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The Evolution of a Digital Identity How organizations can prevent fraud and build trust through holistic identity verification

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Some say that the only constant in life is change, and the same goes for our digital identity. Between opening a first bank

account before moving to college, to applying for credit or a loan, these activities are all stops in our financial journey.

Couple that with the physical addresses and emails collected over decades, a Social Security number (SSN), and date of

birth, and you paint a complex picture of an identity that has grown and evolved — like us.

Many attributes tied to an identity are easily findable online — addresses can be looked up, SSNs can be bought and sold

on the dark web. But what about the more elusive aspects of identity, such as someone's preferred device brand, mobile

carrier, phone language settings, or typing patterns? These are details that tend to remain consistent over time.

The only way for an organization to truly know who they are dealing with is to see and assess all parts of an identity, from

PII, to digital behavior, to geolocation, and more. Every identity element relates to each other in some way, and with an

incomplete picture, you have a disjointed view of risk.

Fighting fraud is hard enough. Doing so with disparate information produces patchy results — at best.

Many organizations are attempting to solve for fraud with a patchwork of single-point solutions that don’t integrate

with each other, resulting in wildly high false positive rates. The more vendors you have, the higher your third-party false

classification risk, and the more missed opportunities to approve good consumers.

Like our digital identities, fraud patterns constantly change. Responsible organizations use solutions with built-in

adaptability, continuously identifying new patterns in data, offering extensive rules to make real-time changes, and

leveraging AI throughout.

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A digital identity’s journey through time

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The focus of our story today

is Mia, a young woman

immigrating to the United

States. We’ll follow her journey

as she traverses the financial

ecosystem, and explore what

organizations can do to ensure

all good consumers gain access

to financial services, without

unnecessary friction.

Let's begin

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First stop: Settling in and gaining financial system access The potential impact of immigration on U.S. economic growth is astounding. According to research conducted by FWD.us, high rates of legal immigration between now and 2050 will result in approximately $50 trillion in GDP.

This important segment of the population is clearly valuable from an economic standpoint, but also presents identity verification challenges.

New-to-country populations, such as Mia, typically do not have a record in the credit reporting ecosystem, so identifying these individuals requires legacy vendors to use various step-up methods and manual reviews to check additional data sources. The result is that these consumers often experience substantially higher friction in the application and onboarding processes, reduced opportunities in credit availability, and are often unfairly denied access to the consumer services available to those with established U.S. credit histories.

So how can banks and eCommerce companies validate these users — without introducing fraud into their systems?

New-to-country individuals — which currently accounts for almost 14% of the population — bring with them significant buying power as participants in the U.S. economy.

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Traditional identity verification solution view From an identity verification standpoint, Mia has no U.S. consumer history and is not yet showing up in other sources, such as the Department of Motor Vehicles. This will leave her looking high-risk as she goes through the application processes.

Because the institutions are using a range of point solutions, their fraud departments may flag that her:

• Email address has not been seen before

• Phone number is less than 180 days old

• Address cannot be resolved to her name

• Name and date of birth cannot be resolved together

With a thin credit file, Mia will likely face friction such as manual reviews, denied applications, and other obstacles to accessing the services she needs. This isn’t exactly a warm welcome to a future participant in the U.S. economy.

Coming to America

In order to start her new life in the U.S., Mia will sign up for a Demand Deposit Account (DDA), apply for a credit card, and use a Buy Now, Pay Later plan to purchase a phone from a major carrier. Let's examine what these three experiences could look like for Mia.

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Why it works Socure's identity graph uses advanced machine learning to quickly analyze Mia's application and verify her identity by checking it against our consortium network.

With a wide range of data sources that extend across geographies, industries, and companies of all sizes, Socure can verify Mia's identity with high accuracy — regardless of her age, race, or cultural background. This inclusive approach is good for both the consumer and the bank — together they can now build a positive relationship.

Socure’s view When Mia applies to an institution that is using Socure Sigma Fraud — a holistic solution for identity verification and fraud prevention — her application is approved, despite her thin credit file.

By combining intelligence around all pieces of Mia’s identity, Socure can confirm that:

• Mia’s email handle contains her first and last name

• Her address is residential and considered deliverable by the U.S. Postal Service

• Her IP’s geolocation matches her address

• She is using a major mobile carrier

On her first interaction, Socure brings together all of these pieces of identity for a holistic view. As Mia applies for financial services across Socure’s network of customers, she establishes herself as a “good” identity across Socure’s consortium graph. This adds a unique layer of confidence that Mia is who she says she is and has no history of malicious activity, allowing her to earn access to build financial independence in her new home country.

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Traditional identity verification solution view

Even with a consistent digital footprint, traditional verification solutions may still fail Mia’s application

when her address changes — even though her behavior remains the same.

Here’s what they see:

• Her address does not match the address on file in credit headers

• Her new address is lowly correlated with her name, email and phone number based on previous observations

• They lack visibility into Mia’s digital risk indicators — such as device use — to confirm or deny her identity

Building a life and consistent behavior

Over the course of a few years, Mia has been establishing consumer habits: she uses Google Pixel phones — always set in the Turkish language — through a premium mobile provider in the U.S.

Mia makes a move to a new state for her job — taking with her the Google Pixel phone set to Turkish through a premium mobile carrier. Once she’s settled, she attempts to apply for a loan for a new car.

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Why it works

Socure builds a comprehensive understanding of digital identities like Mia's by analyzing behavior patterns over time. Our Entity Profiler creates a unique digital footprint for each identity by connecting data points such as device types, carriers, or locations. This contextual understanding of identity consistency allows Socure to accurately assess the risk of new applications and transactions. By leveraging predictive analytics on digital behavior trends, we can distinguish genuine users like Mia from fraudsters using her stolen identity.

In Mia’s case — even if she moves — Socure is still able confirm her identity as long as her core behaviors remain consistent. However, if Mia’s PII is seen again but she suddenly starts using an Apple iPhone in Spanish, Socure would flag this change in behavior and raise her risk score because this is likely a fraudster acting like Mia. Other solutions that focus on just the identity information would be unable to detect both of these patterns.

Socure’s view Socure's identity verification leverages digital intelligence to provide a more complete view of identity — including what’s normal for a user, and what’s not.

Here’s what the team sees:

• Mia’s device matches her history

• Her IP and phone number are still associated with the same major carrier

• Her email and phone can be resolved to her identity

• Socure’s consortium network reports two associated accounts that match her core identity

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Caught up in a large-scale generative AI attack

Unfortunately, Mia’s PII is exposed as part of a large data breach at an online retailer. Her identity is used in two separate fraud attacks:

• The first attack includes 500 new account applications with different identities — including Mia’s — that all use phone numbers from a non-premium mobile carrier with historically high fraud rates.

• In the second fraud attack, the fraudsters tumble an email address for a person named Jane Smith that adds Mia and other victims’ names as a tag, such as:

Fraudsters use this technique of adding tags and dots to create

distinct email addresses — that all actually tie to the same inbox —

for different applications.

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janesmith50+jamespotter@gmail.com

jan.e.smit.h.5.0@gmail.com

janesmith50+miajames@gmail.com

janesmith50@gmail.com

Traditional identity verification solution view

Without a large consortium of partners and intelligent, continuous pattern

detection, other solutions may miss large-scale fraud attacks like this

one because they struggle to connect the dots. They may see the four

applications with the email addresses above and not catch that those

emails all tie to the same fraudster.

Here’s what they see:

• 500 new applications that include a phone number from a non- premium mobile carrier

• No sign of email tumbling because they look at each application in

isolation, missing patterns that show across groups of applications

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Why it works

To detect these two separate fraud attacks, the Sigma Fraud suite works in a few different ways.

Socure has built a consortium network of customers who share application data and fraud reports,

allowing us to identify emerging threats across the network. Combined with Large Language Model

(LLM) techniques, Socure can detect unusual patterns in personal information like emails, phones, and

addresses that are highly linked to fraud. These risk indicators look like gibberish PII, email tumbling,

or high-risk phone carriers that are linked to past fraud attacks.

Our anomaly detection kicks in when indicators that are not risky on their own, such as a medium-sized

phone carrier or ISP, suddenly see anomalous patterns. For example, if an institution typically sees 10,000

applications in a day from a specific ISP, but then this number rises to 100,000 — it’s likely correlated to

a fraud attack.

Similarly, emails with tags or dots are not always good or bad. But a series of applications with different

tumbled versions of the same email? Almost always bad. Socure continuously tracks these patterns and

elevates or lowers risk scores as patterns emerge and fade away.

Socure’s view

Here’s what the team sees:

• The four application email addresses all actually originate from the same email address

• A high-risk phone carrier is observed

• An ISP that is likely part of an emerging fraud attack

• A gibberish email is provided

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Many vendors in the fraud prevention industry solve for only a few fraud types, such as identity document

fraud, payment fraud, or synthetic fraud, missing the holistic view of a consumer and the risks that extend

beyond the narrow single-point solution perspective.

Few vendors can accommodate identity verification use cases that span industries, geographies, and

unique demographic segments, which results in organizations connecting to multiple API integrations

across many vendors, wasting time and resources contracting and implementing incompatible solutions.

A unified approach combines broad data assets with predictive analytics and collaborative intelligence.

This powers low-friction onboarding to safely approve more consumers — regardless of their

demographic — enabling frictionless access to financial services.

Many vendors in the fraud prevention industry solve for only a few fraud types, such as identity document fraud, payment fraud, or synthetic fraud.

Why point solutions don't work — for organizations or consumers

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The industry’s first completely integrated identity fraud solution — with predictive signals across PII,

digital, and behavioral risk dimensions fused into a singular identity view — is here.

The Sigma Fraud suite offers the most complete defense against all forms of identity fraud through

a single, integrated API, replacing complex rules and inconsistent point solutions.

One solution. One provider. Identity fraud: solved

Sigma Fraud offers 6x more accuracy than credit bureaus.

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Sigma Identity Fraud

Analyze every dimension of consumer identity, including email, phone, address, date of birth, SSN, device, and behavioral intelligence through

a single machine learning model so organizations can approve more good customers while minimizing fraud. Sigma Identity can capture

>35% more third-party identity fraud at the same applicant review rate with almost 4-6x more accuracy, compared to credit bureau and other

leading providers.

Sigma Synthetic Fraud

Use diverse, high-quality third-party and historical data to uncover complex patterns and connect identity elements associated with synthetic

identities to capture fraud at the door. Sigma Synthetic achieves a 90% fraud capture rate for the top 5% high-risk consumers through "proof of life"

features, alternative data sources, and email tumbling detection.

The Digital Intelligence Suite: Device Intelligence, Behavioral Analytics, and Entity Profiler

Create a digital footprint of users made up of their device, behavior, network, and location patterns to understand what is typical for an individual.

Email, Phone, and Address RiskScores

Prevent account takeovers by assessing changes in account profiles, enabling progressive onboarding when limited identity attributes

are collected over time. This verifies 28% more phones with 40% greater fraud capture compared to other solutions.

By combining digital intelligence and behavioral signals, the Sigma Fraud suite creates a multilayered defense against sophisticated identity attacks.

Together, these solutions empower all populations, including those in harder-to-verify groups like Mia, to easily access financial services and form a loyal relationship with a financial institution.

This looks like:

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As we’ve seen through Mia's journey, traditional identity verification solutions often fall short in

providing an accurate and holistic view of an individual's identity. Point solutions that rely on limited

data sets frequently misclassify new-to-country immigrants, those with thin credit files, and consumers

whose circumstances naturally change over time. This results in denied applications, frustrating extra

steps, and lost business opportunities.

In contrast, AI-powered solutions like Socure use advanced machine learning to analyze the full

spectrum of identity attributes and behaviors. Combined with powerful insights from feedback and

consortium data from our customers and digital signals from devices, locations, and usage patterns,

Socure can detect fraud patterns that other providers miss. This allows organizations to verify identities

with a high degree of accuracy, regardless of how life changes.

With a complete understanding of identity risk, companies can confidently approve more consumers

while stopping complex fraud attacks. A streamlined, collaborative approach to identity powers

accessible financial services and better experiences for all good customers.

In the new era of compliance, Socure provides the accuracy, agility, and scalability to confidently verify customers, stop fraud, and transform compliance into a competitive advantage.

Unlocking revenue while mitigating risk

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About Socure Socure is the leading provider of digital identity verification and fraud solutions. Its AI and predictive analytics platform applies artificial intelligence and machine learning techniques with trusted online and offline data intelligence to verify identities in real-time. Socure is the only vertically integrated identity verification and fraud prevention platform with both IAL-2 and FedRAMP Moderate certifications, delivering advanced levels of assurance and the highest standards for security and compliance.The company has more than 2,000 customers across the financial services, government, gaming, healthcare, telecom, and e-commerce industries, including four of the five top banks, the top credit bureau and more than 400 fintechs. Organizations including Chime, SoFi, Robinhood, Gusto, Public, Poshmark, Stash, DraftKings, and the State of California trust Socure for accurate and inclusive identity verification and fraud prevention. Learn more at socure.com

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