With the rapid development of digital services, a large volume of personally identifiable information (PII) is stored online and is subject to cyberattacks such as Identity fraud. Most recently, the use of Artificial Intelligence (AI) enabled deep fake technologies has significantly increased the complexity of identity fraud. Fraudsters may use these technologies to create highly sophisticated counterfeit personal identification documents, photos and videos. These advancements in the identity fraud landscape pose challenges for identity fraud detection and society at large. There is a pressing need to review and understand identity fraud detection methods, their limitations and potential solutions. This research aims to address this important need by using the well-known systematic literature review method. This paper reviewed a selected set of 43 papers across 4 major academic literature databases. In particular, the review results highlight the two types of identity fraud prevention and detection methods, in-depth and open challenges. The results were also consolidated into a taxonomy of AI-based identity fraud detection and prevention methods including key insights and trends. Overall, this paper provides a foundational knowledge base to researchers and practitioners for further research and development in this important area of digital identity fraud.

Spam emails are unsolicited, annoying and sometimes harmful messages which may contain malware, phishing or hoaxes. Unlike most studies that address the design of efficient anti-spam filters, we approach the spam email problem from a different and novel perspective. Focusing on the needs of cybersecurity units, we follow a topic-based approach for addressing the classification of spam email into multiple categories. We propose SPEMC-15K-E and SPEMC-15K-S, two novel datasets with approximately 15K emails each in English and Spanish, respectively, and we label them using agglomerative hierarchical clustering into 11 classes. We evaluate 16 pipelines, combining four text representation techniques -Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words, Word2Vec and BERT- and four classifiers: Support Vector Machine, N\"aive Bayes, Random Forest and Logistic Regression. Experimental results show that the highest performance is achieved with TF-IDF and LR for the English dataset, with a F1 score of 0.953 and an accuracy of 94.6%, and while for the Spanish dataset, TF-IDF with NB yields a F1 score of 0.945 and 98.5% accuracy. Regarding the processing time, TF-IDF with LR leads to the fastest classification, processing an English and Spanish spam email in and on average, respectively.

They can also be used for financial fraud. Fraudsters can use deepfake technology to trick employees at financial institutions into changing account numbers and initiating money transfer requests for substantial amounts, says Satish Lalchand, principal at Deloitte Transaction and Business Analytics. He notes that these transactions are often difficult, if not impossible, to reverse. Cybercriminals are constantly adopting new techniques to evade know-your-customer verification processes and fraud detection controls. In response, many businesses are exploring ways machine learning (ML) can detect fraudulent transactions involving synthetic media, synthetic identity fraud, or other suspicious behaviors.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Deepfakes aren't new, but this AI-powered technology has emerged as a pervasive threat in spreading misinformation and increasing identity fraud. The pandemic made matters worse by creating the ideal conditions for bad actors to take advantage of organizations' and consumers' blindspots, further exacerbating fraud and identity theft. Fraud stemming from deepfakes spiked during the pandemic, and poses significant challenges for financial institutions and fintechs that need to accurately authenticate and verify identities.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! The rise in identity fraud has set new records in 2022. This was put in motion by fraudulent SBA loan applications totaling nearly $80 billion being approved, and the rapid rise of synthetic identity fraud. Almost 50% of Americans became victims of identity fraud between 2020 and 2022.

With more companies moving their business models online and adopting new solutions, it's been opening up more opportunities for cybercrime and identity theft. The spread of Covid-19 has only made it worse, as the Federal Trade Commission (FTC) estimated a loss of $13.4 million to Covid-19 scams as of April 15, 2020. This makes the protection of digital identity more important than ever before. This is also a new era of identity authentication. With artificial intelligence (AI) and biometrics, users today are enjoying more streamlined processes while reaping the benefits of added security.

Fraud may have disastrous consequences for both persons and businesses. Companies' reputations can be irreversibly damaged, forcing them to close, while individuals can incur huge personal losses. As hackers and fraudsters get more competent, the number of fraudulent transactions, large data breaches, and incidents of identity theft continue to rise. ID scanning software comes in a variety of flavors: some merely scan an ID's barcode, while more advanced software performs forensic and biometric testing to guarantee that the ID is not counterfeit. Artificial Intelligence in mobile app is proving to be effective in a variety of cybersecurity scenarios, allowing for accurate identity processing, verification, and authentication. Any fraud involving the use of a mobile app is known as mobile app fraud.

Revenue loss certainly grabs attention from business leaders across industries – but fraud affects all of us. It can take aim at a deeply personal level, such as with identity theft. It also spirals up to the level of massive fraud schemes stemming from organized crime rings. Let's look at five types of fraud to learn how they work and how analytics helps us fight back. Synthetic identity fraud happens when a criminal combines real and fabricated credentials to create a new, implied identity that's not associated with a real person.

Founded in 2012, Socure is the leader in high-assurance digital identity verification technology. Named to Forbes' 2019 AI 50 list as one of America's most promising AI companies, and a recent winner of API World's Best Data API, Socure's technology applies artificial intelligence and machine learning techniques with trusted online/offline data intelligence from email, address, phone, IP, social media and the broader Internet to verify identities in real-time. Customers include three of the top five U.S. banks, seven of the top 10 U.S. card issuers, as well as the majority of leading digital banks, lenders and insurers across the U.S. We are funded by some of the world's best investors and entrepreneurs including Scale Venture Partners, Commerce Ventures, Work-Bench, Santander InnoVentures and Two Sigma Ventures. Our trophy case includes numerous industry awards and accolades, including being named one of Forbes America's Best Startup Employers 2021 as well as the Best New Technology Introduced over the Last 12 months – Data and Data Services at the 2020 American Financial Technology Awards (AFTAs), being ranked #70 on Deloitte's Technology Fast 500, getting listed as a Gartner Cool Vendor, and winning Finovate's Award for Best Use of AI/ML, to name a few! The only way we can further our mission of becoming the single, trusted source of identity verification and eliminating identity fraud is by building the best team on the planet. This is where you come in!

With more companies moving their business models online and adopting new solutions, it's been opening up more opportunities for cybercrime and identity theft. The spread of Covid-19 has only made it worse, as the Federal Trade Commission (FTC) estimated a loss of $13.4 million to Covid-19 scams as of April 15, 2020. Let's take a further look into how organizations can secure data and identities through AI. Since the rise of Covid-19, businesses and consumers have had to onboard many digital processes in their lives. This has led to a vast production of data and, subsequently, to the rapid development of cybercrime and digital identity theft.