By Barley Laing, the UK Managing Director at Melissa
Most electronic ID verification (eIDV) platforms share a critical weakness of authenticating documents without verifying the person behind them. This is an issue for those in financial services because it is precisely this discrepancy that is exploited by sophisticated types of fraud.
Proliferation of synthetic identity fraud
According to LexisNexis’ Cybercrime Report 2026 synthetic identity fraud is the fastest growing type of fraud globally, with more than one in ten (11%) of frauds involving synthetic identity, representing an eight-fold increase year on year.
With this type of fraud bad actors blend valid identity information, such as a stolen national insurance or social security number, with fabricated details designed to get past less powerful eIDV systems that stop short of connecting data to specific parties.
Value of address-anchored identity verification
It’s real time address verification that closes the gap and proves the connection between a verified identity and a validated physical address.
Many eIDV platforms don’t have this functionality because address verification requires specific data quality tools they just don’t offer. Confirming the legitimacy of an identity document is simply not the same as confirming that the individual presenting it is genuinely associated with the name, address, and related data on record.
To achieve reliable address-to-identity matching requires sophisticated parsing, standardisation and matching algorithms capable of reconciling over 200 global postal formats. Systems must meet an extensive slate of accuracy standards to correctly resolve name variations across cultural conventions, transliterations and common nicknames.
It’s vital to connect identities with verified identity information by undertaking name-to-address matching which gives financial institutions a stronger layer of proof, based on real time data quality, which can’t be achieved by document checks alone.
Solutions can be supported by artificial intelligence (AI) and machine learning (ML) based data modelling and advanced matching algorithms aligned with an institution’s specific priorities.
By powering a data quality advantage it’s possible to strengthen identity verification frameworks and support fraud detection efforts.
As fraud keeps evolving the added value of data quality in eIDV is critical. Usually with identity theft fraudsters gain access to someone’s full identity and credit. The victim typically notices transactions they didn’t make very quickly, with steps speedily taken to stop the fraudsters. With synthetic identity fraud criminals construct a non-existent identity from a blend of real and fake details – something that’s getting easier to do in the digital age. They create a fictional customer who can establish accounts, build up a credit profile and make purchases, or take out loans that will never be paid. Because no single person’s identity is fully stolen, these schemes can remain active for long periods while financial institutions face losses that are nearly impossible to recover.
Data quality provides trust
Data quality tools, such as address verification, add an additional level of trust, allowing financial institutions to enhance their fraud prevention strategies without disrupting operations. For example, access to 2+2 verification which entails checking identity information via at least two or more independent sources in real time. This involves matching identity details against trusted sources, such as electoral rolls, credit or utility records, for enhanced accuracy.
Obtain IP addresses
In addition to the focus on address-anchored identity verification, obtaining the physical location of the customer’s IP address in real time and cross-referencing it with their home location adds further value. After all, geographic inconsistencies may warrant additional verification, although legitimate explanations may also exist.
Liveness checks and sanctions screening
Additionally, using liveness tools, such as advanced eye tracking technology, determines whether the user is physically present during the application process and not represented by a static image, deepfake, or replayed video. It’s equally important to undertake sanctions screening to support compliance efforts, where applicant data is screened and identified against global sanctions lists and blocked if the individual or entity is flagged.
In summary
Criminals are adapting and using AI-powered technologies to their advantage. It requires robust identity verification procedures by those in financial services, which means implementing solutions that enhance security, mitigate risk and streamline the customer experience. As fraud tactics continue to evolve, it’s important that financial institutions reassess how identity verification, data quality, and risk controls can work together to strengthen onboarding and fraud prevention processes.
