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Learn what lending fraud is, what you can do to safeguard your organization and consumers and how we can help. Read more!

An OTP bot is an automated tool designed to trick users into revealing their one-time password, a temporary code used in MFA.

Here are the top fraud trends and actionable resolutions to help risk managers stay ahead of fraud in 2025.

Bots have been a consistent thorn in fraud teams’ side for years. But since the advent of generative AI (genAI), what used to be just one more fraud type has become a fraud tsunami. This surge in fraud bot attacks has brought with it:  A 108% year-over-year increase in credential stuffing to take over accounts1  A 134% year-over-year increase in carding attacks, where stolen cards are tested1  New account opening fraud at more than 25% of businesses in the first quarter of 2024  While fraud professionals rush to fight back the onslaught, they’re also reckoning with the ever-evolving threat of genAI. A large factor in fraud bots’ new scalability and strength, genAI was the #1 stress point identified by fraud teams in 2024, and 70% expect it to be a challenge moving forward, according to Experian’s U.S. Identity and Fraud Report.  This fear is well-founded. Fraudsters are wasting no time incorporating genAI into their attack arsenal. GenAI has created a new generation of fraud bot tools that make bot development more accessible and sophisticated. These bots reverse-engineer fraud stacks, testing the limits of their targets’ defenses to find triggers for step-ups and checks, then adapt to avoid setting them off.   How do bot detection solutions fare against this next generation of bots?  The evolution of fraud bots   The earliest fraud bots, which first appeared in the 1990s2 , were simple scripts with limited capabilities. Fraudsters soon began using these scripts to execute basic tasks on their behalf — mainly form spam and light data scraping. Fraud teams responded, implementing bot detection solutions that continued to evolve as the threats became more sophisticated.   The evolution of fraud bots was steady — and mostly balanced against fraud-fighting tools — until genAI supercharged it. Today, fraudsters are leveraging genAI’s core ability (analyzing datasets and identifying patterns, then using those patterns to generate solutions) to create bots capable of large-scale attacks with unprecedented sophistication. These genAI-powered fraud bots can analyze onboarding flows to identify step-up triggers, automate attacks at high-volume times, and even conduct “behavior hijacking,” where bots record and replicate the behaviors of real users.  How next-generation fraud bots beat fraud stacks  For years, a tried-and-true tool for fraud bot detection was to look for the non-human giveaways: lightning-fast transition speeds, eerily consistent keystrokes, nonexistent mouse movements, and/or repeated device and network data were all tell-tale signs of a bot. Fraud teams could base their bot detection strategies off of these behavioral red flags.  Stopping today’s next-generation fraud bots isn’t quite as straightforward. Because they were specifically built to mimic human behavior and cycle through device IDs and IP addresses, today’s bots often appear to be normal, human applicants and circumvent many of the barriers that blocked their predecessors. The data the bots are providing is better, too3, fraudsters are using genAI to streamline and scale the creation of synthetic identities.4 By equipping their human-like bots with a bank of high-quality synthetic identities, fraudsters have their most potent, advanced attack avenue to date.   Skirting traditional bot detection with their human-like capabilities, next-generation fraud bots can bombard their targets with massive, often undetected, attacks. In one attack analyzed by NeuroID, a part of Experian, fraud bots made up 31% of a business's onboarding volume on a single day. That’s nearly one-third of the business’s volume comprised of bots attempting to commit fraud. If the business hadn’t had the right tools in place to separate these bots from genuine users, they wouldn’t have been able to stop the attack until it was too late.   Beating fraud bots with behavioral analytics: The next-generation approach  Next-generation fraud bots pose a unique threat to digital businesses: their data appears legitimate, and they look like a human when they’re interacting with a form. So how do fraud teams differentiate fraud bots from an actual human user?  NeuroID’s product development teams discovered key nuances that separate next-generation bots from humans, and we’ve updated our industry-leading bot detection capabilities to account for them. A big one is mousing patterns: random, erratic cursor movements are part of what makes next-generation bots so eerily human-like, but their movements are still noticeably smoother than a real human’s. Other bot detection solutions (including our V1 signal) wouldn’t flag these advanced cursor movements as bot behavior, but our new signal is designed to identify even the most granular giveaways of a next-generation fraud bot.  Fraud bots will continue to evolve. But so will we. For example, behavioral analytics can identify repeated actions — down to the pixel a cursor lands on — during a bot attack and block out users exhibiting those behaviors. Our behavior was built specifically to combat next-gen challenges with scalable, real-time solutions. This proactive protection against advanced bot behaviors is crucial to preventing larger attacks.  For more on fraud bots’ evolution, download our Emerging Trends in Fraud: Understanding and Combating Next-Gen Bots report.  Learn more Sources 1 HUMAN Enterprise Bot Fraud Benchmark Report  2 Abusix 3 NeuroID 4 Biometric Update

Learn how background screeners can optimize pre-employment verification processes, reduce fraud risks, and ensure compliance.

Browser fingerprinting, alongside other fraud prevention tools, can help businesses safeguard their operations and uphold customer trust.

Our behavioral analytics solutions help you stop fraud rings, fraud bots, and other third-party fraud attacks.

Bot attacks are plaguing financial institutions. The risks are significant, weakening both security and financial stability.

With fraudsters continuously refining their methods, fintechs must invest in advanced fintech fraud detection and prevention solutions.

AI fraud detection helps organizations enhance their security measures, reduce fraud losses, authenticate identity, and improve customer experience.

“Learn how to learn.” One of Zack Kass’, AI futurist and one of the keynote speakers at Vision 2024, takeaways readily embodies a sentiment most of us share — particularly here at Vision. Jennifer Schulz, CEO of Experian, North America, talked about AI and transformative technologies of past and present as she kicked off Vision 2024, the 40th Vision. Keynote speaker: Dr. Mohamed El-Erian Dr. Mohamed El-Erian, President of Queens’ College, Cambridge and Chief Economic Advisor at Allianz, returned to the Vision stage to discuss the labor market, “sticky” inflation and the health of consumers. He emphasized the need to embrace and learn how to talk to AI engines and that AI can facilitate content, creation, collaboration and community Keynote speaker: Zack Kass Zack Kass, AI futurist and former Head of Go-To-Market at OpenAI, spoke about the future of work and life and artificial general intelligence. He said AI is aiding in our entering of a superlinear trajectory and compared the thresholds of technology versus those of society. Sessions – Day 1 highlights The conference hall was buzzing with conversations, discussions and thought leadership. Some themes definitely rose to the top — the increasing proliferation of fraud and how to combat it without diminishing the customer experience, leveraging AI and transformative technology in decisioning and how Experian is pioneering the GenAI era in finance and technology. Transformative technologiesAI and emerging technologies are reshaping the finance sector and it's the responsibility of today's industry leaders to equip themselves with cutting-edge strategies and a comprehensive understanding to master the rapidly evolving landscape. That said, transformation is a journey and aligning with a partner that's agile and innovative is critical. Holistic fraud decisioningGenerative AI, a resurgence of bank branch transactions, synthetic identity and pig butchering are all fraud trends that today's organizations must be acutely aware of and armed to protect their businesses and customers against. Leveraging a holistic fraud decisioning strategy is important in finding the balance between customer experience and mitigating fraud. Unlocking cashflow to grow, protect and reduce riskCash flow data can be used not only across the lending lifecycle, but also as part of assessing existing portfolio opportunities. Incorporating consumer-permissioned data into models and processes powers predicatbility and can further assess risk and help score more consumers. Navigating the economyAmid a slowing economy, consumers and businesses continue to struggle with higher interest rates, tighter credit conditions and rising delinquencies, creating a challenging environment for lenders. Experian's experts outlined their latest economic forecasts and provided actionable insights into key consumer and commercial credit trends. More insights from Vision to come. Follow @ExperianVision and @ExperianInsights to see more of the action.

For lenders, mitigating first payment default requires data, advanced analytics, customer engagement, and agile risk management.

Anti-money laundering and fraud prevention have historically been separated, but here's why that might not be a good idea.

As part of banks’ anti-money laundering (AML) programs, KYC in banking can help stop corruption, money laundering and terrorist financing. Creating and maintaining KYC programs is also important for regulatory compliance, reputation management and fraud prevention.  The three components of KYC in banking programs Banks can largely determine how to set up their KYC and AML programs within the applicable regulatory guidelines. In the United States, KYC needs to happen when banks initially onboard a new customer. But it’s not a one-and-done event—ongoing customer and transaction monitoring is also important.  1. Customer Identification Program (CIP) Creating a robust Customer Identification Program (CIP) is an essential part of KYC. At a minimum, a bank’s CIP requires it to collect the following information from new customers: Name Date of birth Address Identification number, such as a Social Security number (SSN) or Employer Identification Number (EIN) Banks' CIPs also have to use risk-based procedures to verify customers’ identities and form a reasonable belief that they know the customer's true identity.1 This might involve comparing the information from the application to the customer’s government-issued ID, other identifying documents and authoritative data sources, such as credit bureau databases. Additionally, the bank's CIP will govern how the bank:  Retains the customer’s identifying information Compares customer to government lists  Provides customers with adequate notices Banks can create CIPs that meet all the requirements in various ways, and many use third-party solutions to quickly collect data, detect forged or falsified documents and verify the provided information.  2. Customer due diligence (CDD)  CIP and CDD overlap, but the CIP primarily verifies a customer’s identity while customer due diligence (CDD) helps banks understand the risk that each customer poses. To do this, banks try to understand what various types of customers do, what those customers’ normal banking activity looks like, and in contrast, what could be unusual or suspicious activity.  Financial institutions can use risk ratings and scores to evaluate customers and then use simplified, standard or enhanced due diligence (EDD) processes based on the results. For example, customers who might pose a greater risk of laundering money or financing terrorism may need to undergo additional screenings and clarify the source of their funds. 3. Ongoing monitoring Ongoing or continuous monitoring of customers’ identities and transactions is also important for staying compliant with AML regulations and stopping fraud.  The monitoring can help banks spot a significant change in the identity of the customer, beneficial owner or account, which may require a new KYC check. Unusual transactions can also be a sign of money laundering or fraud, and they may require the bank to file a suspicious activity report (SAR). Why is KYC important in banking? Understanding and implementing KYC in banking processes can be important for several reasons:  Regulatory compliance: Although the specific laws and rules can vary by country or region, many banks are required to have AML procedures, including KYC. The fines for violating AML regulations can be in the hundreds of millions— a few banks have been fined over $1 billion for lax AML enforcement and sanctions breaching. Reputation management: In some cases, enforcement actions and fines were headline news. Banks that don’t have robust KYC procedures in place risk losing their customers' trust and respect.  Fraud prevention: In addition to the regulatory requirements, KYC policies and systems can also work alongside fraud management solutions for banks. Identity verification at onboarding can help banks identify synthetic identities attempting to open money mule accounts or take out loans. Ongoing monitoring can also be important for identifying long-term fraud schemes and large fraud rings.  KYC in a digital-first world Modernizing KYC in banking is a key part of financial institutions’ digital transformation efforts. Part of that journey is updating the systems and tools in place to meet the expectations of customers and regulators. Experian’s 2025 U.S. Identity and Fraud Report shows four in 10 consumers considered abandoning a new account setup midway through the process – rising to 50% among high-income earners – highlighting growing expectations for seamless digital experiences. The survey wasn’t specific to financial services, but friction could be a problem for banks wanting to attract new account holders. Just as access to additional data sources and machine learning help automate underwriting, financial institutions can use technological advances to add an appropriate amount of friction based on various risk signals. Some of these can be run in the background, such as an electronic Consent Based Social Security Number Verification (eCBSV) check to verify the customer’s name, SSN and date of birth match the Social Security Administration’s records. Others may require more customer involvement, such as taking a selfie that’s then compared to the image on their photo ID — Experian CrossCore® Doc Capture enables this type of verification.  Experian is a leader in identity and data management  Our identity verification solutions use proprietary and third-party data to help banks manage their KYC procedures, including identity verification and Customer Identification Programs (CIP). As a global leader in identity management and fraud prevention, we combine advanced analytics, rich data assets, and innovative technology to deliver secure, seamless identity experiences. Our comprehensive identity ecosystem enables organizations to confidently verify, authenticate and manage customer identities across the lifecycle—reducing fraud risk, improving compliance and enhancing the customer experience.By bundling identity verification with fraud assessment, banks can stop fraudsters while quickly resolving identity discrepancies. The automated processes also allow you to offer a low-friction identity verification experience and use step-up authentications as needed.  Explore identity solutions

New account fraud can pose a serious risk to your business, and your current security methods might be hurting your customers' experience.