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As consumer credit behaviors change, staying informed is crucial for credit unions. Experian’s new State of Credit Unions Report offers a comprehensive analysis of consumer credit trends from January 2022 to October 2024, with a particular focus on auto loans, unsecured personal loans and unsecured credit cards. The report covers: Trends in originations and delinquencies among credit union members How credit unions fare against other fintech lenders and regional banks Strategies to attract new members and mitigate portfolio risk Discover key insights into the consumer credit landscape and how credit unions stack up against other financial institutions. Download the report

Four capabilities to consider for improved coverage and customer experience. Identity verification during account opening is the foundation for building trust between consumers and businesses. Consumers expect a seamless and convenient experience, and with the ease and optionality of online banking, are willing to look for alternatives that offer less friction. According to Experian research, 92% of consumers feel it's important for the businesses they deal with online to identify or recognize them on a repeated basis accurately, but only 16% have high confidence that this is happening. The disconnect between consumers’ expectations for online identity verification and the digital experiences they encounter is leading to reduced satisfaction and increased abandonment during new account opening processes. According to recent research by Experian, 38% of consumers surveyed considered ending a new account opening mid-way through the process due to poor experience. In addition, the same research found that nearly one-fifth of consumers had moved their business elsewhere because of this. Amidst the quest for convenience lies a pressing concern: ensuring the integrity of accounts being opened and protecting against fraud. Businesses continue to experience increasing fraud losses, Juniper Research forecasts that merchant losses from online payment fraud will exceed $362 billion globally between 2023 and 2028, with losses of $91 billion alone in 2028. Identity verification serves as the first line of defense in protecting both financial institutions and consumers. By verifying the identities of individuals before granting them access to services, businesses can mitigate the risk of identity theft, account takeover, and other forms of fraud. Four capabilities to consider when building out an identity verification strategy Personally Identifiable Information (PII) dataComparing consumer input data to a comprehensive data set helps effectively validate the consumer without disrupting customer experience. Details like name, address, date of birth, and social security number provide valuable identity information to verify identities quickly and accurately. Identity graphUsing an identity graph leveraging advanced analytics and data linking techniques helps prevent synthetic IDs from getting through. By mapping relationships between identity attributes, you can easily identify patterns and connections within the data and detect anomalies or inaccuracies in the information provided. Alternative data“Thin file” consumers are often rejected due to a lack of traditional data. Using alternative data like phone ownership and email data helps not only verify that the identity is real but also improves coverage, so you are not rejecting good customers. Document verificationHaving a document verification provider that seamlessly integrates into your identity verification workflow is essential for robust identity verification. Validating good users early in the account opening process helps keep fraudsters out so good users are not subject to stringent identity checks later on during onboarding. Next steps A strong identity verification process builds trust by demonstrating a commitment to protecting and safeguarding consumer data. A proper identity verification workflow would minimize the impact of friction for consumers and help organizations manage fraud and regulatory compliance by examining specific business needs on a case-by-case basis. Identifying the right mix of capabilities through analytics and feedback while utilizing the best data reduces the cost of manual verification and helps onboard good customers faster. Learn more Research conducted in March 2024 by Experian in North America

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

Generative AI (GenAI) is transforming the financial services industry, driving innovation, efficiency and cost savings across various domains. By integrating GenAI into their operations, financial institutions can better respond to rapidly changing environments. GenAI is reshaping financial services from customer engagement to compliance, leading to streamlined operations and enhanced decision-making. The strategic role of GenAI in financial services Adopting GenAI in financial services is now a strategic imperative. A 2024 McKinsey report (The State of AI in 2024) notes more than a 10% revenue increase for companies using GenAI. As institutions strive to stay competitive, GenAI provides powerful tools to enhance customer experiences, optimize operations, accelerate regulatory compliance, and expedite coding and software development. Key areas where GenAI is making an impact Enhanced customer engagement Financial institutions use GenAI to offer personalized products and services. By analyzing real-time customer data, GenAI enables tailored recommendations, boosting satisfaction and retention. Streamlining and optimizing operations GenAI automates tasks like data entry and transaction monitoring, freeing up resources for strategic activities. This accelerates workflows and reduces errors. Further, GenAI-driven efficiency directly cuts costs. By automating processes and optimizing resources, institutions can lower overhead and invest more in innovation. Deloitte’s Q2 2024 study found AI automation reduced processing times by up to 60% and operational costs by 25%. Accelerating regulatory compliance GenAI simplifies compliance by automating data collection, analysis and reporting. This ensures regulatory adherence while minimizing risks and penalties. According to a 2024 Thomson Reuters survey, AI-driven compliance reduced reporting times by 40% and costs by 15%. Developer coding support for efficiencies GenAI is an invaluable tool for programmers. It aids in code generation, task automation and debugging, boosting development speed and allowing focus on innovation. Gartner’s 2024 research highlights a 30% improvement in coding efficiency and a 25% reduction in development timeframes due to GenAI. Accelerating credit analytics with Experian Assistant Within the credit risk management space, GenAI offers a powerful solution that addresses some known pain points. These relate to mining vast amounts of data for insight generation and coding support for attribute selection and creation, model development, and expedited deployment. Experian Assistant is a game-changer in modernizing analytics workflows across the data science lifecycle. Integrated into the Experian Ascend™ platform, it’s specifically designed for analytics and data science teams to tackle the challenges of data analysis, model deployment and operational efficiency head-on. Capabilities and skills of Experian Assistant Data tutor: Offers comprehensive insights into Experian’s data assets, enabling users to make informed decisions and optimize workflows Analytics expert: Provides tailored recommendations for various use cases, helping users identify the most predictive metrics and enhance model accuracy Code advisor (data prep): Automatically generates code for tasks like data merging and sampling, streamlining the data preparation process Code advisor (analysis): Generates code for risk analytics and modeling tasks, including scorecard development and regulatory analyses Tech specialist: Facilitates model deployment and documentation, minimizing delays and ensuring a seamless transition from development to production Driving more-informed decisions Adopting GenAI will be key to maintaining competitiveness as the financial services industry evolves. With projections showing significant growth in GenAI investments by 2025, the potential for enhanced efficiencies, streamlined operations and cost savings is immense. Experian Assistant is at the forefront of this transformation, addressing the bottlenecks that slow down analytical processes and enabling financial institutions to move faster, more informed and with greater precision. By integrating the capabilities of the Experian Assistant, financial institutions can leverage GenAI in credit risk management, automate data processes, and develop customized analytics for business decision-making. This alignment with GenAI’s broader benefits—like operational streamlining and improved customer experience—ensures better risk identification, workflow optimization, and more informed decisions. To learn more about how Experian Assistant can transform your data analytics capabilities, watch our recent tech showcase and book a demo with your local Experian sales team. Watch tech showcase Learn more

Effective collection strategies are critical for the financial health of credit unions. Unlike traditional banks, credit unions often emphasize member relationships and community values, making the collection process more tactful. Crafting a strategy that balances the need for financial stability with member-centric values is essential.  Here’s a step-by-step guide on how to create an effective credit union collection strategy. 1. Understand your members The foundation of an effective credit union collection strategy is understanding your members. Credit unions often serve specific communities or groups, and members may face unique financial challenges. By analyzing member demographics, financial behavior, and common reasons for delinquency, you can tailor your approach to be more vigilant and effective. Segment members: Group members based on factors like loan type, payment history, and financial behavior. This allows for targeted communications and outreach strategies. Member communication preferences: Determine how your members prefer to be contacted—whether by phone, email, or in person. This can increase engagement and responsiveness. 2. Prioritize compliance Compliance with regulations is non-negotiable in the collections process. Ensure that your strategy adheres to all relevant laws and guidelines. Fair Debt Collection Practices Act (FDCPA): Ensure that your team is fully trained on the FDCPA and that your practices comply with its requirements. State and local regulations: Be aware of any state or local regulations that may impact your collections process. This could include restrictions on contact methods or times. Internal audits: Regularly conduct internal audits to ensure compliance and identify any areas of risk. 3. Leverage technology for efficiency Technology can streamline the collection process, making it more efficient and a better member experience. Automated reminders: Use automated systems to send reminders before and after payment due dates. This reduces the likelihood of missed payments due to forgetfulness. Data analytics: Use data analytics to identify trends in member behavior, establish a collections prioritization strategy, and predict potential delinquencies. This allows your team to be proactive rather than reactive. Digital communication channels: Implement digital communication options, such as text messages or chatbots to make it easier for members to interact with the credit union. 4. Establish clear communication protocols Early and frequent communication is key to preventing delinquency and managing it when it occurs. Create clear protocols for member communication that prioritize empathy and treatment plans over demands. Early intervention: Reach out to members as soon as they miss a payment. Early intervention can prevent minor issues from escalating. Consistent communication: Ensure that your communication is consistent across all channels. Whether a member receives a call, an email, or a letter, the message should be clear and aligned with the credit union’s values. Human understanding: Train your collections team to use a compassionate tone. Members are more likely to respond positively when they feel understood and respected. 5. Offer flexible payment solutions Flexibility is crucial when working with members who are struggling financially. Offering a range of payment solutions can help members stay on track and reduce the likelihood of default. Customized treatment plans: Offer customizable payment plans that fit the member’s financial situation. This could include lower payments over a longer term or temporary payment deferrals. Loan modifications: In some cases, modifying the terms of the loan—such as extending the repayment period or lowering the interest rate—may be necessary to help the member succeed. Debt consolidation options: If a member has multiple loans, consider offering debt consolidation to simplify their payments and reduce their overall financial burden. 6. Train your collection team Your collection team is the frontline of your strategy. Providing them with the right training and tools is essential for success. Ongoing training: Regularly update your team on the latest regulations, best practices, and communication techniques. This keeps them informed and prepared to handle various situations. Better decision making: Empower your team to make decisions that align with the credit union’s values. This could include offering payment extensions or waiving late fees in certain situations. Regular support: Working in collections can be complex. Provide resources and support to help your team manage stress and maintain a positive attitude. 7. Monitor and adjust your strategy A successful credit union collection strategy is dynamic. Regularly monitor its performance and adjust as needed. Key performance indicators (KPIs): Track KPIs such as delinquency rates, recovery rates, roll-rates and member satisfaction to gauge the effectiveness of your strategy. Member feedback: Survey members who have gone through the collections process. Their insights can help you identify areas for improvement. Continuous improvement: Use data and feedback to continuously refine your strategy. What worked last year may not be as effective today, so staying adaptable is key. Creating an effective credit union collections strategy requires a balance of empathy, effective communication, and compliance. By understanding your members, communicating clearly, offering flexible solutions, leveraging technology, and continuously improving your approach, you can develop a strategy that not only reduces delinquency but also strengthens member relationships. In today’s fiercely competitive landscape, where efficiency and efficacy stand paramount, working with the right partner equipped with innovative credit union solutions can dramatically transform your outcomes. Choosing us for your debt collection needs signifies an investment in premier analytics, advanced debt recovery tools, and unmatched support.  Learn more Watch credit union collection chat This article includes content created by an AI language model and is intended to provide general information.

Fraud-as-a-Service (FaaS) represents an emerging and increasingly sophisticated business model within cybercrime. In this model, malicious actors commercialize their expertise, tools, and infrastructure, enabling others to perpetrate fraud more easily and efficiently. These FaaS offerings are often accessible via dark web marketplaces or underground forums, streamlining and automating fraud processes, such as large-scale phishing campaigns. This enables the creation of convincing counterfeit websites and the distribution of bulk emails, allowing cybercriminals to harvest credentials and personal information en masse.   Organized cybercrime syndicates leverage account creation bots to establish hundreds of fraudulent accounts across various platforms, bypassing standard security protocols and scaling their illicit activities seamlessly. A fraudster no longer requires deep technical skills or detailed knowledge of complex verification techniques, such as liveness detection. Instead, they can acquire turnkey FaaS solutions that, for instance, inject pre-recorded video footage to spoof verification processes, enabling the rapid creation of thousands of fraudulent accounts.  The commoditization of fraud has effectively democratized it, lowering the barriers to entry. Previously accessible to a select few, FaaS has developed sophisticated techniques and is now available to a broader and less technically adept audience. Now, even individuals with basic computer skills can access these services and initiate fraudulent schemes with minimal effort.   Key tools in the FaaS arsenal  Central to the success of fraud-as-a-service is the ability to create fraudulent accounts while evading detection. This process can be alarmingly straightforward, even for companies adhering to industry-recognized best practices. Widely available programs, such as app cloners, enable fraudsters to generate multiple instances of the same application on a single device, modifying its source code to bypass security measures to detect such activities. The generalization of artifical intelligence (AI) and increased access to technology have provided cybercriminals with new tools to launch sophisticated scams, such as Pig Butchering and Authorized Push Payment (APP) scams.   Similarly, image injection tools facilitate the insertion of manipulated images to deceive verification systems, while emulators simulate legitimate device activity at scale, making detection more challenging. Techniques such as location spoofing allow fraudsters to alter the perceived geographical location of a device, thereby evading location-based security checks and allowing their scams to remain undetected.  Once fraudulent accounts are established, cybercriminals focus on monetizing their efforts. Industries like food delivery and ride-hailing are particularly vulnerable to promotional abuse. Fraudsters exploit promotional offers intended for new customers by using cloned apps, injected images, and emulators to create multiple fake accounts, redeem discounts, and resell them for profit. AI-driven automation and advanced communication technologies lower the barriers for these scams, enabling criminals to operate at a larger scale and with greater efficiency. This has made scams more pervasive and difficult for individuals and institutions to detect.  In the ride-hailing industry, these tactics are used to manipulate fare structures and incentives. Fraudsters operate multiple driver or rider accounts on the same device to earn referral bonuses and other promotional rewards. Emulators can simulate rides with fabricated start and end points, while location spoofing tools manipulate GPS data, inflating fares, and earnings. Such fraudulent activities result in significant financial losses for companies and degrade service quality for legitimate users, as resources are diverted from genuine transactions and logistical algorithms are disrupted.  The implications of FaaS for businesses  The commercialization of fraud poses a substantial threat to businesses, not only by democratizing fraud but also by enabling it to rapidly scale. . Fraudsters can experiment with multiple schemes simultaneously, sharing feedback and accelerating their learning curve. A single tool developed by one individual can be deployed by numerous bad actors to perpetrate fraud on a large scale, with remarkable speed. This ease of execution allows fraudsters to overwhelm companies with a barrage of attacks, maximizing their financial gains while exacerbating the challenges of fraud prevention for targeted organizations.  Developing a FaaS-Resilient fraud prevention strategy  To effectively combat fraud-as-a-service, businesses must adopt AI fraud strategies that mirror the operational sophistication of fraudsters. These cybercriminals treat their activities as profitable enterprises, continually optimizing their return on investment through scalable and adaptable tactics. By deeply understanding the methodologies employed by fraudsters, companies can develop more effective fraud prevention measures that disrupt fraudulent operations without inconveniencing legitimate users.  Proactive fraud prevention strategies are essential in countering FaaS tactics. Effective measures rely on robust data collection and analysis. Regular reviews of key performance indicators (KPIs) and velocity checks, which monitor the rate at which users complete transactions, can help identify irregular behaviors.  Passive signals, such as device fingerprinting and location intelligence, are also invaluable in detecting suspicious activities. By scrutinizing data related to app tampering or device emulation, businesses can more accurately determine whether a genuine user is accessing their platform or if a fraudster is attempting to bypass detection.  Given the dynamic nature of FaaS, adaptation is crucial. Fraud prevention strategies must evolve continually to keep pace with emerging threats. Advanced technologies offer nuanced insights into user behavior, enabling businesses to identify and thwart fraud attempts with greater precision. Moreover, cutting-edge risk monitoring tools can help avoid false positives, ensuring that legitimate users are not unduly impacted.  As fraudsters persist in innovating and refining their tactics, organizations must remain vigilant, stay informed about emerging trends, invest in advanced fraud prevention and detection technologies, and cultivate a culture of security and awareness. While it may be tempting to underestimate fraudsters due to the illicit nature of their activities, it is important to recognize that many approach their work with a level of professionalism comparable to legitimate businesses. Understanding this reality offers valuable insights into how companies can effectively counteract fraud and protect their monetary interests.  Learn more This article includes content created by an AI language model and is intended to provide general information.

In this article...Rise of AI in fraudulent activitiesFighting AI with AI Addressing fraud threatsBenefits of leveraging AI fraud detectionFinancial services use caseExperian's AI fraud detection solutions In a world where technology evolves at lightning speed, fraudsters are becoming more sophisticated in their methods, leveraging advancements in artificial intelligence (AI). According to our 2024 U.S. Identity and Fraud Report, 70% of businesses expect AI fraud to be their second-greatest challenge over the next two to three years. To combat emerging fraud threats, organizations are turning to AI fraud detection to stay ahead and protect their businesses and their customers, essentially fighting AI with AI. This blog post explores the evolving AI fraud and AI fraud detection landscape. The rise of AI in fraudulent activities Technology is a double-edged sword. While it brings numerous advancements, it also provides fraudsters with new tools to exploit. AI is no exception. Here are some ways fraudsters are utilizing AI: Automated attacks: Fraudsters employ AI to design automated scripts that launch large-scale attacks on systems. These scripts can perform credential stuffing, where stolen usernames and passwords are automatically tested across multiple sites to gain unauthorized access. Deepfakes and synthetic identities: Deepfake technology and the creation of synthetic identities are becoming more prevalent, as we predicted in our 2024 Future of Fraud Forecast. Fraudsters use AI to manipulate videos and audio, making it possible to impersonate individuals convincingly. Similarly, synthetic identities blend real and fake information to create false personas. Phishing and social engineering: AI-driven phishing attacks are more personalized and convincing than traditional methods. By analyzing social media profiles and other online data, fraudsters craft tailored messages that trick individuals into revealing sensitive information. Watch now: Our 2024 Future of Fraud Forecast: Gen AI and Emerging Trends webinar explores five of our fraud predictions for the year. Fighting AI with AI in fraud detection To combat these sophisticated threats, businesses must adopt equally advanced measures. AI fraud detection offers a robust solution: Machine learning algorithms: Fraud detection machine learning algorithms analyze vast datasets to identify patterns and anomalies that indicate fraudulent behavior. These algorithms can continuously learn and adapt, improving their accuracy over time. Real-time monitoring: AI systems provide real-time monitoring of transactions and activities. This allows businesses to detect and respond to fraud attempts instantly, minimizing potential damage. Predictive analytics: Predictive analytics uses historical data to forecast future fraud trends. By anticipating potential threats, organizations can take proactive measures to safeguard their assets. Addressing fraud threats with AI fraud detection AI's versatility allows it to tackle various types of fraud effectively: Identity theft: 84% of consumers rank identity theft as their top online concern.* AI systems can help safeguard consumers by cross-referencing multiple data points to verify identities. They can spot inconsistencies that indicate identity theft, such as mismatched addresses or unusual login locations. Payment fraud: Coming in second to identity theft, 80% of consumers rank stolen credit card information as their top online concern.* Payment fraud includes unauthorized credit card transactions and chargebacks. AI can be used in payment fraud detection to surface unusual spending patterns and flag suspicious transactions for further investigation. Account takeover: Account takeover fraud, the topmost encountered fraud event reported by U.S. businesses in 2023, occurs when fraudsters gain access to user accounts and conduct unauthorized activities.* AI identifies unusual login behaviors and implements additional security measures to prevent account breaches. Synthetic identity fraud: Synthetic identity fraud involves the creation of fake identities using real and fabricated information. Notably, retail banks cite synthetic identity fraud as the operational challenge putting the most stress on their business.* AI fraud solutions detect these false identities by analyzing data inconsistencies and behavioral patterns. Benefits of leveraging AI fraud detection Implementing AI fraud detection offers numerous advantages: Enhanced accuracy: AI systems are highly accurate in identifying fraudulent activities. Their ability to analyze large datasets and detect subtle anomalies surpasses traditional methods. Cost savings: By preventing fraud losses, AI systems save businesses significant amounts of money. They also reduce the need for manual investigations, freeing up resources for other tasks. Improved customer experience: AI fraud detection minimizes false positives, ensuring genuine customers face minimal friction. This enhances the overall customer experience and builds trust in the organization. Scalability: AI systems can handle large volumes of data, making them suitable for organizations of all sizes. Whether you're a small business or a large enterprise, AI can scale to meet your needs. Financial services use case The financial sector is particularly vulnerable to fraud, making AI an invaluable tool for fraud detection in banking. Protecting transactions: Banks use AI to monitor transactions for signs of fraud. Machine learning algorithms analyze transaction data in real time, flagging suspicious activities for further review. Enhancing security: AI enhances security by implementing multifactor authentication and behavioral analytics. These measures make it more challenging for fraudsters to gain unauthorized access. Reducing fraud losses: By detecting and preventing fraudulent activities, AI helps banks reduce their fraud losses throughout the customer lifecycle. This not only saves money but also protects the institution's reputation. Experian's AI fraud detection solutions AI fraud detection is revolutionizing the way organizations combat fraud. Its ability to analyze vast amounts of data, detect anomalies, and adapt to new threats makes it an essential element of any comprehensive fraud strategy. Experian’s range of AI fraud detection solutions help organizations enhance their security measures, reduce fraud losses, authenticate identity with confidence, and improve the overall customer experience. If you're interested in learning more about how AI can protect your business, explore our fraud management solutions or contact us today. Learn More *Source: Experian. 2024 U.S. Identity and Fraud Report. This article includes content created by an AI language model and is intended to provide general information. 

Financial institutions are constantly searching for ways to engage their consumers while providing valuable services that keep them financially sound and satisfied. At the same time, consumers are looking for ways to limit their risk and grow their financial power while improving and protecting their financial health. Both can be accomplished through personalized financial experiences.

The world of finance can be a dangerous place, where cunning schemes lurk in the shadows, ready to pounce on unsuspecting victims. In the ever-evolving landscape of financial crime, the insidious ‘pig butchering’ scam has emerged as a significant threat, targeting both financial institutions and their clients. What is a ‘pig butchering’ scam? ‘Pig butchering’ scams are named after the practice of farmers fattening up their livestock before “butchering” them. This comparison describes the core of ‘pig butchering’ scams, where criminals entice victims to participate in investment schemes and cryptocurrency fraud. Originating in Southeast Asia and now rampant in the United States, these scams often start with online interactions via social media or dating applications. Scammers build trust with the victim, eventually gaining access to their online accounts. They "fatten the pig" by enticing more cryptocurrency investments and then make off with their ill-gotten gains. The repercussions are staggering, with reported losses exceeding $3.5 billion in 2023 alone according to an AP News article, and around 40,000 victims in the United States, including cases of losses as massive as $4 million. The real-life impact The story of “RB,” a San Francisco man who engaged with a scammer named "Janey Lee," serves as a stark warning. Through social media, Janey orchestrated an elaborate scheme, promising "RB” substantial returns in cryptocurrency investment. Seduced by false promises, “RB” emptied his life savings into the scam, only to be rescued by a Federal Bureau of Investigation (FBI) intervention, narrowly avoiding financial ruin.1 Malicious actors are improving their targeting skills, and often pursue executives and victims with a large sum of money, such as C-level officials from financial institutions. This past February, a $50 million pig slaughtering fraud incident caused the CEO of a local bank in Kansas to lose all his funds and the bank to collapse a few months later. FinCEN's vigilance and updates The Financial Crimes Enforcement Network (FinCEN) remains vigilant, issuing advisories to financial institutions to combat ‘pig butchering’ scams. Their latest advisory highlights evolving scam tactics, including aggressive promotions, using money mules for illegal fund transfers, and leveraging new financial products like decentralized finance (DeFi) platforms to obfuscate transactions. FinCEN also warns about red flags such as large and sudden investments from older customers, quick fund withdrawals after big deposits, and the frequent use of coins or mixers that hide transactions. Financial institutions are encouraged to: Report any suspicious activities by using specific terms like "pig butchering fraud advisory" in their reports to make analysis and response easier. File suspicious activity reports (SARs) using the key term “FIN-2023-PIGBUTCHERING.” Guide potential victims to report to the FBI’s IC3 or the Security and Exchange Commission (SEC’s) reporting system. A call to action for financial institutions The fight against ‘pig butchering’ scams requires proactive measures from financial institutions: Enhance fraud detection and anti-money laundering (AML) programs: Implement robust systems compliant with regulatory guidelines, conduct thorough customer enhanced due diligence, and leverage fraud detection software to spot anomalies and red flags., and leverage fraud detection software to spot anomalies and red flags. Leverage data analytics: Utilize data analytics tools to monitor customer behavior, identify irregular patterns, and swiftly detect potential ‘pig butchering’ activities. Employee training: Educate employees on scam risks, fraud detection techniques, and FinCEN red flags to empower them as the first line of defense., and FinCEN red flags to empower them as the first line of defense. Community education: Educate customers on recognizing and avoiding investment scams, promoting awareness, and safeguarding their assets. Navigating challenges with effective solutions ‘Pig butchering’ scams cause not only money losses but also personal troubles and reputational harm. Awareness, learning, and cooperation are essential in protecting from these complex financial fraudsters, securing the safety and confidence of your institutions and stakeholders. By combining the best data with our automated identification verification processes, you can protect your business and onboard new talents efficiently. Our industry-leading solutions employ device recognition, behavioral biometrics, machine learning, and global fraud databases to spot and block suspicious activity before it becomes a problem. Learn more 1San Francisco Chronical (2023). Crypto Texting Scam *This article includes content created by an AI language model and is intended to provide general information.

This article was updated on March 6, 2024. Advances in analytics and modeling are making credit risk decisioning more efficient and precise. And while businesses may face challenges in developing and deploying new credit risk models, machine learning (ML) — a type of artificial intelligence (AI) — is paving the way for shorter design cycles and greater performance lifts. LEARN MORE: Get personalized recommendations on optimizing your decisioning strategy Limitations of traditional lending models Traditional lending models have worked well for years, and many financial institutions continue to rely on legacy models and develop new challenger models the old-fashioned way. This approach has benefits, including the ability to rely on existing internal expertise and the explainability of the models. However, there are limitations as well. Slow reaction times:  Building and deploying a traditional credit risk model can take many months. That might be okay during relatively stable economic conditions, but these models may start to underperform if there's a sudden shift in consumer behavior or a world event that impacts people's finances. Fewer data sources:  Traditional scoring models may be able to analyze some types of FCRA-regulated data (also called alternative credit data*), such as utility or rent payments, that appear in credit reports. Custom credit risk scores and models could go a step further by incorporating data from additional sources, such as internal data, even if they're designed in a traditional way. But AI-driven models can analyze vast amounts of information and uncover data points that are more highly predictive of risk. Less effective performance:  Experian has found that applying machine learning models can increase accuracy and effectiveness, allowing lenders to make better decisions. When applied to credit decisioning, lenders see a Gini uplift of 60 to 70 percent compared to a traditional credit risk model.1 Leveraging machine learning-driven models to segment your universe From initial segmentation to sending right-sized offers, detecting fraud and managing collection efforts, organizations are already using machine learning throughout the customer life cycle. In fact, 79% are prioritizing the adoption of advanced analytics with AI and ML capabilities, while 65% believe that AI and ML provide their organization with a competitive advantage.2 While machine learning approaches to modeling aren't new, advances in computer science and computing power are unlocking new possibilities.3 Machine learning models can now quickly incorporate your internal data, alternative data, credit bureau data, credit attributes and other scores to give you a more accurate view of a consumer's creditworthiness. By more precisely scoring applicants, you can shrink the population in the middle of your score range, the segment of medium-risk applicants that are difficult to evaluate. You can then lower your high-end cutoff and raise your low-end cutoff, which may allow you to more confidently swap in  good accounts (the applicants you turned down with other models that would have been good) and swap out bad accounts (those you would have approved who turned bad). Machine learning models may also be able to use additional types of data to score applicants who don't qualify for a score  from traditional models. These applicants aren't necessarily riskier — there simply hasn't been a good way to understand the risk they present. Once you can make an accurate assessment, you can increase your lending universe by including this segment of previously "unscorable" consumers, which can drive revenue growth without additional risk. At the same time, you're helping expand financial inclusion to segments of the population that may otherwise struggle to access credit. READ MORE: Is Financial Inclusion Fueling Business Growth for Lenders? Connecting the model to a decision Even a machine learning model doesn't make decisions.4 The model estimates the creditworthiness of an applicant so lenders can make better-informed decisions. AI-driven credit decisioning software can take your parameters (such cutoff points) and the model's outputs to automatically approve or deny more applicants. Models that can more accurately segment and score populations will result in fewer applications going to manual review, which can save you money and improve your customers' experiences. CASE STUDY:  Atlas Credit, a small-dollar lender, nearly doubled its loan approval rates while decreasing risk losses by up to 20 percent using a machine learning-powered model and increased automation. Concerns around explainability One of the primary concerns lenders have about machine learning models come from so-called “black box" models.5 Although these models may offer large lifts, you can't verify how they work internally. As a result, lenders can't explain why decisions are made to regulators or consumers — effectively making them unusable. While it's a valid concern, there are machine learning models that don't use a black box approach. The machine learning model doesn't build itself and it's not really “learning" on its own — that's where the black box would come in. Instead, developers can use machine learning techniques to create more efficient models that are explainable, don't have a disparate impact on protected classes and can generate reason codes that help consumers understand the outcomes. LEARN MORE: Explainability: Machine learning and artificial intelligence in credit decisioning Building and using machine learning models Organizations may lack the expertise and IT infrastructure required to develop or deploy machine learning models. But similar to how digital transformations in other parts of the business are leading companies to use outside cloud-based solutions, there are options that don't require in-house data scientists and developers. Experian's expert-guided options can help you create, test and use machine learning models and AI-driven automated decisioning; Ascend Intelligence Services™ Acquire:  Our model development service allows you to prebuild and test the performance of a new model before Experian data scientists complete the model. It's collaborative, and you can upload internal data through the web portal and make comments or suggestions. The service periodically retrains your model to increase its effectiveness. Ascend Intelligence Services™ Pulse:  Monitor, validate and challenge your existing models to ensure you're not missing out on potential improvements. The service includes a model health index and alerts, performance summary, automatic validations and stress-testing results. It can also automatically build challenger models and share the estimated lift and financial benefit of deployment. PowerCurve® Originations Essentials:  Cloud-based decision engine software that you can use to make automated decisions that are tailored to your goals and needs. A machine learning approach to credit risk and AI-driven decisioning can help improve outcomes for borrowers and increase financial inclusion while reducing your overall costs. With a trusted and experienced partner, you'll also be able to back up your decisions with customizable and regulatorily-compliant reports. Learn more about our credit decisioning solutions. Learn more When we refer to "Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions as regulated by the Fair Credit Reporting Act (FCRA). Hence, the term "Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.1Experian (2024). Improving Your Credit Risk Machine Learning Model Deployment2Experian and Forrester Research (2023). Raising the AI Bar3Experian (2022). Driving Growth During Economic Uncertainty with AI/ML Strategies4Ibid5Experian (2020). Explainability ML and AI in Credit Decisioning

This article was updated on March 4, 2024. If you steal an identity to commit fraud, your success is determined by how long it takes the victim to find out. That window gets shorter as businesses get better at knowing when and how to reach an identity owner when fraud is suspected. In response, frustrated fraudsters have been developing techniques to commit fraud that does not involve a real identity, giving them a longer run-time and a bigger payday.  That's the idea behind  synthetic identity (SID) fraud — one of the fastest-growing types of fraud.  Defining synthetic identity fraud Organizations tend to have different  definitions of synthetic identity fraud, as a synthetic identity will look different to the businesses it attacks. Some may see a new account that goes bad immediately, while others might see a longer tenured account fall delinquent and default. The qualifications of the synthetic identity also change over time, as the fraudster works to increase the identity’s appearance of legitimacy. In the end, there is no person to confirm that fraud has occurred, in the very best case, identifying a synthetic identity is inferred and verified. As a result, inconsistent reporting and categorization can make tracking and fighting SID fraud more difficult.  To help create a more unified understanding and response to the issue, the Federal Reserve and 12 fraud experts worked together to develop a definition. In 2021, the  Boston Federal Reserve  published the result, “Synthetic identity fraud is the use of a combination of personally identifiable information to fabricate a person or entity to commit a dishonest act for personal or financial gain."1 To break down the definition, personally identifiable information (PII) can include:  Primary PII:  Such as a name, date of birth (DOB), Social Security number (SSN) or another government-issued identifier. When combined, these are generally unique to a person or entity. Secondary PII:  Such as an address, email, phone number or device ID. These elements can help verify a person or entity's identity.   Synthetic identities are created when fraudsters establish an identity from scratch using fake PII. Or they may combine real and fake PII (I.e., a stolen SSN with a fake name and DOB) to create a new identity. Additionally, fraudsters might steal and use someone's SSN to create an identity - children, the  elderly  and incarcerated people are popular targets because they don't commonly use credit.4 But any losses would still be tied to the SID rather than the victim. Exploring the Impact of SID fraud The most immediate and obvious impact of SID fraud is the fraud losses. Criminals may create a synthetic identity and spend months  building up its credit profile, opening accounts and increasing credit limits. The identities and behaviors are constructed to look like legitimate borrowers, with some having a record of on-time payments. But once the fraudster decides to monetize the identity, they can apply for loans and max out credit cards before ‘busting out’ and disappearing with the money.  Aite-Novaric Group estimates that SID fraud losses totaled $1.8 billion in 2020 and will increase to $2.94 billion in 2024.2 However, organizations that do not identify SIDs may classify a default as a credit loss rather than a fraud loss.  By some estimates, synthetic identity fraud could account for up to 20 percent of loan and credit card charge-offs, meaning the annual charge-off losses in the U.S. could be closer to $11 billion.3 Additionally, organizations lose time and resources on collection efforts if they do not identify the SID fraud.  Those estimates are only for unsecured U.S. credit products. But fraudsters use synthetic identities to take out secured loans, including auto loans.   As part of schemes used to steal relief funds during the pandemic, criminals used synthetic identities to open demand deposit accounts to receive funds. These accounts can be used to launder money from other sources and commit peer-to-peer payment fraud. Deposit account holders are also a primary source of cross-marketing for some financial institutions. Criminals can take advantage of vulnerable onboarding processes for deposit accounts where there’s low risk to the institution and receive offers for lending products. Building a successful SID prevention strategy Having an effective SID prevention strategy is more crucial than ever for organizations. Aside from fraud losses, consumers listed identity theft as their top concern when conducting activities online. And while 92% of businesses have an identity verification strategy in place, 63% of consumers are "somewhat confident" or "not very confident" in businesses' ability to accurately identify them online. Read: Experian's 2023 Identity and Fraud Report Many traditional fraud models and identity verification methods are not designed to detect fake people. And even a step up to a phone call for verification isn't enough when the fraudster will be the one answering the phone. Criminals also quickly respond when organizations update their fraud detection methods by looking for less-protected targets. Fraudsters have even signed their SIDs up for social media accounts and apps with low verification hurdles to help their SIDs pass identity checks.5  Understand synthetic identity risks across the lifecycle  Synthetic Identities are dynamic. When lending criteria is tightened to synthetics from opening new accounts, they simply come back when they can qualify. If waiting brings a higher credit line, they’ll wait. It’s important to recognize that synthetic identity isn’t a new account or a portfolio management problem - it’s both.    Use analytics that are tailored to synthetic identity  Many of our customers in the financial services space have been trying to solve synthetic identity fraud with credit data. There’s a false sense of security when criteria is tightened and losses go down—but the losses that are being impacted tend to not be related to credit. A better approach to synthetic ID fraud leverages a larger pool of data to assess behaviors and data linkages that are not contained in traditional credit data.  You can then escalate suspicious accounts to require additional reviews, such as screening through the Social Security Administration's Electronic Consent Based SSN Verification (eCBSV) system or more stringent document verification.  Find a trusted partner  Experian's interconnected data and analytics platforms offer lenders turnkey identity and synthetic identity fraud solutions. In addition, lenders can take advantage of the risk management system and continuous monitoring to look for signs of SIDs and fraudulent activity, which is important for flagging accounts after opening. These tools can also help lenders identify and prevent other common forms of fraud, including account takeovers, e-commerce fraud, child identity theft fraud and elderly fraud. Learn more about our synthetic identity fraud solutions. Learn more 1Federal Reserve Bank (2021). Defining Synthetic Identity Fraud 2Aite Novarica (2022). Synthetic Identity Fraud: Solution Providers Shining Light into the Darkness 3Experian (2022). Preventing synthetic identity fraud 4The Federal Reserve (2022). Synthetic Identity Fraud: What Is it and Why You Should Care? 5Experian (2022). Preventing synthetic identity fraud 

Our Econ to Action podcast series dives into the top economic trends and the implications of those trends in the market. In each episode, we explore the challenges different market segments are facing and how businesses in the segment are navigating the current economic climate. Listen to our host, Josee Farmer, Economic Analyst, discuss these topics with other Experian experts. In a special episode of Econ to Action to commemorate the start of the new year, Josee is joined by three market experts to discuss the 2024 forecast. The experts discuss the broader U.S. economic forecast, according to the Federal Reserve’s SEP (Summary of Economic Projections), as well as the forecasts for the mortgage, collections and national bank market segments. Shawn Rife, Client Executive, returns to Econ to Action with more collections insights, along with new guests Kendall Hellman, Senior Account Executive, Strategic Sales and Rob Rollo, Senior Account Executive, Strategic Mortgage Sales. Watch our first video episode and learn how the 2024 forecast will affect the market. Be sure to go back and catch up on previous episodes on our Econ to Action podcast hub and visit Experian Edge for our latest economic, credit and market insights.  

This article was updated on February 13, 2024. Traditional credit data has long been a reliable source for measuring consumers' creditworthiness. While that's not changing, new types of alternative credit data are giving lenders a more complete picture of consumers' financial health. With supplemental data, lenders can better serve a wider variety of consumers and increase financial access and opportunities in their communities. What is alternative credit data? Alternative credit data, also known as expanded FCRA-regulated data, is data that can help you evaluate creditworthiness but isn't included in traditional credit reports.1 To comply with the Fair Credit Reporting Act (FCRA), alternative credit data must be displayable, disputable and correctable. Lenders are increasingly turning to new types and sources of data as the use of alternative credit data becomes the norm in underwriting. Today, lenders commonly use one or more of the following: Alternative financial services data: Alternative financial services (AFS) credit data can include information on consumers' use of small-dollar installment loans, single-payment loans, point-of-sale financing, auto title loans and rent-to-own agreements. Consumer permission data: With a consumer's permission, you can get transactional and account-level data from financial accounts to better assess income, assets and cash flow. The access can also give insight into payment history on non-traditional accounts, such as utilities, cell phone and streaming services. Rental payment history: Property managers, electronic rent payment services and rent collection companies can share information on consumers' rent payment history and lease terms. Full-file public records: Local- and state-level public records can tell you about a consumer's professional and occupational licenses, education, property deeds and address history. Buy Now Pay Later (BNPL) data: BNPL tradeline and account data can show you payment and return histories, along with upcoming scheduled payments. It may become even more important as consumers increasingly use this new type of point-of-sale financing. By gathering more information, you can get a deeper understanding of consumers' creditworthiness and expand your lending universe. From market segmentation to fraud prevention and collections, you can also use alternative credit data throughout the customer lifecycle. READ: 2023 State of Alternative Credit Data Report Challenges in underwriting today While unemployment rates are down, high inflation, rising interest rates and uncertainty about the economy are impacting consumer sentiment and the lending environment.2 Additionally, lenders may need to shift their underwriting approaches as pandemic-related assistance programs and loan accommodations end. Lenders may want to tighten their credit criteria. But, at the same time, consumers are becoming accustomed to streamlined application processes and responses. A slow manual review could lead to losing customers. Alternative credit data can help you more accurately assess consumers' creditworthiness, which may make it easier to identify high-risk applicants and find the hidden gems within medium-risk segments. Layering traditional and alternative credit data with the latest approaches to model building, such as using artificial intelligence, can also help you implement precise and predictive underwriting strategies. Benefits of using alternative data for credit underwriting Using alternative data for credit underwriting — along with custom credit attributes and automation — is the modern approach to a risk-based credit approval strategy. The result can offer: A greater view of consumer creditworthiness: Personal cash flow data and a consumer's history of making (or missing) payments that don't appear on traditional credit reports can give you a better understanding of their financial position. Improve speed and accuracy of credit decisions: The expanded view helps you create a more efficient underwriting process. Automated underwriting tools can incorporate alternative credit data and attributes with meaningful results. One lender, Atlas Credit, worked with Experian to create a custom model that incorporated alternative credit data and nearly doubled its approvals while reducing risk by 15 to 20 percent.3 Increase financial inclusion: There are 28 million American adults who don't have a mainstream credit file and 21 million who aren't scoreable by conventional scoring models.4 With alternative credit data, you may be able to more accurately assess the creditworthiness of adults who would otherwise be deemed thin file or unscorable. Broadening your pool of applications while appropriately managing risk is a measurable success. What Experian builds and offers Experian is continually expanding access to expanded FCRA-regulated data. Our Experian RentBureau and Clarity Services (the leading source of alternative financial credit data) have long given lenders a more complete picture of consumers' financial situation. Experian also helps lenders effectively use these new types of data. You can also incorporate the data into your proprietary marketing, lending and collections strategies. Experian is also using alternative credit data for credit scoring. The Lift Premium™ model can score 96 percent of U.S. adults — compared to the 81 percent that conventional models can score using traditional data.5 The bottom line Lenders have been testing and using alternative credit data for years, but its use in underwriting may become even more important as they need to respond to changing consumer expectations and economic uncertainty. Experian is supporting this innovation by expanding access to alternative data sources and helping lenders understand how to best use and implement alternative credit data in their lending strategies. Learn more 1When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA Data" may also apply and can be used interchangeably. 2Experian (2024). State of the Economy Report 3Experian (2020). OneAZ Credit Union [Case Study] 4Oliver Wyman (2022). Financial Inclusion and Access to Credit [White Paper] 5Ibid.

This article was updated on January 31, 2024. Debt. For many, it’s a struggle – and a constant one. In fact, total consumer debt balances have increased year-over-year.1 High inflation and fears of a recession aren't letting up either. Successful third-party debt collections can be achieved by investing in the right data and technologies. Overcoming debt collections challenges While third-party debt collectors may take a more specialized approach to collections, they face unique challenges. Debt collectors must find the debtor, get them to respond, collect payment, and stay compliant. With streamlined processes and enhanced strategies, lending institutions and collection agencies can recoup more costs.  Embrace automationAutomation, artificial intelligence, and machine learning are at the forefront of the continued digital transformation within the world of collections. When implemented well, automation can ease pressure on call center agents and improve the customer experience. Automated systems can also help increase recovery rates while minimizing the risk of human error and the corresponding liability. READ: Three Tips for Successful Automated Debt CollectionsMaximize digitalizationIntegrating and expanding digital technologies is mandatory to be successful in the third-party debt collections space. Third-party debt collectors must be at the forefront of adopting digital communication tools (i.e., email, text, chatbots, and banking apps), to connect more easily with debtors and provide a frictionless customer experience. A digital debt recovery solution helps third-party debt collectors streamline processes, maintain debt collection compliance, and maximize collections efforts. READ: The Ultimate Guide to Successful Debt Collection TechniquesLeverage the best data Consumer data is ever-changing, especially during times of economic distress. Capturing accurate consumer information through a combination of data sources — and continually evaluating the data’s validity — is key to reducing risk throughout the consumer life cycle. By gaining a fresher, more complete view of existing and potential customers, third-party debt collectors can better determine an individual’s propensity to pay and enhance their overall decisioning. Keep pace with changing regulations With increasing scrutiny on the financial services industry and ever-evolving consumer protection and privacy regulations, remaining compliant is a top priority for third-party debt collections departments and agencies. The increased focus on regulations and compliance has also brought to the surface the need for teams to include debt collectors with soft skills who can communicate effectively with indebted consumers. With the right processes and third-party debt collections tools, you can better develop a robust compliance management strategy that works to prevent reputational risk and minimize costly violations. Finding the right debt collections partner In today's climate, it's never been more important to build the right third-party debt collections strategies for your business. By creating a more effective, consumer-focused collections process, you can maximize your recovery efforts, make more profitable decisions and focus your resources where they’re needed most. Our third-party debt management solutions empower your organization to see the complete behavioral, demographic, and emerging view of customer portfolios through extensive data assets, debt collection predictive analytics innovative platforms. For more insights to strengthen your debt collection strategy, download our tip sheet. Access tip sheet

In today's fast-paced digital world, the risk of fraud across all industries is a constant threat. The traditional methods of fraud detection are no longer sufficient, as fraudsters become increasingly sophisticated in their attacks. However, with artificial intelligence (AI) and machine learning (ML) solutions, financial institutions can stay one step ahead of fraudsters. AI and machine learning-equipped fraud detection tools have the ability to identify suspicious activity and patterns of fraud that are imperceptible to the human brain. In this blog post, we’ll dive into the significance of AI and machine learning in fraud detection and how these solutions are uniquely equipped to handle the demands of modern-day risk management. Understanding artificial intelligence and machine learning AI and machine learning solutions are transformative technologies that are reshaping the landscape of many industries. AI, at its core, is a field of computer science that simulates human intelligence in machines, enabling them to learn from experience and perform tasks that normally require human intellect. Machine learning, a subset of AI, is the science of getting computers to learn and act like humans do, but with minimal human intervention. They can analyze vast amounts of data within seconds, identifying patterns and trends that would be impossible for a human to recognize. When it comes to fraud detection, this ability is invaluable.  Advantages of fraud detection using machine learning AI and machine learning have several benefits that make them valuable in fraud detection. One significant advantage is that these technologies can recognize patterns that are too complex for humans to identify. By running through a vast set of data points, these solutions can pinpoint anomalous behavior, and thereby prevent financial losses. AI analytics tools are adept at monitoring complex networks, detecting the dispersion of attacks that may involve multiple individuals and entities, and correlating activity patterns that would otherwise be hidden. Machine learning algorithms can take these patterns and turn them into mathematical models that help identify instances of fraud before the damage takes place. Secondly, they continuously learn from new data, which allows them to become more efficient in identifying fraud as they process more data. Thirdly, they automate fraud mitigation processes, which significantly reduces the need for manual interventions that may consume valuable time and resources. Another significant benefit of machine learning is its analytics capabilities, which allow organizations to gain valuable insights into customer behavior and fraud patterns. With AI analytics, they can detect and investigate fraudulent activities in real-time, and combine it with other tools to help detect and mitigate fraud risk. For example, in financial services, AI fraud detection can help banks and financial service providers detect and prevent fraud in their systems, add value to their services and improve customer satisfaction. The future of fraud detection and machine learning The rate at which technology is evolving means that machine learning and AI fraud detection will become increasingly important in the future. In the next few years, we can expect a more sophisticated level of fraud detection using unmanned machine systems, robotics process automation, and more. Ultimately, this will improve the efficiency and effectiveness of fraud detection. AI-based fraud management solutions are taking center stage. Organizations must leverage advanced machine learning and AI analytics solutions to prevent and mitigate cyber risks and comply with regulatory mandates. The benefits extend far beyond the financial bottom line to improving the safety and security of customers. AI and machine learning solutions offer accurate, efficient and proactive routes to managing the risk of fraud in an ever-changing environment. How can Experian® help Integrating machine learning for fraud detection represents a significant advancement in cybersecurity. Fraud management solutions detect, prevent and manage fraud across all industries, including financial services, healthcare and telecommunications. With the advancement of technology, fraud management solutions now integrate machine learning to improve their processes. Experian® provides fraud prevention solutions, including machine learning models and AI analytics, which can help more effectively mitigate fraud risk, streamline fraud investigations and create a more secure digital environment for all. With Experian’s AI analytics, risk mitigation tools and fraud management solutions, organizations can stay one step ahead of fraudsters and protect their brand reputation, customer trustworthiness and corporate data. Embracing these solutions can save organizations from significant losses, reputational damage and regulatory scrutiny. To learn more about how to future-proof your business and safeguard your customers from fraud, check out Experian’s robust suite of fraud prevention solutions. Want to hear what our industry experts think? Check out this on-demand webinar on artificial intelligence and machine learning strategies. Learn more Watch webinar *This article includes content created by an AI language model and is intended to provide general information.