This article is sponsored by Lexis Nexis.

Across the world, growing numbers of young people, new-to-country immigrants, and other groups are poised to enter the financial system as customers of credit, loans, remittances, and more. By 2030, 75% of consumers in emerging markets will be between the ages of 15 and 34.

Companies that can safely onboard and serve this population of emerging identities can unlock significant growth potential and improve financial inclusion. But for the banking and payments systems of the world, emerging identities often complicate traditional approaches to recognizing trusted customers.

• Younger demographics haven’t had as much time to build up a record of working, borrowing and purchasing.
• New-to-country immigrants might not have acquired financial products or proof of residence outside of their birth countries.
• Older consumers that live communally and don’t have a driver’s license may seem risky.

Identity verification needs to keep pace with all of these changes, and more.

Emerging Identities Provide Superb Camouflage For Synthetic IDs

From the business world’s perspective, emerging identities can seem to appear out of nowhere, often with robust digital profiles but fewer physical identifiers. Unfortunately, these profiles also strongly resemble third-party synthetic identities, cobbled together by fraudsters from real, modified, and fake bits of identity information.

Since first materializing in the US more than 10 years ago, synthetic identities have spread to other major financial economies. Recent analysis found three million high-risk synthetic identities in circulation in the UK alone, with the volume increasing at a rate of over 500% between 2020 and 2023.

With global losses from synthetic identities estimated at up to $40 billion, businesses must be cautious of this rising threat as they attempt to find ways to onboard emerging identities.

It’s bad business to reject low-risk emerging identities. Even flagging them for manual review increases operational costs and degrades the applicant’s onboarding experience, starting the new relationship with an unproductive atmosphere of mistrust.

How Synthetic Identities Cloud The Search For Emerging Identities

There are two types of synthetic identities, broadly speaking. First-party synthetics are alternate identities that consumers create for themselves, for a specific purpose—and not always with malicious intent. However, these identities often collide with the real identity they are augmenting, and do not pass validity checks.

Third-party synthetics are more malicious in nature. These are sometimes referred to as “Frankenstein” identities because a third party cobbles together pieces of identities from legitimate and fictitious sources into one imaginary digital identity they can leverage for cybercrime. These are managed via disposable email addresses and phone numbers, to help maintain anonymity.

Credit bureaus have become an unexpected, but reliable ‘source’ of synthetic identities. It’s hard for criminals to fabricate an identity through credentialed sources like voter registration, a property deed, or a professional certification. On the other hand, it’s relatively easy to submit multiple credit applications to stimulate the creation of a credit profile.

How To Tell Synthetic Identities From Emerging Identities

Though synthetic identities can appear very similar to emerging identities, smart analysis backed by robust intelligence can reveal telltale patterns of synthetic fraud. For example, synthetic identities are 7x more likely than emerging identities to have no first-degree relatives, 20x more likely to appear in multiple credit applications in a short time period and 7x more likely to first show up at a credit bureau at an unusually late age.

Businesses Are Finding New Ways To Safely Onboard Emerging Identities

Competing more effectively in the emerging consumer market starts with an accelerated customer acquisition process that speeds approvals for legitimate customers while mitigating fraud threats. Balancing faster approvals with increased confidence demands identity verification that accurately assesses applicant identity and behavior patterns in real time.

Because emerging identities appear without historic data, businesses need more diverse sources of context around risk.

• Seek out alternative data sources. For example, education sources can help to verify younger demographics.
• Clarify a bigger picture. Robust collaborative intelligence networks help to set an identity’s desired action in the broader context of their past and real-time interactions with other organizations, in different industries and even across borders.
• Authenticate documents with liveness checks. More advanced solutions can verify and authenticate valid documentation without much disruption to the user experience.
• Layer insights for a more comprehensive view of identity. How is the user behaving? Are they mobile? Are they submitting many applications in a short period of time? Does the email, device or location carry risk signals? The sum of these insights clarifies risk more than any one contributing factor.

Both customers and businesses win when emerging identities can be verified reliably and distinguished from synthetic identities. More legitimate consumers access the financial services they want. Businesses acquire more customers safely while reducing costs and better focusing manual fraud risk assessments.

In the financial services sector, artificial intelligence (AI) is often heralded as a transformative force capable of revolutionizing everything from customer engagement to fraud detection. However, as the excitement around AI continues to grow, so do the challenges associated with its implementation. According to the latest McKinsey Global Survey on AI, AI adoption is accelerating, with 72% of organizations using AI in at least one business function in 2024, up from 50% in previous years. However, the challenges of achieving tangible business value remain substantial. The survey highlights that organizations need to focus on aligning AI projects with strategic business goals to achieve success (McKinsey, “The State of AI in Early 2024”).

The journey to successful AI implementation in financial services is not about jumping on the latest technology bandwagon; it is about identifying core business challenges, choosing the right AI strategy, and following a robust engagement methodology. Here’s how financial institutions can move beyond the AI hype and achieve real, measurable business value.

1. Start with the business challenge, not the technology

The key to successful AI deployment begins with a comprehensive understanding of the specific business problems that need to be addressed. Too often, organizations are drawn to AI’s potential without a clear roadmap for its application, leading to projects that flounder in development or fail to deliver a return on investment (ROI). McKinsey notes that “the business goal must be paramount,” emphasizing the importance of identifying the most promising business opportunities and working backward to potential AI applications rather than pursuing tech for tech’s sake (McKinsey, “The State of AI in Early 2024”).

For financial institutions, this means asking critical questions: What are the pain points that, if resolved, would yield the most significant benefits? Whether it’s enhancing customer engagement, improving fraud detection, or optimizing operational efficiency, defining the challenge upfront ensures that AI initiatives are grounded in strategic business needs rather than technological fascination.

2. Evaluate: build, buy, or partner

Once the business challenge is identified, the next step is to determine the most effective strategy for deploying AI. This involves a critical decision: whether to build a custom solution, buy an existing one, or partner with an AI expert.

• Build: Custom solutions offer the highest degree of specificity and alignment with unique business processes, but they require significant time, resources, and in-house expertise. For institutions with complex, industry-specific needs, building an AI solution may be the most effective approach, but it also carries the highest risk.
• Buy: Off-the-shelf solutions provide a faster route to deployment and can be cost-effective for common challenges. However, they may not offer the flexibility needed to adapt to specific business environments. McKinsey’s latest research shows that while 50% of organizations are using off-the-shelf generative AI models, the high performers are increasingly moving toward significant customization or developing proprietary models to meet specific needs (McKinsey, “The State of AI in Early 2024″).
• Partner: Partnering with a specialized AI consultancy, like Intelygenz, allows organizations to leverage deep technical expertise and experience while focusing on rapid implementation. A trusted partner can guide institutions through the complexities of AI deployment, ensuring that the solution is tailored to deliver the maximum business impact. This approach combines the benefits of both build and buy strategies, mitigating risks and accelerating time to value.

3. Implement with a proven engagement methodology

The pathway from AI concept to value realization is rarely linear. To navigate this complexity, financial institutions need a structured, end-to-end engagement methodology that enables rapid development and deployment while ensuring alignment with strategic objectives. Accenture’s “Tech Vision 2024” report emphasizes that adopting an agile, iterative approach to AI deployment enables organizations to see faster returns on investment and adjust quickly to evolving business needs (Accenture, “Tech Vision 2024″).

Intelygenz’s “Day Zero Promise” embodies this approach. Our methodology begins with a rigorous scoping session to align AI projects with strategic business outcomes from the very beginning. This is followed by:

• Agile Development: An iterative approach that allows for continuous refinement and adaptation of AI solutions to evolving business needs.
• Seamless Integration: Close collaboration with internal IT and business teams ensures that AI solutions integrate smoothly with existing systems and workflows.
• Accelerated Deployment: Fast-tracking the time to value by deploying AI solutions in a matter of weeks, not months or years.

By maintaining a relentless focus on delivering measurable ROI, Intelygenz helps financial institutions avoid the common pitfalls of AI implementation and ensures that AI initiatives contribute directly to business growth.

4. Focus on flexibility and cost-efficiency

For many financial institutions, one of the barriers to AI adoption is the perceived cost and complexity. However, AI does not have to be prohibitively expensive or rigid. Intelygenz positions itself as a more flexible and cost-efficient alternative to top-tier AI companies. We deliver high-quality AI solutions without the overhead and rigidity often associated with larger providers, making us an ideal partner for organizations looking to innovate while managing costs.

5. A collaborative approach to AI success

AI projects are not just technical endeavors; they are fundamentally business transformations. A collaborative approach between the AI partner and the organization is crucial for success. At Intelygenz, we engage closely with our clients throughout the entire process, ensuring that every AI solution is not only technically robust but also aligned with the organization’s strategic goals. This partnership approach has led to real-world success stories where financial institutions have transformed AI from a buzzword into a business-critical capability.

Learn More at FinovateFall

For financial services leaders looking to leverage AI effectively, the path to success involves a thoughtful strategy that prioritizes business value over technology for technology’s sake. At FinovateFall, Chris Brown, President of Intelygenz USA, will delve deeper into these themes during his keynote session, ‘Beyond the Hype: Delivering Real Business Value with AI in Financial Services’. Attendees will learn how to identify the right business challenges, evaluate strategic options for AI deployment, and implement solutions that drive tangible ROI.

Join us on day two of FinovateFall to gain actionable insights and see how Intelygenz’s expert consultancy and implementation services can help your institution harness the true potential of AI.