The Role of AI in Identity Verification and Fraud Detection

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Artificial intelligence, or AI, has swept over the digital world so thoroughly that it is currently the term most people seek on search engines. With major new technologies, including everyone’s favorite ChatGPT, artificial intelligence has become mainstream and a part of the discourse, with speculations that this pop culture phenomenon will take people’s jobs.

While that might be a stretch, fraudsters now also use such tools and AI for their own benefit. This means creating better phishing messages, more realistic deepfakes, and other threats that lead to breaches, account takeovers, or easily bypassed verification systems.

This article delves deeper into how artificial intelligence changes specific industries, highlighting new developments, obstacles, and potential paths, particularly focusing on its nuances in identity verification and fraud detection.

How AI Forever Changed Identity Verification

Identity verification is critical in today’s digital world. Many interactions happen online, from buying and selling to hosting meetings, connecting with strangers, and rendering services. It’s now a duty of protection and trust for the safety of every user. When we think of verifying our identity, we now mostly imagine doing it online: uploading a photo of a government-issued document or using our facial biometrics to access different platforms, or simply unlocking our iPhone.

Previously, this was a more cumbersome process, which required customers to visit physical banking branches, wait hours in lines, and have their physical features inspected by a real Know Your Customer (KYC) specialist. In contrast, we now have bots and AI-powered verification systems that eliminate the need to leave your house and bring paper-based documents to a physical location.

However, why is the ID verification process important, you might wonder? For starters, various businesses simply need identity verification. This includes banking, e-commerce, healthcare, crypto platforms, iGaming establishments, sometimes even streaming services, or popular adult-content websites, where identity verification is intertwined with age verification requirements, obligating businesses to check their user’s legal age to ensure ethical and compliance requirements.

So, it’s safe to say that AI technology can speed up this process by reducing the need for manual intervention and automating complex tasks. What’s more, for service providers and digital marketers, customer trust isn’t a joke but a massive deal because once this trust is broken, it might never be built again or might not be viable like before.

Why Is Everyone Switching to Automation and AI

AI has been built on large data sets, both labeled and unlabelled, which makes it possible to have accurate knowledge to identify fraudulent and genius activity. AI solutions are now built on large language models (LLM) or natural language processing (NLP). Compared to a real-life person, who doesn’t stand a chance when it comes to efficiency, businesses now switch to AI and choose automation to complete different tasks, which, previously, required manual labor.

Some use cases where AI and automation are beneficial include:

AI in Biometric Authentication

Using deep learning algorithms, AI-powered facial recognition systems map facial traits from pictures or video streams. These systems can recognize and match a user’s face to a database to confirm someone’s identity, and this way grants them access to various online platforms in a simple and efficient manner.  This method of biometric authentication is extensively utilized in access control, smartphone unlocking, and airport security.

Additionally, biometric systems now often have built-in liveness detection technology, which is a common part of many AI-backed verification systems. This technology is able to check the user’s engagement with the system, confirming that the user’s movements are natural. To verify liveness, AI examines minute human motions like breathing, blinking, and head motion, making it more difficult for fraudsters to spoof the system. That means liveness direction identifies phony images, videos, masks, and other spoofing attempts.

AI in Voice Recognition

Similar to face recognition, AI can also be used for authentication purposes, just with voice recognition. AI-based natural language processing (NLP) systems can examine speech rhythm, pitch, and pattern. These systems enable safe voice-based authentication for virtual assistants, online banking, call centers, and translation services, such as Google Translate. Other more recent advancements in this technology include virtual doctor’s assistants and autonomous bank deposits.

AI in Fingerprint and Iris Scanning

AI enhances real-time pattern recognition, increasing the precision and speed of biometric systems such as fingerprint and iris scanning. These techniques are very safe because they are almost hard to forge, especially now that AI systems can accurately identify fingerprints and their unique features, confirming whether the fingerprint belongs to the same person or not. The same principle is applied to iris recognition, where unique features are analyzed by extracting information from the eye.

Document Verification

Optical Character Recognition (OCR) can use artificial intelligence to examine identity documents, including ID cards, passports, and driver’s licenses. This is important because criminals often use forged documents to access fintech platforms, obtain loans, or conduct identity theft and commit further financial crimes without ever getting caught. Thankfully, AI models trained on massive datasets can quickly extract pertinent data (such as name, DOB, and ID number) and authenticate documents, such as IDs, passports or driver’s licenses, using official government databases.

AI systems also examine patterns in document manipulation, variations in fonts, holograms, watermarks, and microprinting to identify forgeries. As AI gains knowledge from previous fraud attempts, this process improves over time and results in constant model improvement. Compared to traditional manual checks, AI-powered software wins, as humans are linked to errors and misjudgment, especially regarding subjective assessment of different documents.

Other Popular Use Cases of AI in Fraud Detection

Fraud detection is the process of detecting crimes, like money laundering, including less serious crimes, such as account takeovers online. No matter the scale of the fraudulent scheme, it harms the clients, the company, and the overall financial system, especially when fraud is now linked to organized crime online.

Here, criminals use bots and other schemes to attack and breach multiple systems at once. More importantly, they do it successfully, and this means financial losses for businesses of all kinds. That said, companies can customize their fraud detection strategies, but most systems, once again, rely on AI and automation to function effectively.

Look into some common examples of how AI-powered fraud detection technologies are used:

Machine Learning for Anomaly Detection

Machine learning models and AI trained on labeled data can analyze previous data on recognized events to assess whether a transaction or activity is fraudulent or legal. This method has proven effective at detecting the known types of fraud, and its limitations are in the new and changing fraud schemes. In the meantime, semi-supervised learning in AI fraud detection is the combination of labeled and unlabelled data, and this is used when labeled fraud data is limited, but the model can be improved.

For example, unsupervised models, such as clustering and autoencoders, are essential for detecting previously unknown or changing fraud patterns. These algorithms detect outliers or behaviors that depart significantly from the norm and may imply fraud, such as large purchases, sudden cross-border transactions, multiple payments just below the reporting threshold, purchases of the same item, and so on.

Real-Time Transaction Monitoring

Artificial intelligence provides real-time fraud detection by consistently analyzing extensive data farmed from internet transactions, card payments, transfers, and others. In general, the algorithms examine transactions to identify fraud patterns based on different risk factors.

For example, there’s a geolocation risk, so purchases made in two countries within minutes or items bought from high-risk locations, with high rates of financial crime, can trigger alerts. On top of that, to assess the overall fraud risk, AI is able to  combine real-time transactional data with contextual information (device fingerprinting, location data, IP address, browsing behavior).

Generative AI for Deepfake Identification

Generative Adversarial Networks (GANs)  can generate realistic false data (such as faces and voices) that avoid weak verification methods. To address this, AI systems are trained on such data to detect and prevent fake videos or fake images, voice recordings and other types of media, better known under the term “deepfakes”.

In general, deepfake technology learns from hundreds or thousands of examples, quickly analyzing facial features, expressions, speech patterns, behavior, and more. Replicating these details with such accuracy would be difficult for humans to achieve in real life. So, once again, regulated entities, such as banks, fintechs, and crypto platforms, use verification and fraud detection measures that are fully automated and can quickly spot forgeries and close the door on fraudsters.

Conclusion

AI plays a critical role in identity verification and fraud detection, providing the ability to detect complicated fraud schemes while retaining scalability, efficiency, and security. However, as fraudsters increasingly use AI, the industry becomes more hostile, necessitating ongoing innovation and the development of more robust and adaptive models. The future of fraud detection will most likely involve the integration of AI with other new technologies, such as blockchain, federated learning, and quantum computing, all to stay ahead of evolving fraud strategies.

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