The modern world of digital finance creates unprecedented opportunities, but also brings with it new, sophisticated threats. On the one hand, the development of technology allows for the offering of new services and products, and on the other, it opens a Pandora’s box of numerous, previously unseen threats. One of the most serious and at the same time most insidious is the problem of synthetic identities. They cause the entire banking sector to face challenges that require innovative solutions and a deep understanding of the evolving nature of digital fraud. What can be done to avoid falling behind cybercriminals?
Introduction to the Problem of Synthetic Identity
Synthetic Identity Definition
Synthetic identity is not the equivalent of identity theft, which we have been encountering in the real world for years. While identity theft involves taking over the real data of an existing person, synthetic identity is a clever combination of real and fake personal data, creating a completely new, seemingly credible “person” who does not actually exist. This could be a PESEL number belonging to a deceased person combined with a generated name, surname and address, as well as a photo and biometric data generated by AI. The scale of the problem in the banking sector is growing exponentially, and fraudsters are increasingly using these techniques to obtain loans, open accounts or hide the origin of money.
Evolution of Digital Threats
In the age of AI, we are currently witnessing one of the greatest evolutions in the digital world – from traditional frauds such as phishing to extremely advanced attacks based on artificial intelligence. Deepfake technologies, which enable the generation of hyper-realistic images, voices and videos, and generative artificial intelligence, which is capable of creating coherent and convincing narratives, make it increasingly difficult for people and IT systems to recognize a fake customer. It is estimated that the global costs of synthetic fraud amount to billions of dollars per year, constituting a serious burden for financial institutions and their customers.
Methods of creating synthetic identities
Data sources for constructing fake profiles
Fraudsters use data obtained from leaks that end up on darknet auctions, often combining it with information from social media or publicly available databases. Sometimes they also use data brokees who sell legally obtained information about customers from various sources. On their basis, they generate synthetic identity documents – ID cards, passports – which, although fake, can pass basic verification. Another key element is building a credit history “from scratch”, often by opening small accounts, taking out small loans and paying them off regularly to build the appearance of credibility. This makes the process extremely complex and spread over time, which additionally lulls the vigilance of systems verifying this data. It is worth remembering that in the financial sector, unlike the justice system, a “clean history” is not a good prognostic, but a warning light.
Technologies supporting fraud
Modern AI technologies are key to creating synthetic identities. Face generation algorithms such as StyleGN or DALL-E allow for the creation of realistic photos that can be used in documents. Synthetic voices and deepfake audio enable manipulation in phone conversations, and automation processes allows for mass submission of applications, minimizing the risk of detection.
Lifecycle of a synthetic identity
A synthetic identity often goes through a long “life cycle”. In the “growing” phase of the profile, lasting from 6 to 24 months, fraudsters build credibility by opening small accounts, taking on and repaying small liabilities that do not require thorough verification. The goal is to build a positive credit history. Only at the moment of “harvesting”, when the profile reaches an appropriate level of credibility, do losses maximize by taking out large loans or extorting significant sums of money.
Challenges for the banking sector
Weaknesses of traditional verification systems
Traditional identity verificationsystems and anti-fraud solutoins have their limitations. Document verification, although important, is increasingly easy to bypass with synthetic forgeries. Problems also arise with biometric verification, especially if a fraudster is able to create a credible deepfake. Loopholes in KYC (Know Your Customer) processes, which are often based on static data, are an ideal field for synthetic identities to operate.
Impact on various banking products
Synthetic identities can affect any banking product – from consumer and mortgage loans, through credit and payment cards, to investment and insurance products. In each of these areas, a fake client can generate losses for the bank, and consequently – for its real customers. Of course, due to the nature of data verification and decision-making processes, popular consumer loans or credit cards are most often used. However, bearing in mind the length of time it takes to build a synthetic identity, they are increasingly used to obtain larger funds, e.g. through an investment loan. Once funds are obtained using the same tools, they are either put into circulation as fully legal or withdrawn from the banking system to another country outside the EEA.
Regulatory and compliance aspects
Detecting synthetic fraud is also becoming a regulatory challenge. AML (Anti-Money Laundering) requirements and GDPR/GPDR regulations require banks not only to effectively counteract money laundering, but also to protect personal data, which makes it difficult to exchange information about suspicious transactions. This creates a kind of paradox in which the law makes it easier for fraudsters to operate. Banks are also legally liable for damage caused by fraud, which increases the pressure for effective solutions.
Synthetic Identity Detection Methods
Behavioral Analysis
Modern banking security systems must be based on behavioral analysis. Monitoring patterns of interaction with banking systems, detecting anomalies in digital behavior (e.g. unusual login times, strange clicks), and analyzing devices and locations from which access occurs can help identify synthetic identities. This type of data cannot be easily copied, and it is dificult to build repeatable patterns even using top AI solutions.
Advanced Data Analytics
Machine Learning plays a key role in detecting anomalies in huge data sets. Graph analytics allows for the identification of hidden relationships between seemingly unrelated accounts or applications. Correlations between different transactions and profiles can reveal the patterns of synthetic fraudsters.
Next-Generation Biometric Verification
Traditional biometrics are not enough. The implementation of “liveness detection” is becoming crucial, i.e. technology for detecting “liveness” that can distuinguish a real person from a deepfake. Analysis of facial micro-mimicry, natural reactions, as well as behavioral biometrics (e.g. the way of typing on the keyboard, mouse movements) is the future of identity verification. A picture or fingerprint alone is not enough at the moment. Only a live image of at least a dozen or so seconds in length effectively hinders the creation of synthetic identities.
External sources of verification
Integration with government databases, cooperation with bureaus and the use of data from social media (within the framework permitted by law) can provide additional information for identity verification and building a comprehensive picture of the customer.
Defensive Technologies and Tools
Artificial Intelligence in the Service of Security
AI is both a threat and a powerful defensive tool. Adversarial networks can be used to detect deepfakes, and predictive fraud risk models can assess the likelihood of an attack in real time. Real-time application scoring allows for immediate risk assessment when submitting an application.
Blockchain and distributed technologies
Technologies such as blockchain offer immutable identity records, making it difficult to falsify data. Decentralized verification and smart contrasts in KYC processes can revolutionize the way banks verify their customers, increasing security and transparency. With the popularization of blockchain and Edge AI technologies, criminals will have a much more difficult task.
Multimodal biometrics
The combination of different biometric features (e.g. face + voice + fingerprint) significantly increases security. Continuous biometrics, which monitor the user throughout the session, and adaptive authentication systems, which adjust the level of verification to the level of risk, are the next steps in building a resilient system.
Organizational and operational strategies
Building a culture of awareness
Technology is not everything. Building a culture of awareness among bank employees is equally important. Training for front-line employees, procedures for escalating suspicious cases, and close cooperation between departments (IT, security, compliance) are key to effectively detecting and responding to threats. It is worth remembering that the weakest element of any system is the human.
Optimizing onboarding processes
Multi-stage customer verification, implementing a risk-based approach, and finding the right balance between security and user experience are priorities in optimizing onboarding processes. Customers must feel safe, but the process cannot be too burdensome for them. The level of security must be balanced with convenience, which is a balancing act on a fine line, the hall of which is the safety of users and the financial institution itself.
Cross-industry collaboration
Exchanging information about threats, creating common databases of fraudsters and developing industry standards and best practices are essential elements of effective defense against synthetic identities. Fraudsters operate globally, so defense must be coordinated. Legislators should proceed as soon as possible to create a legal framework enabling the exchange of information between financial sector institutions, which are currently best prepared for this due to the implementation of numerous systems related to the protection of personal data.
The Future of Detecting Synthetic Identities
Emerging technologies
The future brings new challenges and new solutions. Quantum computers can revolutionize cryptography and create a completely new standard of security, and advanced AI detection systems will evolve with the development of generative models. The Internet of Things (IoT), with its ubiquitous sensors, may become a new source of data for identity verification and customer authentication.
Global regulatory trends
We expect new regulations on artificial intelligence in finance and further tightening of international cooperation of regulators. Global digital identity standards will be crucial to ensuring consistency and security on a global scale. Without them, it will be impossible to create a coherent security system at the central level.
Expected challenges
Unfortunately, we can expect an escalation of the “arms race” between fraudsters and defense systems. Privacy versus security will continue to be controversial, and the costs of implementing advanced systems will increase with their level of complexity.
Practical Recommendations
Short-Term Actions
In the short term, banks should audit their current fraud detection systems, implement basic AI tools to monitor anomalies, and strengthen identity verification processes. This step allows them to verify the initial state and the existing situation, based on which future strategies can be created.
Medium-Term Strategy
In the medium term, key are investments in advanced technologies, building specialist teams dealing with digital security, and developing technology partnerships with companies such as Edge One Solutions that offer innovative solutions in AI and data analytics.
Long-Term Vision
The long-term vision is proactive defensive strategies, active participation in shaping industry standards, staying ahead of the competition in the area of new technologies, a holistic approach to the IT ecosystem, and building a resilient security ecosystem that will be able to meet future challenges.
Summary and conclusions
Key tips
The problem of synthetic identities is a growing threat that requires a completely new approach to banking security. Effective defense is based on the synergy of advanced technologies (AI, blockchain, biometrics), optimized processes, as well as informed and trained staff. Extensive industry cooperation is also necessary.
There is no time to waste. It is necessary to act immediately, invest in education, and implement advanced technologies.
Want to learn how to effectively implement solutions that protect your bank from synthetic identities? Contact Edge One Solutions experts today to build a resilient security ecosystem that will protect your institution and customers from synthetic reality threats.
