Is it surprising to read that there are approximately 2,200 cyberattacks per day globally? While the number of successful breaches is not increasing, some reports have indicated the cost per breach is rising significantly for business and consumers alike.
Advanced identity verification (IDV) measures are one of the primary tools to address this surge in losses due to fraud and cybercrime. However, as part of the verification of end users, companies now handle increased amounts of sensitive personal information. As a result, they face intensifying pressure to protect customer data while maintaining rigorous security standards.
Traditional fraud detection methods present a significant challenge to this quandary: they rely heavily on collecting and storing personal data as well as training their systems on this data, an explosive mix that triggers privacy concerns among users and regulators.
Privacy-first artificial intelligence (AI) offers a precise solution to this tension, creating a framework where security, privacy, and ethics coexist—without compromise.
Security benefits of privacy-first AI
Privacy-first generative AI introduces sophisticated fraud detection patterns that operate independently of personal user data. This technology reduces exposure risk through innovative data management approaches, eliminating common vectors for breaches and misuse.
The impact extends beyond technical security improvements (which are vital in their own respect). Users have increasing awareness of data handling practices through—for example, high-profile regulatory actions against the Googles and Facebooks of the world. So it is incumbent on companies to implement privacy-first solutions to gain measurable advantages in customer trust and retention.
Clear communication about privacy-focused fraud prevention creates meaningful differentiation in the verification technology market.
Advanced detection without personal data
The technology operates through key mechanisms such as:
- Synthetic data generation based on observed patterns: enables AI model training without accessing actual personal information.
- Federated learning across decentralized sources: distributes the process across local devices, eliminating centralized data storage.
- Behavioral and feature anomaly detection without personal identifiers: focuses on general behavioral and input feature patterns rather than individual data points.
These methods, among others, deliver high-accuracy fraud detection while maintaining strict privacy standards.
Training for success
The approach to the training of an AI system is one area in particular for a company to gain outlier status. While many identity verification providers train their engines using end user personal data—some even without explicit user consent—privacy-first AI relies on synthetic data training.
In relation to identity verification, a machine learning technique known as generative adversarial networks (GANs) can be used to create vast data sets, including images, videos, and text. GANs can also be used for training and testing identity verification algorithms, which strengthen the product against deepfakes, for accuracy and performance, and to achieve Zero Bias™, among other benefits.
This approach helps achieve maximum anti-fraud and UX effect with minimum PII exposure.
Compliance as strategic advantage
Global regulatory requirements including GDPR, CPRA, and other data protection standards are common in their approach of applying voluminous checklists for compliance, followed by a long chapter that empowers an overseeing agency with enforcement rights.
With personal data management embedded into the foundation of a tech stack, companies gain significant advantages through:
- Reduced risk of regulatory violations
- Simplified compliance management
- Enhanced positioning as privacy advocates
- Limited legal exposure
- Clear ethical standing in AI deployment
These fundamental differences establish a clear ethical advantage while reducing regulatory complexity.
Foundations remain foundations, with a refresh
One key area of evolution for the traditional regulations such as GDPR and PSD2 will be the concept of informed consent. As AI algorithms become increasingly sophisticated in their ability to analyze and utilize personal data, the traditional notion of explicit consent will have to be revisited.
Regulations will require organizations to provide transparent and understandable explanations of how AI systems process personal data, even if the decisions made by these systems are not fully comprehensible to human operators. This will lead to new compliance requirements for data controllers to document and justify the logic and decision-making processes of AI algorithms, particularly those that have a significant impact on individuals.
Reducing bias through regulation
Another important consideration is the potential for algorithmic bias. AI systems are trained on vast amounts of data, and if this data contains biases, the AI’s decision-making may perpetuate or even amplify those biases.
Regulations will impose stricter requirements for organizations to assess and mitigate the potential for algorithmic bias in AI-powered payment systems. This could involve regular audits of AI systems, rigorous testing procedures, and ongoing monitoring of their performance to identify and address any discriminatory patterns.
As a side note, the informed consent and bias transparency changes enumerated above dovetail neatly into the proposal for synthetic generated data, which serve as strong preventative and mitigatory tools of compliance.
Safeguarding data for payments
Getting back to expected regulatory changes, we cannot ignore that the increasing use of AI in payments will carry continued concerns about the security and privacy of personal data. The risk of data breaches and unauthorized access will not disappear, or lessen.
We expect that regulations will impose even stricter cybersecurity standards for organizations using AI in payments, including robust data protection measures, regular security assessments, and incident response plans. This may include some combination of trusted best practices, such as increased encryption standards and data anonymization.
Trust through technology
Privacy-first AI that includes synthetic data training represents a decisive shift in identity verification, protecting users from fraud while eliminating risks associated with data misuse and improper consent management.
In an environment where privacy standards continue to evolve, privacy-first AI positions organizations ahead of compliance requirements. The goal of an IDV solution vendor—in theory and by practical implementation—should align perfectly with that of its clients, namely, to keep the fraudsters outside the fortress gates, and to serve those within it securely and efficiently, with no frivolity in the management of end users’ most trusted of assets: their identity.
About the post:
Images and videos are generative AI-created. Prompt: An elaborate medieval fortress with a moat on top of a high mountain, dragons soar around looking for a way to enter, fantastical, whimsical vibes. Tools: Midjourney, Luma.
About the author:
Terry Brenner is the Head of Legal, Risk, and Compliance for IDVerse Americas. He oversees the company’s foray into this market, heeding to the sensitivities around data protection, inclusivity, biometrics, and privacy. With over two decades of legal experience, Brenner has served in a variety of roles across a diverse range of sectors.
