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Guest blog: How mobile data secure solutions can help prevent fraud throughout the customer journey

GUEST BLOG

How mobile data secure solutions
can help prevent fraud throughout the customer journey

  • Sarah Small, Global Partner Marketing Manager, Sekura Mobile Intelligence

It’s clear that the events of the last two years have forced the business world to undertake a digital reality check and left organisations racing to address the challenges created by the rapid and unexpected adoption of online services. This double-edged sword of rapid consumer behaviour change has created the ideal environment for organisations to rethink their business model towards a digital-first strategy, whilst unfortunately also exposing them to digital fraud and online scams in unprecedented volumes.

Changes to consumer behaviour – a playground for fraud

Much has been written since the pandemic hit about the impact of Covid 19 and the changes that society has had to make in both personal and working lives. High streets are struggling, major retailers have closed or moved exclusively online, and for a period of time our working and in part social lives became an online activity only.

But as ever, where the genuine consumers are, the fraudster will follow. With the dramatic shift to online digital transaction has come the inevitable increase in online fraud – social engineering-enabled scams, account takeovers and theft.

In the first half of last year, criminals stole a total of £753.9 million through fraud, an increase of 30% compared to the first half of 2020. UK Finance

And so, controls are needed. To create the appropriate level of protection in online channels, these controls must be applied across the customer lifecycle, from the initial account set up and throughout a user’s transactional journey, including verification of a customer’s identity at account application and onboarding, confirmation of ID at account login and by verifying device ownership and authenticating the user during transactions, payments, money transfer and through all sensitive online activities.

The ultimate goal is to achieve a secure and frictionless online experience for the genuine customer while making it as difficult as possible for bad actors to succeed. Mobile operator data can offer the solution.

The power of mobile data

While the world has been going through rapid digital change, global mobile operators have been working to get mobile identity services prepared and optimised in readiness to support the explosion of new services and the use cases that this has created.

Worldwide, mobile operators are recognising and commercially deploying their digital identity services and resources to provide a unique set of tools and to enable secure customer authentication, user identity verification and account protection solutions. Their entry into this market has been carefully considered (for example to align with data privacy regulations) and now more operators are offering authentication services and other mobile identity tools and are gaining traction in the market.

Monthly active users of operators MSISDN-based authentication services alone are approaching 1 billion and are estimated to be growing at over 17% a year. Furthermore, several dozen innovative mobile operators are developing their mobile identity services, building on new investment and capabilities in big data, AI, machine learning and APIs to bring more advanced mobile identity products to market.

How Sekura is utilizing this data

The Sekura prediction: 2022 will be seen as the year that mobile identity intelligence data became a truly fundamental enabler for the ongoing growth of the global economy.

Now is the time that the global identity challenges and the increased availability of mobile solutions align to create a market in which mobile data can address real world problems using frictionless, seamless, real time mobile services to simplify customer journeys and prevent bad actors.

The mobile phone provides unique data in that it is the only available source of ‘dynamic’ data about what is happening right now – in ‘real time’. This capability allows mobile data to be used as a source of valuable signals in the fight against fraud, even in the fast-moving digital world that we live in.

Sekura have harnessed this ‘dynamic data’ to flag whether a device has been lost or stolen, signal that a SIM card has been recently swapped, that Call Forwarding has been set to divert calls to another device, or that a device has been recycled and is no longer used by the previous owner. The signals that it can reveal are available immediately so that if any of these actions occur, the data can be checked within an online banking or retail flow and used to help prevent fraudulent transactions or transfers before they happen.

Even better, by using mobile data to keep fraudsters out, it is also possible to easily identify the good guys – you and me – and ensure that, with simple mobile data checks behind the scenes, we are able to access our online services securely, quickly and easily and get on with our lives.

Ten Companies Using Alternative Data for the Greater Good

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Artificial Intelligence, Simplified.

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Artificial Intelligence, Simplified.

How to Level Up Your Risk Decisioning in Under 60 Days

Artificial intelligence in financial services is a $450 billion opportunity – but most AI projects never even get off the ground. Using AI in combination with the right data and the right decisioning tools means you can take a bite out of those billions of dollars of opportunity – and you can get there in less than two months.

Discover why you should implement AI in your risk decisioning, and how to do it.

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Data on Demand: The Power of Real-Time Data for Risk Decisioning

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Data on Demand: The Power of Real-Time Data for Risk Decisioning

How real-time data enhances risk decisioning and wins new customers.

In a digital first world everything is available on demand. Consumers expect instant gratification, not just for small things like movies or music, but for everything, including financial services. If you’re not approving a loan application in real-time, there’s a competitor who will.

In our latest ebook, we explore what the future of data for risk decisioning looks like. And, how real-time, on-demand data will:

  • Enhance decisioning accuracy
  • Power world-class consumer experiences
  • Support innovation across your business strategy

Download the ebook today to discover how curating a single source for historical and real-time data simplifies data access, enhances data science and machine learning strategies, and democratizes data use across your organization.

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Auto Financing – Drive a Better Consumer Experience

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Auto Financing – Drive a Better Consumer Experience

Auto Financing – Drive a Better Consumer Experience

Did you know that all it takes is 90 minutes in a dealership for customer satisfaction to decline? Whether your customers are purchasing cars, trucks, motorcycles, RVs or even boats, the faster and smoother you can make the experience, the better. Get in the fast lane and discover how to accelerate the auto lending experience with automated risk decisioning tools.

Make your auto financing process more like Netflix, and less like Blockbuster. Download our ‘Netflixing Auto Financing’ eBook to learn more.

Ready to accelerate your auto financing strategy?

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Operationalize your Financial Risk Models

DATA SHEET

Operationalize your Financial Risk Models

Quickly and easily import multiple types of risk models

The Provenir decisioning Platform and risk analytics is unique in its ability to quickly and easily import multiple types of risk models so that they can be operationalized in automated decisioning processes.

Because the integration is “model agnostic”, simple and complex models and scorecards developed in third-party tools such as SAS, R and Excel or exported using PMML or MathML can be imported, validated and mapped via easy-to-use wizards.

With Provenir, there is no need for programming, enabling models to be imported and operationalized in minutes. This exclusive capability extends the value of your investment in industry-standard modeling tools while ensuring that automated risk decisioning processes developed in Provenir are always using the most accurate and up-to-date risk models.

Here’s a quick summary that shows just how easy it is to operationalize an R model.

Simply click Add to select a model from your system files and import it into Provenir. Provenir automatically validates the model’s code and extracts the fields for mapping.

R Model Files | Provenir

Open the model to visually configure the input and output mapping by dragging lines between the model’s and Provenir’s data fields. This creates Execution Profiles.

Input mapping specifies the data from Provenir that will be used by the model to execute the analysis.
Output mapping specifies the data to be returned to Provenir once the model has executed.
Provenir also provides options for managing the Execution Profile including how a returned value should be handled as well as using more than one Execution Profile with a model.

Provenir dynamically maps between the R model and its decisioning process. You can also map constant or fixed values between the model and Provenir.

Finally, check in your new model object and you are ready to test it. You can test it directly from the model object itself or place it in a business logic process and test it. Provenir provides visual feedback to show you exactly what happened during the test.

With these few simple steps, your R model has been operationalized in an automated Provenir risk decisioning process.

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eBook: The Future of Buy Now, Pay Later – the Industry Weighs In

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The Future of Buy Now, Pay Later – the Industry Weighs In

Discover what industry experts predict is on the horizon for the rapidly evolving Buy Now, Pay Later market

uy Now, Pay Later (BNPL) is absolutely everywhere these days. Record transaction numbers, new providers, new markets served. What does this rapid growth mean for the future of BNPL? What impact will increasing regulations have on the seemingly endless runway of opportunity? Hear from industry influencers what they believe the future of BNPL has in store – for providers, merchants and consumers.

Learn about:

  • The impact of the Covid-19 pandemic on BNPL trends
  • How providers can create products that support long-term credit health
  • How the right technology solutions can future-proof your BNPL products

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Alternative Data – The Champion of Innovative Credit Scoring

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Alternative Data –
The Champion of Innovative Credit Scoring

How to improve decisioning accuracy and expand your customer base

Traditional credit scores from Credit Reporting Agencies have historically been the only method to determine the risk of lending to an individual. But there are ways to get a much clearer view of a person’s credit risk and make more accurate lending decisions. There are 2.5 quintillion byes of data created globally every day – including various types of alternative data not captured by traditional credit scores.

64% of lenders/credit providers using alternative data see tangible benefits within only a year. Discover why using alternative data is quickly becoming a competitive advantage for lenders.

Want to know how to use alternative data in your credit decisioning?

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Rapid and Simple Data Integration with Provenir Foundation Adapters

DATA SHEET

Rapid and Simple Data Integration
with Provenir Foundation Adapters

The Provenir Risk Decisioning Platform can substantially reduce the time, resources and costs for implementing sophisticated risk decisioning applications. A core strength of the platform is its pre-built and standards-based integration adapters. These “Foundation Adapters” enable IT organizations to communicate and integrate quickly with other internal and external systems as well as multiple structured and unstructured data sources.

Provenir has designed the adapters for ease of use. IT organizations can create integrations in the visual environment provided by the Provenir Platform without any coding or dependency on Provenir. There is no need to start from scratch each time you want to integrate with another data source — connections can be made rapidly using the pre-built integration components. Each adapter provides:

  • Connectivity, security, transaction support and data conversion/parsing
  • Two way communication
  • A visual or graphical data mapper guides the user through the task of establishing the integration and mapping the required input/output data.
  • Visual testing to check the accuracy of the integration. You can test any integration independently, or place it in a business logic process for a more comprehensive test. Provenir provides instant feedback along with a detailed breakdown of the results to show you exactly what happened during the test.

Adapters for Structured Data Sources

Provenir adapters contain the protocol-specific connection specifications for a wide variety of structured data sources1 including:

  • Web Service – Used for XML, HTTP(S), SOAP or Container-less connections
  • JDBC – Used for IBM DB2, JDBC-compliant DB, MSSQL, MySQL, Oracle connections
  • JMS/Message Queue – Used for WebSphere MQ, MSMQ, Apache Active MQ, Rabbit MQ etc.
  • File – Includes (but is not limited to) Delimited, Fixed Length, Binary, PDF, XML, Word and Excel
  • E-mail – Used to send e-mails, faxes and SMS-based messages
  • TCP/IP – Used to connect to legacy systems, such as older credit bureau protocols
  • SMS – Used to send/receive messages across all channels including mobile phones
  • Web-tracking – Used for run-time customer interaction tracking on the web

Unstructured Data Adapters

Social media now offers a potentially powerful new source of valuable data. However, integrating with social media has proven time-consuming and costly. It is not easy to gain access to the data, connect and pull data from social media sources, or make use of the data because it is not stored in a structured format.
Provenir solves this problem with ready-to-use adapters that function as gateways to key social media sites including Twitter and Facebook. Leveraging the connectivity exposed by these sites, Provenir’s social media adapters offer a standardized approach to accessing unstructured data, including a visual configuration interface to create the connection and define what to listen for on a site.

  • The Facebook adapter enables clients to listen for posts on Facebook pages and, when available, use information about the person who posted the message to determine a course of action in response to a post.
  • The Twitter adapter allows clients to:
    • Send tweets
    • Listen for tweets, or specific words in a tweet
    • Reply to a tweet
    • Re-tweet
    • Find followers
    • Determine if specific person is a follower
    • Determine if two accounts are following each other
    • Send a private direct message to a follower
    • Receive timeline and information for a specific user

Provenir Foundation Adaptors

Provenir Foundation Adapters eliminate the need for extensive coding or one-off integrations to multiple structured and unstructured data sources.  

Creating Data Integrations

Creating an integration between Provenir and structured or unstructured data sources is as simple as selecting it from the Add list on the Adapters tab.

Repository Admin Console

Once added, each adapter can be configured to describe the input and output integration between Provenir and the data source.

Configuring a Structured Data Integration

Provenir guides you through each step in the process of configuring an integration, such as this example of using Provenir’s Web Service Adapter to connect to Experian NetConnect, which pulls Experian business and consumer reports over HTTPS.

input and output data values

Easy-to-follow wizards are used to configure both the input and output data values.

mapping in Provenir

You map the Experian element to Provenir, defining how to store the Experian response data in your schema.

Provenir also allows you to use individual fields from the Experian response data in your business logic. When you choose this option, Provenir automatically adds the full xml to your data schema.

Provenir combines the schema and mapping logic that you have defined with the adapter to automatically create the integration.

Configuring an Unstructured Data Integration

Provenir approaches integration with an unstructured data source in the same way as for a structured source. Configuration starts with establishing the physical connection between Provenir and the source, such as Twitter.

twitter

Provenir then guides you through the process of setting up each aspect of the Twitter profile, such as the Listen function where you can track for specific words in tweets or specific users.

output map

You map the fields to be used in the desired business process that are available in received tweets, such as text fields which contain a tweet’s text, user/id that contains a number ID of the person who sent the tweet and user/screen_name which is the Twitter user name of the individual who sent the tweet.

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Data Integration in Minutes

DEMO

Data Integration
in Minutes

Book a Meeting

The Simple Solution to Integrating Structured and Unstructured Data Sources.

In this demo Mike LaFleur, Provenir’s Global Head of Solution Architecture, will show you how the Provenir Risk Analytics and Decisioning Platform puts the power of the process in your risk team’s hands. See how Provenir empowers your business to rapidly create integrations with both internal and external systems as well as multiple structured and unstructured data sources.

You’ll see:

  • How Provenir’s pre-built adapters make integration quick and easy
  • How your business can save time and money with Provenir
  • The Provenir Platform in action


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    Guest Blog: Future-proofing Your Business with Alternative Data Analytics

    GUEST BLOG

    Future-proofing Your Business
    with Alternative Data Analytics

    • Ralitsa Boyadzhieva, Marketing Manager at FinScore

    Despite the gradual recovery of society from the COVID-19 pandemic thanks to vaccinations, the digital means to make lending transactions are here to stay.  It is safe, efficient, and convenient for both your customers and your lending business. The pandemic has acted as a catalyst for lenders to rapidly adopt digital technologies and stay competitive in this changing environment. Upgrading digital infrastructure introduces fundamental changes that can create more economic opportunities.

    However, more digital technologies not only reel in more opportunities but also disadvantages. When it comes to future-proofing your financial services company, aim to grow by expanding your market reach while ensuring profitability by detecting fraudulent individuals who are more likely to default on their loans.

    To achieve both, you can rely on alternative data credit scoring.

    What is Alternative Credit Scoring?

    Alternative data credit scoring is the umbrella term for credit scoring using non-traditional data sources and methods.

    In traditional credit scoring, lenders manage their risk and reduction of bad loans by relying on credit bureaus that provide credit information on the consumers. They can learn if the loan applicant has been paying off loans on time, has ongoing loans, or has been blacklisted for not paying at all. But this only works for those who have a loan history. Besides this, they assess the socio-demographic profile of the borrower which can be found on the loan application form either in pen-and-paper or digital form. The lender may also use a credit scorecard, a type of credit model, that outputs a score predicting the probability that the borrower can repay the loan on time. With Alternative Credit Scoring, alternative data sources such as mobile usage, digital wallet usage, bill payment history, social media behavior, geolocation, and more can be aggregated to produce a credit score.

    Boosting Your Market Reach by Serving the Unbanked Using a Telco Data Credit Score

    The mobile phone has become a physical extension of a 21st-century human. Whatever model, size, or function it may have, each mobile phone owner has a SIM card so he or she can connect with the rest of the world. They use it to make phone calls, send messages, access a wide array of mobile applications — from social media to digital banking. As time passes, a user creates his or her mobile footprint such as, but not limited to, texting usage, data usage, voice usage, top-up patterns, SIM age, and more. These are telco data that is unique to each user. 

    Alternative data credit scoring, specifically telco data, can help verify, analyze, and assess the creditworthiness of the credit-invisible applicant. The credit-invisible market, also called the unbanked market, is a portion of the population who does not have any financial history they can leverage if they need to apply for a loan.

    In the Philippines, in which our company is headquartered, 70% of the population remains unbanked. Telco data credit scoring can be the solution for banks and financial institutions in the Philippines to finally make the invisible market, visible. Not only visible but also reachable and possibly viable for future financially inclusive loan products.

    Preventing Fraudulent Accounts by Using Social Media Data Analytics

    Now that you have the confidence to serve larger volumes of customers, it is also important to add a fast and powerful way to extend credit to real customers. Fraudulent activities do not sleep. In fact, synthetic identities, bot attacks, and fraud attacks are more present in digital channels.

    This is where Social Media Presence Data, which is another type of alternative data analytics, comes in.

    Social media presence data is derived from public databases, which are near-real-time data. You can confirm an applicant’s identity with an e-mail address and/or mobile number. FindSocial, a fraud prevention tool, for example, can check more than 20 social media platforms, showing a user avatar and profile information in a simple interface. FindSocial also produces a Social Presence Score that can help you determine whether the customer’s social presence can be trusted or not.

    The Future of Lending

    Banks and financial institutions of any size are increasingly having positive perceptions towards Alternative Data Credit Scoring because not only can it increase their profitability but also reduce costs due to its speed and efficiency.

    The COVID-19 pandemic has further pushed such businesses to transform the way they assess borrowers’ creditworthiness. Besides making their digital banking and/or lending mobile application available to the masses, the lending process, especially loan underwriting and disbursement needs to be fast, easy, and secure to keep up with the demand of the market and to stay ahead of their competitors.

    Alternative data scoring provides borrowers with better, easier access to credit. If they apply for a loan from a lender that uses this particular type of credit scoring method, there is a higher probability they will qualify for credit applying for a loan with an establishment relying on traditional methods alone.

    Summary

    • Going digital can help financial institutions reach larger markets to serve. However, it can also attract fraudulent individuals.
    • By leveraging alternative data in your analytics efforts, you can reach the large group of unbanked and underbanked individuals and prevent fake accounts from exploiting digital loopholes.
    • Telco data is a type of alternative data that can determine the financial power and creditworthiness of a borrower using data variables such as SMS and call usage, top-up amounts and patterns, SIM age, and more.
    • Social media presence data is a type of alternative data that enables fraud prevention and faster identity reviews.
    • By using an advanced analytics platform, you can instantly confirm if an applicant’s declared e-mail address and mobile number are registered on more than 20 social media platforms.

    Learn more on the Marketplace.

    Ten Companies Using Alternative Data for the Greater Good

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