services providers globally to understand their risk decisioning and fraud challenges across the customer lifecycle, decisioning investment priorities, and AI opportunities. The survey was conducted by Provenir, a global leader in AI Decisioning solutions.
Over half of all respondents plan to invest in risk decisioning solutions and AI/embedded intelligence in 2025 and beyond. At present, nearly 60% of respondents say they find it difficult to deploy and maintain risk decisioning models. 55% of executives recognize the value of AI to make streamlined strategy decisions, and in its ability to provide AI-powered performance improvement recommendations, and 53% see the value in the ability to automatically tune models to make better, more accurate decisions.
Key priorities for customer and account management are real-time, event-driven decisioning (65%), eliminating friction across the customer lifecycle (44%), and increasing customer lifetime value (44%).
Over half of respondents agree the biggest data challenge they face is being able to easily integrate data sources into decisioning processes.
Survey insights also reveal the pitfalls of operating multiple decisioning systems across the customer lifecycle. 59% of respondents say this is causing a lack of seamless data flow and unified insights, while 52% say it creates operational inefficiencies. Additionally, 28% said it contributes to an inconsistent customer experience.
When asked about data and fraud, 37% say they struggle with effective data orchestration for application fraud prevention, specifically in not being able to easily ingest and integrate new data sources, while 36% are challenged in using AI and machine learning for fraud prevention. Nearly one-third of respondents agree that the most important aspect for comprehensive fraud strategies is the ability to break down data silos between fraud and credit risk teams.
“Financial institutions are keenly aware of today’s increasingly complex threat landscape and must adopt new approaches for improved risk decisioning and fraud prevention across the customer lifecycle while providing frictionless and personalized customer experiences,” said Carol Hamilton, Chief Product Officer, Provenir. “With an AI decisioning platform more closely aligning credit and fraud risk teams, financial services executives can ensure holistic, end-to-end decisioning with a complete view of customers across the entire lifecycle.”
The survey was conducted November-December 2024; respondents were based in North America, EMEA, Latin America and Asia Pacific, holding the titles of manager, director, vice president, or above.
The full report of the survey findings can be found here.
Financial institutions will continue to face a complex set of both challenges and opportunities in 2025, including customer management, tech advancements, and the always-evolving world of fraud prevention. How can you position your organization for success this year? Check out the article from Finextra, featuring Provenir’s EVP of North America, Kathy Stares, for all of the insights.
Parsippany, NJ – January 22, 2025 – As financial institutions endeavor to balance risk with opportunity across credit, fraud, and identity/compliance, a new executive brief from Chartis Research details the most crucial technology capabilities to address key and emerging credit decisioning use cases.
Organizations must verify identity quicker, detect fraud earlier, and make more accurate credit decisions overall. The Chartis Research brief outlines key requirements for “best-in-class” decisioning, including the application of Machine Learning and AI, automation in vital areas such as Know Your Customer (KYC), and real-time fraud alerts. The importance of cross-institutional data sharing and data-driven identity verification and scoring for real-time risk assessment and synthetic ID fraud detection are also emphasized as key capabilities.
Per Chartis Research analysts, Provenir, a global leader in AI Decisioning Software, provides best-in-class market-leading functionality across the board.
“Chartis considers Provenir to be a global leader in software and services, providing top-tier RegTech and risk products to financial institutions across the globe,” said Anish Shah, Research Director at Chartis Research. “Provenir exhibits best-in-class capabilities nearly across the board, with comprehensive solutions for credit risk decisioning, credit monitoring, credit risk management, credit portfolio management, identity verification and fraud and ID monitoring and management. The company’s adoption of such advanced technologies as AI and ML has enabled it to provide an industry leading automated workflow framework that addresses the market challenges around credit and fraud risks. It also provides a robust analytical framework that allows financial institutions to analyze data and make timely decisions in real time. The fact that Provenir delivers on both these fronts is distinctive.
“Due to the highly configurable nature of its platform, Provenir empowers clients across a range of industries, including payments, banking, digital banking, small and midsize enterprise (SME) lending, credit unions, FinTech, telecom, auto financing, buy now, pay later (BNPL), consumer lending, credit cards and embedded finance.”
“As the financial services landscape evolves, it is clear that combining credit and fraud management is no longer a choice but a necessity,” said Carol Hamilton, Chief Product Officer for Provenir. “Provenir’s AI Decisioning Platform empowers institutions globally to streamline operations, combat fraud, and drive better business outcomes. By leveraging cutting-edge AI and Machine Learning technologies, Provenir enables organizations to make smarter, faster, and more accurate decisions, driving success in an increasingly complex and regulated market.”



















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.
Where attackers manipulate providers’ security protocols to hijack users’ phone numbers, allowing unauthorized access to sensitive personal data and financial accounts.
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.
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.
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.
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.
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.
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.
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?





As fraud continues to increase in the automotive industry, the impact it has on financial services providers and vehicle buyers is significant. Thanks to the high-value transaction of car buying, and a growing shift towards digital loan applications, fraudsters are finding increasingly sophisticated ways to exploit vulnerabilities in the system. And it’s working for them – automotive fraud is up by more than 50% this year versus last year. Auto lenders are caught in a high-stakes environment, forced to balance the need for instant loan approvals and seamless customer experiences with robust risk management and fraud prevention measures.
This need-for-speed in application processing, driven by consumer expectations and mounting competitive pressure, can create gaps that fraudsters are ready to exploit – putting your financial stability, profitability, and industry reputation at risk. So we’re looking at common fraud schemes, the impact fraud has on the auto industry, and actionable insights for technology-driven solutions that can help you combat fraud in an increasingly digital, high-risk world.
Why is fraud so prevalent in auto lending? There are several factors that make auto financing an appealing target for fraudsters:
Auto loans tend to be high-volume and high-value, meaning successful scams can yield substantial financial rewards.
Investment in the right technology is key to a successful, proactive approach to fraud and risk management. The foundation of future-proofing lies in adopting scalable, cloud-based solutions that are capable of adapting to changing fraud threats. Cloud-based platforms offer you flexibility and real-time updates, while AI-driven tech enhances fraud detection by rapidly and accurately analyzing large datasets to identify subtle, complex patterns that can otherwise slip through the cracks. And advanced AI tools will continuously learn from your fraud decisions, allowing you to refine fraud detection processes and stay ahead of fraudsters.
Provenir’s AI-powered fraud solutions offer you:
Bring your own data or connect to one of our market leading partners using Marketplace integrations





Technology is an enabler of growth, but it can also be a hindrance to efficiency. When you’ve accumulated outdated, redundant, or overly complex tech systems, you may feel the pressures of ‘tech bloat.’ Check out the recent article with FinTec Buzz, where Brendan Deakin, Provenir’s General Manager of the U.S. shares his thoughts on how to reduce tech bloat in order to improve efficiency, security, and innovation.

The African market, characterized by rapid digital growth and increasing financial inclusion, is facing a surge in application fraud. Traditional fraud prevention methods are struggling to keep pace with the evolving tactics of fraudsters. This webinar will explore how a holistic onboarding decision platform can revolutionize fraud prevention in Africa.
We will discuss the key challenges of application fraud in the African context, including identity theft, synthetic fraud, and social engineering attacks. We will then delve into how a decision platform can address these challenges by:
By adopting a holistic approach with a decision platform, organizations can enhance their fraud prevention capabilities, protect their businesses, and ensure a positive customer experience in the dynamic African market.

Provenir
Alliance for Innovative Regulation (AIR)

Provenir