Immigration agents have used Mobile Fortify to scan the faces of countless people in the US--including many citizens. On Wednesday, the Department of Homeland Security published new details about Mobile Fortify, the face recognition app that federal immigration agents use to identify people in the field, undocumented immigrants and US citizens alike. The details, including the company behind the app, were published as part of DHS's 2025 AI Use Case Inventory, which federal agencies are required to release periodically. The inventory includes two entries for Mobile Fortify--one for Customs and Border Protection (CBP), another for Immigration and Customs Enforcement (ICE)--and says the app is in the "deployment" stage for both. CBP says that Mobile Fortify became "operational" at the beginning of May last year, while ICE got access to it on May 20, 2025.

Object recognition and detection are well-studied problems with a developed set of almost standard solutions. Identity documents recognition, classification, detection, and localization are the tasks required in a number of applications, particularly, in physical access control security systems at critical infrastructure premises. In this paper, we propose the new original architecture of a model based on an artificial convolutional neural network and semantic segmentation approach for the recognition and detection of identity documents in images. The challenge with the processing of such images is the limited computational performance and the limited amount of memory when such an application is running on industrial oneboard microcomputer hardware. The aim of this research is to prove the feasibility of the proposed technique and to obtain quality metrics. The methodology of the research is to evaluate the deep learning detection model trained on the mobile identity document video dataset. The dataset contains five hundred video clips for fifty different identity document types. The numerical results from simulations are used to evaluate the quality metrics. We present the results as accuracy versus threshold of the intersection over union value. The paper reports an accuracy above 0.75 for the intersection over union (IoU) threshold value of 0.8. Besides, we assessed the size of the model and proved the feasibility of running the model on an industrial one-board microcomputer or smartphone hardware.

Accurate barcode detection and decoding in Identity documents is crucial for applications like security, healthcare, and education, where reliable data extraction and verification are essential. However, building robust detection models is challenging due to the lack of diverse, realistic datasets an issue often tied to privacy concerns and the wide variety of document formats. Traditional tools like Faker rely on predefined templates, making them less effective for capturing the complexity of real-world identity documents. In this paper, we introduce a new approach to synthetic data generation that uses LLMs to create contextually rich and realistic data without relying on predefined field. Using the vast knowledge LLMs have about different documents and content, our method creates data that reflects the variety found in real identity documents. This data is then encoded into barcode and overlayed on templates for documents such as Driver's licenses, Insurance cards, Student IDs. Our approach simplifies the process of dataset creation, eliminating the need for extensive domain knowledge or predefined fields. Compared to traditional methods like Faker, data generated by LLM demonstrates greater diversity and contextual relevance, leading to improved performance in barcode detection models. This scalable, privacy-first solution is a big step forward in advancing machine learning for automated document processing and identity verification.

Microblink, a global leader in AI-powered computer vision technology, has been listed as one of the fastest growing companies on the prestigious Deloitte's Technology Fast 500 list. Microblink's exponential growth rate of 284 percent is attributed to its enterprise-ready AI solutions and best-in-class user experience born of a culture of curiosity and a fearless team. Recognized for the third time, Microblink's technology leverages Artificial Intelligence and Machine Learning to generate solutions for its two business segments; Identity and Commerce. The identity product portfolio encompasses solutions for ID scanning, identity document verification and identity verification to improve the customer onboarding experience. For commerce businesses, they are developing solutions that create magical shopping experiences while using purchasing data to empower retailers and CPGs.

Creating efficiencies in your business is at the top of your list. You want your employees to be more productive, have them focus on high impact tasks, or find ways to implement better processes to improve the outcomes to your customers. There are various ways to solve this problem, and more companies are turning to artificial intelligence (AI) and machine learning (ML) to help. In the financial services sector, there is the creation of new accounts online, or in healthcare there are new digital platforms to schedule and manage appointments, which require users to fill out forms. These can be error prone, time consuming, and certainly improved upon.

RegTech is the management of regulatory processes within the financial industry through technology. The main functions of RegTech include regulatory monitoring, reporting, and compliance. Europe is home to about 140 regtech startups- 30% of those startups specialize in compliance management, 27% focus on know your customer (KYC) and anti-money laundering (AML) automation, and 26% leverage technology and data to provide risk management tools, according to a data provided by XAnge, a Franco-German venture capital firm. London headquartered ComplyAdvantage is a RegTech company that leverages uses AI and ML to help firms manage compliance obligations. The company provides data intelligence to help firms understand the risk of who they're doing business with, while automating compliance and risk processes.

Combining biometric identification with artificial intelligence (AI) enables banks to take a new approach to verifying the digital identity of their prospects and customers. Biometrics is the process by which a person's unique physical and personal traits are detected and recorded by an electronic device or system as a means of confirm identity. Biometric identifiers are unique to individuals, so they are more reliable in confirming identity than token and knowledge-based methods, such as identity cards and passwords. Biometric identifiers are often categorized as physiological identifiers that are related to a person's physicality and include fingerprint recognition, hand geometry, odor/scent, iris scans, DNA, palmprint, and facial recognition. But how do you ensure the effectiveness of identifying a customer when they are not physically in the presence of the bank employee?

Onfido's proprietary technology verifies that people are who they claim to be simply by comparing an identity document to a selfie, and is helping convert the previously unbanked for a number of leading FinTechs. Almost 40% of the world's adult population is excluded from opening a bank account, most often due to having thin credit files, or being new migrants without the requisite documents needed for in-person verification and legally required Anti-Money Laundering checks. Onfido solves this issue using a combination of a highly sophisticated document check alongside a Facial Similarity Check. Onfido's Machine Learning-based solution has the benefit of being able to scan and compute identity documents more easily and more accurately than other technology. Typically requiring complex and expensive hardware to complete accurate image capture (like airport scanners), Onfido's Machine Learning technology can accurately identify documents that have been uploaded using commodity technology like scanners and lower-res smartphone cameras.