"Grandma, it’s me, Mike.” Imagine hearing the voice of a loved one (or what sounds like it) informing you they were arrested and in need of bail money. Panicked, a desperate family member may follow instructions to withdraw a large sum of money to provide to a courier. Suspicious, they even make a video call to which they see a blurry image on the other end, but the same voice. When the fight or flight feeling settles, reality hits. Sadly, this is not the scenario of an upcoming Netflix movie. This is fraud – an example of a new grandparent scam/family emergency scam happening at scale across the U.S. While generative AI is driving efficiencies, personalization and improvements in multiple areas, it’s also a technology being adopted by fraudsters. Generative AI can be used to create highly personalized and convincing messages that are tailored to a specific victim. By analyzing publicly available social media profiles and other personal information, scammers can use generative AI to create fake accounts, emails, or phone calls that mimic the voice and mannerisms of a grandchild or family member in distress. The use of this technology can make it particularly difficult to distinguish between real and fake communication, leading to increased vulnerability and susceptibility to fraud. Furthermore, generative AI can also be used to create deepfake videos or audio recordings that show the supposed family member in distress or reinforce the scammer's story. These deepfakes can be incredibly realistic, making it even harder for victims to identify fraudulent activity. What is Generative AI? Generative artificial intelligence (GenAI) describes algorithms that can be used to create new content, including audio, code, images, text, simulations, and videos. Generative AI has the potential to revolutionize many industries by creating new and innovative content, but it also presents a significant risk for financial institutions. Cyber attackers can use generative AI to produce sophisticated malware, phishing schemes, and other fraudulent activities that can cause data breaches, financial losses, and reputational damage. This poses a challenge for financial organizations, as human error remains one of the weakest links in cybersecurity. Fraudsters capitalizing on emotions such as fear, stress, desperation, or inattention can make it difficult to protect against malicious content generated by generative AI, which could be used as a tactic to defraud financial institutions. Four types of Generative AI used for Fraud: Fraud automation at scale Fraudulent activities often involve multiple steps which can be complex and time-consuming. However, GenAI may enable fraudsters to automate each of these steps, thereby establishing a comprehensive framework for fraudulent attacks. The modus operandi of GenAI involves the generation of scripts or code that facilitates the creation of programs capable of autonomously pilfering personal data and breaching accounts. Previously, the development of such codes and programs necessitated the expertise of seasoned programmers, with each stage of the process requiring separate and fragmented development. Nevertheless, with the advent of GenAI, any fraudster can now access an all-encompassing program without the need for specialized knowledge, amplifying the inherent danger it poses. It can be used to accelerate fraudsters techniques such as credential stuffing, card testing and brute force attacks. Text content generation In the past, one could often rely on spotting typos or errors as a means of detecting such fraudulent schemes. However, the emergence of GenAI has introduced a new challenge, as it generates impeccably written scripts that possess an uncanny authenticity, rendering the identification of deceit activities considerably more difficult. But now, GenAI can produce realistic text that sounds as if it were from a familiar person, organization, or business by simply feeding GenAI prompts or content to replicate. Furthermore, the utilization of innovative Language Learning Model (LLM) tools enables scammers to engage in text-based conversations with multiple victims, skillfully manipulating them into carrying out actions that ultimately serve the perpetrators' interests. Image and video manipulation In a matter of seconds, fraudsters, regardless of their level of expertise, are now capable of producing highly authentic videos or images powered by GenAI. This innovative technology leverages deep learning techniques, using vast amounts of collected datasets to train artificial intelligence models. Once these models are trained, they possess the ability to generate visuals that closely resemble the desired target. By seamlessly blending or superimposing these generated images onto specific frames, the original content can be replaced with manipulated visuals. Furthermore, the utilization of AI text-to-image generators, powered by artificial neural networks, allows fraudsters to input prompts in the form of words. These prompts are then processed by the system, resulting in the generation of corresponding images, further enhancing the deceptive capabilities at their disposal. Human voice generation The emergence of AI-generated voices that mimic real people has created new vulnerabilities in voice verification systems. Firms that rely heavily on these systems, such as investment firms, must take extra precautions to ensure the security of their clients' assets. Criminals can also use AI chatbots to build relationships with victims and exploit their emotions to convince them to invest money or share personal information. Pig butchering scams and romance scams are examples of these types of frauds where AI chatbots can be highly effective, as they are friendly, convincing, and can easily follow a script. In particular, synthetic identity fraud has become an increasingly common tactic among cybercriminals. By creating fake personas with plausible social profiles, hackers can avoid detection while conducting financial crimes. It is essential for organizations to remain vigilant and verify the identities of any new contacts or suppliers before engaging with them. Failure to do so could result in significant monetary loss and reputational damage. Leverage AI to fight bad actors In today's digital landscape, businesses face increased fraud risks from advanced chatbots and generative technology. To combat this, businesses must use the same weapons than criminals, and train AI-based tools to detect and prevent fraudulent activities. Fraud prediction: Generative AI can analyze historical data to predict future fraudulent activities. By analyzing patterns in data and identifying potential risk factors, generative AI can help fraud examiners anticipate and prevent fraudulent behavior. Machine learning algorithms can analyze patterns in data to identify suspicious behavior and flag it for further investigation. Fraud Investigation: In addition to preventing fraud, generative AI can assist fraud examiners in investigating suspicious activities by generating scenarios and identifying potential suspects. By analyzing email communications and social media activity, generative AI can uncover hidden connections between suspects and identify potential fraudsters. To confirm the authenticity of users, financial institutions should adopt sophisticated identity verification methods that include liveness detection algorithms and document-centric identity proofing, and predictive analytics models. These measures can help prevent bots from infiltrating their systems and spreading disinformation, while also protecting against scams and cyberattacks. In conclusion, financial institutions must stay vigilant and deploy new tools and technologies to protect against the evolving threat landscape. By adopting advanced identity verification solutions, organizations can safeguard themselves and their customers from potential risks. To learn more about how Experian can help you leverage fraud prevention solutions, visit us online or request a call

While the principle of “trust but verify” might work for personal relationships, “verifying before trusting” is a more appropriate approach for businesses. According to Experian’s 2024 U.S. Identity and Fraud Report, consumers ranked identity theft as their top online security concern. As consumers conduct more activities online, the use of digital identity verification methods is becoming increasingly important. In this article, we explore how a streamlined initial verification process and continual authentication can help you build consumer trust and loyalty, as well as protect your business.  What is identity verification?  Online identity verification is the process of digitally confirming the identity of a user. Whether you’re reviewing an account application or approving an online transaction, you need to know that the person you’re dealing with is who they claim to be.  Technology can help bring traditional identification verification methods online, such as checking a photo ID. Additionally, people and organizations have more digital “fingerprints” than ever before, which digital identity solutions can use to authenticate users with increased accuracy and less friction.  What do online identity verification methods help solve?  A well-designed and implemented online identity verification process can help address fraud, compliance and customer demands all at once. Verifying someone’s identity when they first create an account could be an important part of the know your customer (KYC) and customer identification program (CIP) requirements. From that moment on, continuous authentication can help detect and prevent fraud.  Balancing the need for identity verification with a smooth online experience can be challenging. Customers may abandon a cart if identification requirements aren’t easy and fast, and may look for new services altogether if they’re repeatedly asked to authenticate themselves. But the challenge also presents an opportunity for companies that can leverage online identity verification services and methods to verify users’ identities accurately and discreetly.  Examples of online identity verification methods  There are multiple ways to verify someone’s identity, but some of the most popular online identity verification methods include:  Personally identifiable information. Including their name, address, email address and phone number that can be checked against existing databases.  Mobile network operator data. A service that verifies a person’s mobile phone identity. For instance, this can help verify the name, address, device details and other information associated with a phone number.  Document verification. There are services that ask consumers to snap and upload a picture of the required document, like a driver’s license, passport, visa or national ID card. These may be verified with 2D or 3D facial recognition with liveness detection (e.g., verifying the user is human) or validating whether the document is real by verifying things like magnetic ink, the machine-readable zone and the barcode are genuine. One-time passwords. A one-time password is sent to a user’s phone or email during an application process to verify that they can access the account or device. Multifactor authentication. A service for existing users who can verify their identity with a combination of different factors, such as a password or biometrics (a method that measures unique physiological characteristics using fingerprints and face recognition). Knowledge-based association questions. These are questions that users answer to verify their identity. The questions may be based on their previous answers to “secret questions” or information from a credit bureau. Behavioral analysis. A service that verifies identity by comparing how a user interacts with a website or app to their previous behavior or an average user’s behavior. Environmental attributes, such as time and location, may also be considered. This technique requires no effort from the consumer. To keep up with increasing consumer and business demand, online identity verification processes may use artificial intelligence and machine learning techniques to complement the digital and manual processes. Some methods, such as consistency checks on a device and behavioral biometric assessments, can also help offer an “invisible” approach to verification. Even small behavioral traits, such as a user’s scrolling style or finger pressure, could be important data points. These invisible methods may be welcomed as a low-friction approach by consumers, who are increasingly aware of the lack of security that comes from only using passwords as an identity verification method. In Experian’s 2024 U.S. Identity and Fraud Report, 71 percent of consumers said physical biometrics are most important for a better online experience, followed by PIN codes sent to a mobile device (70 percent) and behavioral biometrics (66 percent). How Experian can help Experian is a global leader in identity verification and fraud detection services. We offer a layered approach that draws on different verification methods, including credit, device, non-traditional and user-provided data. Step-up authentication can add additional verification requirements based on how risky a user appears or the action they’re trying to take. The approach gives your trusted users a lower-friction experience while helping you detect multiple types of fraud and address CIP discrepancies. At the same time, your customers are assigned a unique and persistent identity, which can give you a single, consolidated view of your customers based on data from different platforms. Using these insights from identity resolution, you can deliver a personalized experience that surprises and delights. Learn more about Experian’s identity verification solutions and Experian VerifyTM. Learn more

This article was originally published on multifamilyinsiders.com One of the challenges currently facing the rental housing industry is the amount of lease application fraud. An Entrata study found a 111% increase in lease application fraud between 2019 and 2020. In the same study, 55% of surveyed apartment managers and rental operators said their properties experience fraudulent lease application attempts every few months, and 15% said their communities were subjected to multiple attempts each month. One-third of respondents described themselves as "very concerned" about application fraud. Just as alarming as the rise in attempts is the apparent likelihood of success. In the study, 65% of apartment managers said they are not confident in their current fraud prevention efforts. Some applicants can use a range of tools to commit fraud such as fake pay stubs, bank statements, employment records, and other falsified documents. Unfortunately, readily available computer technology makes it all too easy for applicants to produce these falsified documents. Tools to fight against fraud Apartment communities that rely on an overly manual screening process may find themselves at a disadvantage in the current landscape. Relying on associates to manually verify things like income and employment history can increase the risk of a deceitful applicant being successful. In addition, these processes can be extraordinarily time-consuming, which means leasing associates have less bandwidth for their many other important duties and responsibilities. Not to mention, the units stay unoccupied while these time-consuming verifications are being done manually. Among the general screening technologies that operators should consider: Automated verification of income, assets and employment — These solutions eliminate the need for operators to collect this kind of documentation from applicants. Furthermore, it eliminates the opportunity for applicants to supply falsified supporting documentation. Frictionless authentication — A multi-layered identity verification process for those applying for rental housing, frictionless authentication detects the subtle and not-so-subtle signs that an applicant is, to one degree or another, using a false identity. By highlighting discrepancies, the process assigns a “score” to quantify the likelihood that misrepresentation is taking place. Additional confirmation of the applicant’s identity can be completed using a one-time passcode (OTP) or knowledge-based authentication (KBA). This technology also uses device intelligence to recognize the risks associated with the physical devices (such as computers, tablets, and smartphones) that consumers use for online applications to identify potential imposters. In today's landscape, apartment owners and operators need to make sure they're protecting themselves against fraudulent applicants, who may not fulfill their financial obligations as outlined in their leases. By embracing the ever-growing array of advanced screening tools and technologies, owners and operators can achieve that protection and reduce their risk significantly — and save their associates time and energy.

Investing in a strong customer acquisition strategy is critical to attracting leads and converting them into high-value customers. In this blog post, we’ll be focusing on one of the first stages of the customer acquisition process: the application stage. Challenges with online customer application processes When it comes to the customer application stage, speed, ease, and convenience are no longer nice-to-haves — they are musts. But various challenges exist for lenders and consumers in terms of online credit or account application processes, including: Limited digital capabilities. Consumers have grown more reliant on digital channels, with 52% preferring to use digital banking options over banking at branches. That said, financial institutions should prioritize the digital customer experience or risk falling behind the competition. The length of applications. Whether it’s a physical or digital application, requiring consumers to provide a substantial amount of information about themselves and their past can be frustrating. In fact, 67% of consumers will abandon an application if they experience complications. Potential human error. Because longer, drawn-out applications require various steps and data inputs, consumers may leave fields blank or make errors along the way. This can create more friction and delays as consumers may potentially be driven offline and into branches to get their applications sorted out. Improve the speed and accuracy of online credit applications Given that consumers are more likely to abandon their applications if their experience is friction-filled, financial institutions will need an automated, data-driven solution to simplify and streamline the online form completion process. Some of the benefits of leveraging an automated solution include: Improved customer experiences. Shortening time-to-value starts with faster decisioning. By using accurate consumer data and automation to prefill parts of the online credit application, you can reduce the amount of information applicants are required to enter, leading to lower abandonment rates, less potential for manual error, and enhanced user experiences. Fraud prevention. Safeguarding consumer information throughout the credit application process is crucial. By leveraging intelligent identity verification solutions, you can securely and compliantly identify consumer identities while ensuring data isn’t released in risky situations. Then by using identity management solutions, you can gain a connected, validated customer view, resulting in minimized end-user friction. Faster approvals. With automated data prefill and identity verification, you can process applications more efficiently, leading to faster approvals and increased conversions. Choosing the right partner Experian can help optimize your customer application process, making it faster, more efficient, and less error prone. This way, you can win more customers and improve digital experiences. Learn more about Experian’s customer acquisition solutions.

Financial institutions have long been on the cutting edge of technology trends, and it continues to be true as we look at artificial intelligence and machine learning. Large analytics teams are using models to solve for lending decisions, account management, investments, and more. However, unlike other industries taking advantage of modeling, financial institutions have the added complexity of regulation and transparency requirements to ensure fairness and explainability. That means institutions need highly sophisticated model operations and a highly skilled workforce to ensure that decisions are accurate and accountability is maintained. According to new research from Experian, we see that while financial institutions plan to use or are using models for a wide range of use cases, there is a range of ModelOps maturity across the industry. Just under half of financial institutions are in the early stages of model building, where projects are more ad-hoc in nature and experimental. Only a quarter of institutions seem to be more mature, where processes are well defined and models can be developed in a reliable timeframe. With more than two-thirds of lenders saying that ModelOps will play a key role in shaping the industry over the next five years, the race to maturity is critical. One of the biggest challenges we see in the space is that it takes too long for models to make it into production. On average, financial institutions estimate that the end-to-end process for creating a new model for credit decisioning takes an average of 15 months. Organizations need to accelerate model velocity, meaning the time that it takes to get a model into production and generating value, to take advantage of this powerful technology. Having the right technology, the right talent, and the right data at the right time continue to drag down operational speed and tracking of models after they are in production. For more information on Experian’s recent study, download the new report ‘Accelerating Model Velocity in Financial Institutions’. We are also hosting an upcoming webinar with tips on how to tackle some of the biggest model development and deployment challenges. You can register for the webinar here.

It's no secret that the banking industry is essential to a thriving economy. However, the nature of the industry makes it prone to various risks that can have significant consequences. Therefore, effective and efficient risk management is vital for mitigating these risks and enhancing the stability of the banking sector. This is where risk management in banking comes in. Let’s look at the importance of risk management in banking and its role in mitigating risks in the industry. What is risk management in banking? Risk management in banking is an approach used by financial institutions to manage risks associated with banking operations. Establishing a structured risk management process is essential to identifying, evaluating and controlling risks that could affect your operations. The process involves developing and implementing a comprehensive risk management framework consisting of several components, including risk assessment, mitigation, monitoring and reporting. Importance of banking risk management Banks face risks from every angle – changing customer behaviors, fraud, uncertain markets, and regulatory compliance, making banking risk management critical for the stability of financial institutions. There are various risks associated with the industry, including:  Credit risk: The probability of a financial loss resulting from a borrower's failure to repay a loan, which results in an interruption of cash flows and increased costs for collection. How to mitigate: Leverage advanced analytics, data attributes, and predictive models to improve predictability, manage portfolio risk, make better decisionsand acquire the best customers. Market risk:The likelihood of an investment decreasing in value because of market factors (I.e., changes in interest rates, geopolitical events or recessions). How to mitigate: While it is impossible to eliminate market risk, you can diversify your assets, more accurately determine your risk threshold and stay informed on economic and market conditions.  Liquidity risk:The risk that an organization cannot meet its short-term liabilities and financial payment obligations. How to mitigate: More regularly forecast your cash flow and conduct stress tests to determine potential risk scenarios that would cause a loss of liquidity and how much liquidity would be lost in each instance.  Operational risk:Potential sources of losses that result from inadequate or failed internal processes (I.e., poorly trained employees, a technological breakdown, or theft of information). How to mitigate: Hire the right staff and adequately train them, stay up to date with cybersecurity threats and automate processes to reduce human error. Reputational risk: The potential that negative publicity regarding business practices, whether true or not, will cause a decline in the customer base, costly litigation or revenue reductions. How to mitigate: Define your bank’s core ethical values and relay them to stakeholders and employees. You should also develop a reputational management strategy and contingency plan in case a reputation-affecting incident occurs. Risk management in banking best practices Successful banks embrace risks while developing powerful mechanisms to prevent or manage them and stay ahead. By taking a proactive approach and leveraging risk management tools, you can minimize losses, enhance stability and grow responsibly.  The steps for implementing a banking risk management plan, include:  Risk identification and assessment: Financial institutions need to identify potential risks associated with their operations and assess the severity and impact of these risks. Risk mitigation: Once risks have been identified and assessed, financial institutions can implement strategies to mitigate the effects of these risks. There are several strategies for risk mitigation, including risk avoidance, reduction, acceptance and transfer. Risk monitoring and reporting: One of the fundamental principles of a banking risk management strategy is ongoing monitoring and reporting. Financial institutions should continually monitor their operations to identify evolving risks and develop mitigation strategies. Generating reports about the progress of the risk management program gives a dynamic view of the bank’s risk profile and the plan’s effectiveness. Several challenges may arise when implementing a risk management strategy. These include new regulatory rules or amendments, cybersecurity and fraud threats, increased competition in the sector, and inefficient resources and processes. An effective risk management plan serves as a roadmap for improving performance and allows you to better allocate your time and resources toward what matters most.  Benefits of implementing a risk management strategy Banks must prioritize risk management to stay on top of the various critical risks they face every day. There are several benefits of taking a proactive approach to banking risk management, including:Improved efficiency: Enhance efficiency and deploy more reliable operations by identifying areas of weakness or inefficiencies in operational processes.Confident compliance: Ensure you comply with new and amended regulatory requirements and avoid costly fines. Enhanced customer confidence: Foster customer confidence to increase customer retention and mitigate reputational risk. Partnering to reduce risk and maximize growth Effective risk management is crucial for mitigating risks in the banking industry. By implementing a risk management framework, financial institutions can minimize losses, enhance efficiency, ensure compliance and foster confidence in the industry. At Experian, we have a team of experts dedicated to supporting our banking partners. Our team’s expertise paired with our innovative solutions can help you implement a powerful risk management process, as well as: Leverage data to reach company-wide business goals. Lower the cost of funds by attracting and retaining deposits. Protect your business against fraud and risk. Create less friction through automated decisioning. Grow your business portfolio and increase profitability. Learn more about our risk management solutions for banks and fraud risk solutions.

Money mule fraud is a type of financial scam in which criminals exploit individuals, known as money mules, to transfer stolen money or the proceeds of illegal activities. Money mule accounts are becoming increasingly difficult to distinguish from legitimate customers, especially as criminals find new ways to develop hard-to-detect synthetic identities. How money mule fraud typically works: Recruitment: Fraudsters seek out potential money mules through various means, such as online job ads, social media, or email/messaging apps. They will often pose as legitimate employers offering job opportunities promising compensation or claiming to represent charitable organizations. Deception: Once a potential money mule is identified, the fraudsters use persuasive tactics to gain their trust. They may provide seemingly legitimate explanations like claiming the money is for investment purposes, charity donations or for facilitating business transactions. Money Transfer: The mule is instructed to receive funds to their bank or other financial account. The funds are typically transferred from other compromised bank accounts obtained through phishing or hacking. The mule is then instructed to transfer the money to another account, sometimes located overseas. Layering: To mask the origin of funds and make them difficult to trace, fraudsters will employ layering techniques. They may ask the mule to split funds into smaller amounts, make multiple transfers to different accounts, or use various financial platforms such as money services or crypto. Compensation: The money mule is often promised a percentage of transferred funds as payment.  However, the promised monies are lower than the dollars transferred, or sometimes the mule receives no payment at all. Legal consequences: Regardless whether mules know they are supporting a criminal enterprise or are unaware, they can face criminal charges. In addition, their personal information could be compromised leading to identity theft and financial loss. How can banks get ahead of the money mule curve: Know your beneficiaries Monitor inbound paymentsEngage identity verification solutionsCreate a “Mule Persona” behavior profileBeware that fraudsters will coach the mule, therefore confirmation of payee is no longer a detection solution Educate your customers to be wary of job offers that seem too good to be true and remain vigilant of requests to receive and transfer money, particularly from unknown individuals and organizations. How financial institutions can mitigate money mule fraud risk When new accounts are opened, a financial institution usually doesn’t have enough information to establish patterns of behavior with newly registered users and devices the way they can with existing users. However, an anti-fraud system should catch a known behavior profile that has been previously identified as malicious. In this situation, the best practice is to compare the new account holder’s behavior against a representative pool of customers, which will analyze things like: Spending behavior compared to the averagePayee profileSequence of actionsNavigation data related to machine-like or bot behaviorAbnormal or risky locationsThe account owner's relations to other users The risk engine needs to be able to collect and score data across all digital channels to allow the financial institution to detect all possible relationships to users, IP addresses and devices that have proven fraud behavior. This includes information about the user, account, location, device, session and payee, among others. If the system notices any unusual changes in the account holder’s personal information, the decision engine will flag it for review. It can then be actively monitored and investigated, if necessary. The benefits of machine learning This is a type of artificial intelligence (AI) that can analyze vast amounts of disparate data across digital channels in real time. Anti-fraud systems based on AI analytics and predictive analytics models have the ability to aggregate and analyze data on multiple levels. This allows a financial institution to instantly detect all possible relationships across users, devices, transactions and channels to more accurately identify fraudulent activity. When suspicious behavior is flagged via a high risk score, the risk engine can then drive a dynamic workflow change to step up security or drive a manual review process. It can then be actively monitored by the fraud prevention team and escalated for investigation. How Experian can help Experian’s fraud prevention solutions incorporate technology, identity-authentication tools and the combination of machine learning analytics with Experian’s proprietary and partner data to return optimal decisions to protect your customers and your business. To learn more about how Experian can help you leverage fraud prevention solutions, visit us online or request a call

This article was updated on August 9, 2023. Debt collections can be frustrating — for both consumers and lenders alike. Coupled with ever-changing market conditions and evolving consumer expectations for their digital experience, lending institutions and collections agencies must develop the right collections strategies to reduce costs and maximize recovery rates. How can they do this? By following the three Cs — communication, choice and control. Communication To increase response rates and successfully retrieve payments, lenders must cater to consumers’ preferences for communication, or more specifically, make the right type of contact at the right time. With debt collection predictive analytics, you can gain a more holistic view of consumers and further insight into their behavioral and contact channel preferences. This way, you can better assess an individual's propensity to pay, determine the best way and time to reach them and develop more personalized treatment strategies. Control Debt collection solutions that provide a more comprehensive customer view can also give individuals greater control as they’re able to engage with collectors via a channel that may be easier or more comfortable for them than a phone call, such as email, text or chatbots. Providing consumers with various options is especially important as 81% think more highly of brands who offer multiple digital touchpoints. To further improve your methods of communication, consider streamlining monotonous processes with collection optimization. By automating repetitive tasks and outreach, you can reduce errors and free up your agents’ time to focus on accounts that need more attention, creating a customer-centric collections experience. Choice Ultimately, the success of collections initiatives relies heavily on how well collection practices are accepted and adopted by the end user. Consumers want to make informed decisions and want to be offered choices – therefore giving them more control in a decision-making process and with their finances. “Consumers have made a monumental shift to digital. To enhance your collections performance, it is critical to engage consumers in the method and channel of their choosing,” said Paul Desaulniers, Head of Scoring, Alternative Data and Collections at Experian. Lending institutions and third-party collection agencies that are able to communicate across all consumer channels will see more success in their collections strategies. Are your debt collection tactics and strategies up-to-par? READ: Strengthening Your Debt Collection Strategy Improve your collections strategy By catering to consumers’ communication preferences, giving them control and offering them choices, financial institutions and collections agencies can more effectively reach their customer base, with less effort. It’s a win-win for all. Experian offers various debt management and collections systems that can help you optimize processes, reduce costs and increase recovery rates. To get started, visit us today. Learn more

Using data to understand risk and make lending decisions has long been a forte of leading financial institutions. Now, with artificial intelligence (AI) taking the world by storm, lenders are finding innovative ways to improve their analytical capabilities. How AI analytics differs from traditional analytics Data analytics is analyzing data to find patterns, relationships and other insights. There are four main types of data analytics: descriptive, diagnostic, predictive and prescriptive. In short, understanding the past and why something happened, predicting future outcomes and offering suggestions based on likely outcomes. Traditionally, data analysts and scientists build models and help create decisioning strategies to align with business needs. They may form a hypothesis, find and organize relevant data and then run analytics models to test their hypothesis. However, time and resource constraints can limit the traditional analytics approach. As a result, there might be a focus on answering a few specific questions: Will this customer pay their bills on time? How did [X] perform last quarter? What are the chances of [Y] happening next year? AI analytics isn't completely different — think of it as a complementary improvement rather than a replacement. It relies on advances in computing power, analytics techniques and different types of training to create models more efficient than traditional analytics. By leveraging AI, companies can automate much of the data gathering, cleaning and analysis, saving them time and money. The AI models can also answer more complex questions and work at a scale that traditional analytics can't keep up with. Advances in AI are additionally offering new ways to use and interact with data. Organizations are already experimenting with using natural language processing and generative AI models. These can help even the most non-technical employees and customers to interact with vast amounts of data using intuitive and conversational interfaces. Benefits of AI analytics The primary benefits of AI-driven analytics solutions are speed, scale and the ability to identify more complex relationships in data. Speed: Where traditional analytics might involve downloading and analyzing spreadsheets to answer a single question, AI analytics automates these processes – and many others.Scale: AI analytics can ingest large amounts of data from multiple data sources to find analytical insights that traditional approaches may miss. When combined with automation and faster processing times, organizations can scale AI analytics more efficiently than traditional analytics.Complexity: AI analytics can answer ambiguous questions. For example, a marketing team may use traditional analytics to segment customers by known characteristics, such as age or location. But they can use AI analytics to find segments based on undefined shared traits or interests, and the results could include segments that they wouldn't have thought to create on their own. The insights from data analytics might be incorporated into a business intelligence platform. Traditionally, data analysts would upload reports or update a dashboard that business leaders could use to see the results and make educated decisions. Modern business intelligence and analytics solutions allow non-technical business leaders to analyze data on their own. With AI analytics running in the background, business leaders can quickly and easily create their own reports and test hypotheses. The AI-powered tools may even be able to learn from users' interactions to make the results more relevant and helpful over time. WATCH: See how organizations are using business intelligence to unlock better lending decisions with expert insights and a live demo. Using AI analytics to improve underwriting From global retailers managing supply chains to doctors making life-changing diagnoses, many industries are turning to AI analytics to make better data-driven decisions. Within financial services, there are significant opportunities throughout customer lifecycles. For example, some lenders use machine learning (ML), a subset of AI, to help create credit risk models that estimate the likelihood that a borrower will miss a payment in the future. Credit risk models aren't new — lenders have used models and credit scores for decades. However, ML-driven models have been able to outperform traditional credit risk models by up to 15 percent.1 In part, this is because the machine learning models might use traditional credit data and alternative credit data* (or expanded FCRA-regulated data), including information from alternative financial services and buy now pay later loans. They can also analyze the vast amounts of data to uncover predictive attributes that logistic regression (a more traditional approach) models might miss. The resulting ML models can score more consumers than traditional models and do so more accurately. Lenders that use these AI-driven models may be able to expand their lending universe and increase automation in their underwriting process without taking on additional risk. However, lenders may need to use a supervised learning approach to create explainable models for credit underwriting to comply with regulations and ensure fair lending practices. Read: The Explainability: ML and AI in credit decisioning report explores why ML models will become the norm, why explainability is important and how to use machine learning. Experian helps clients use AI analytics Although AI analytics can lead to more productive and efficient analytics operations over time, the required upfront cost or expertise may be prohibitive for some organizations. But there are simple solutions. Built with advanced analytics, our Lift Premium™ scoring model uses traditional and alternative credit data to score more consumers than conventional scoring models. It can help organizations increase approvals among thin-file and credit-invisible consumers, and more accurately score thick-file consumers.2 Experian can also help you create, test, deploy and monitor AI models and decisioning strategies in a collaborative environment. The models can be trained on Experian's vast data sources and your internal data to create a custom solution that improves your underwriting accuracy and capabilities. Learn more about machine learning and AI analytics. * 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. 1. Experian (2020). Machine Learning Decisions in Milliseconds 2. Experian (2022). Lift PremiumTM product sheet

As 2023 unfolds, rental housing owners and operators find themselves faced with a slightly different market than in the recent past. While rents are still high, rent growth has slowed somewhat, and the prospect of a cooler U.S. economy means more renters could be facing economic hardships in the months ahead. So, who is today's renter? In The State of the U.S. Rental Housing Market, a new report from Experian, we uncover that today’s renters are typically younger. According to our data derived from Experian RentBureau® and our analysis, 68.8% of today’s renters are either millennials (41.8%) or Gen Z (27%). Meanwhile, 17.3% are Gen X, 11.9% are baby boomers and only 2.2% are from the Silent Generation. Similarly, when you look at the renters who have a higher propensity to move — and thus need a new apartment or home to rent — they tend to skew younger. Our analysis shows that, of the renters who made two or more moves during the last two years, 43.2% were Gen Y (millennials). The younger Gen Y segment accounts for 25.2% of the frequent movers. As the population of renters has increased over the past decade, the concentration of growth appears to be among households earning $75,000 or more in annual income. About 7.6 million of these households were renters in 2009; by 10 years later, that figure had increased to 11.2 million. What is their financial status? Also, by some measurements, U.S. consumers — and, by extension, renters — improved their financial standing during the pandemic era. Credit scores rose as consumers used stimulus payments to pay down debt and save, but this trend is starting to normalize. The median conventional credit score rose above 700 in 2022, up from just above 680 in 2019. Still, according to Experian RentBureau, 63% of all renter households are low- to moderate-income earners, meaning they make less than 80% of the area median income. Furthermore, the average renter spends 38.6% of their income on rent. Households that spend more than 30% of their income on housing costs — including rent or mortgage payments, utilities and other fees — are considered “housing cost burdened” by the U.S. Department of Housing and Urban Development. For more insight and analysis of today’s rental-housing market, click here to download your free copy of The State of the U.S. Rental Housing Market report.

We already know over 286 million cars and light-duty trucks are in operation in the United States and that 4 out of 10 of those vehicles have been in an accident. That’s over 114 million vehicles on the road that have been in an accident. However, have you ever wondered what happens to a vehicle after it’s been involved in an accident? Does the owner keep the vehicle or get rid of it? If they decide to purchase a different vehicle, how soon after the reported accident do they purchase it? Does the frequency and timing in which an owner household purchases a different vehicle vary based on the accident's severity? Does this vary by other factors, such as vehicle type? If so, what vehicles are owners most likely, and least likely, to dispose of after a reported accident? To answer these questions, we completed an internal 3-year analysis of over 15 million unique VINs from all 50 states, Washington D.C., and Puerto Rico. We created a new detailed report with all the data insights! Check out our complimentary Vehicle Accident & Damage Insights report today!

Trust is the primary factor in any business building a long-lasting relationship, especially when a company operates globally and wants to build a loyal customer base. With the rapid acceleration of digital shopping and transactions comes a growing fraud landscape. And, given the continual increase of people wanting to transact online, marketplace companies – from ecommerce apps, ridesharing, to rental companies – need to have ideal strategies in place to protect themselves and their customers from fraudulent activities. Without ideal risk mitigation or comprehensive fraud and identity proofing strategies, marketplaces may find themselves facing the following: Card-not-present (CNP) Fraud: As online shopping increases, customers can’t provide a credit card directly to the merchant. That’s why fraudsters can use stolen credit card information to make unauthorized transactions. And in most cases, card owners are unaware of being compromised. Without an integrated view of risk, existing credit card authentication services used in isolation can result in high false positives, friction and a lack of card issuer support. Unverified Consumer Members, Vendors, Hosts & Drivers: From digital marketplace merchants like Etsy and Amazon, to peer-to-peer sharing economies like AirBnB, Uber and Lyft, the marketplace ecosystem is prone to bad actors who use false ID techniques to exploit both the platform and consumers for monetary gain. Additionally, card transaction touchpoints across the customer lifecycle increases risks of credit card authentication. This can be at account opening, account management when changes to existing account information is necessary, or at checkout. Buy Now, Pay Later (BNPL) Muling: While a convenient way for consumers to plan for their purchases, experts warn that without cautionary and security measures, BNPL can be a target for digital fraud. Fraudsters may use their own or fabricated identities or leverage account takeover to gain access to a legitimate user’s account and payment information to make purchases with no intent to repay. This leaves the BNPL provider at the risk of unrecoverable monetary losses that can impact the business’ risk tolerance. Forged Listings & Fake Accounts: Unauthorized vendors that create a fake account using falsified identities, stolen credit cards and publish fake listings and product reviews are another threat faced by ecommerce marketplaces. These types of fraud can happen without the vast data sources necessary to assess the risk of a customer and authenticate credit cards among other fraud and identity verification solutions. By not focusing on establishing trust, fraud mitigation management solutions and identity proofing strategies, businesses can often find themselves with serious monetary, reputational, and security qualms. Interested in learning more? Download Experian’s Building Trust in Digital Marketplaces e-book and discover the strategies digital marketplaces, like the gig economy and peer-to-peer markets, can take to keep their users safe, and protected from fraudulent activity. For additional information on how Experian is helping businesses mitigate fraud, explore our comprehensive suite of identity and fraud solutions. Download e-book

Today’s digital-first world is more interconnected than ever. Financial transactions take place across borders and through various channels, leaving financial institutions and their customers at increasing risk from evolving threats like identity theft, fraud and others from sophisticated crime rings. And consumers are feeling that pressure. A recent Experian study found that over half of consumers feel like they are more of a target for online fraud than a year ago. Likewise, more than 40% of businesses reported increased fraud losses in recent years. It’s not only critical that organizations ensure the security and trustworthiness of digital transactions and online account activity to reduce risk and losses but what consumers expect. In the same Experian study, more than 85% of consumers said they expect businesses to respond to their fraud concerns, an expectation that has increased over the last several years.   Businesses and financial institutions most successful at mitigating fraud and reducing risk have adopted a layered, interconnected approach to identity confirmation and fraud prevention. One vital tool in this process is identity document verification. This crucial step not only safeguards the integrity of financial systems but also protects individuals and organizations from fraud, money laundering and other illicit activities. In this blog, we will delve into the significance of identity document verification in financial services and explore how it strengthens the overall security landscape.  Preventing identity theft and fraud Identity document verification plays a vital role in thwarting identity theft and fraudulent activities. By verifying the authenticity of identification documents, financial institutions can ensure that the individuals accessing their services are who they claim to be. Sophisticated verification processes, including biometric identification and document validation, help detect counterfeit documents, stolen identities and impersonation attempts. By mitigating these risks, financial institutions can protect their customers from unauthorized access to accounts, fraudulent transactions and potential financial ruin. Compliance with regulatory requirements Financial institutions operate in an environment governed by stringent regulatory frameworks designed to combat money laundering, terrorist financing and other financial crimes. Identity document verification is a key component of these regulatory requirements. By conducting thorough verification checks, financial service providers can adhere to Know Your Customer (KYC) and Anti-Money Laundering (AML) regulations. Compliance safeguards the institution's reputation and helps combat illicit financial activities that can have far-reaching consequences for national security and stability. Mitigating risk and enhancing trust Effective identity document verification mitigates risks associated with financial services. By verifying the identity of customers, financial institutions can reduce the likelihood of fraudulent activities, such as account takeovers, unauthorized transactions and loan fraud. This verification process bolsters the overall security of the financial system and creates a more trustworthy environment for stakeholders. Trust is fundamental in establishing long-lasting customer relationships and attracting new clients to financial institutions.   Facilitating digital onboarding and seamless customer experience As financial services embrace digital transformation, identity document verification becomes essential for smooth onboarding processes. Automated identity verification solutions enable customers to open accounts and access services remotely, eliminating the need for in-person visits or cumbersome paperwork. By streamlining the customer experience and minimizing the time and effort required for account setup, financial institutions can attract tech-savvy individuals and enhance customer satisfaction.  Combating money laundering and terrorist financing Proper document verification is a key component of combating money laundering and terrorist financing activities. By verifying customer identities, financial institutions can establish the source of funds and detect suspicious transactions that may be linked to illicit activities. This proactive approach helps protect the integrity of the financial system, supports national security efforts, and contributes to the global fight against organized crime and terrorism. Identity document verification is a vital component in the layered, interconnected approach to mitigating and preventing fraud in modern financial services. By leveraging advanced technologies and robust verification processes, financial institutions can ensure the authenticity of customer identities, comply with regulatory requirements, mitigate risk and enhance trust.  As financial services continue evolving in an increasingly digital landscape, identity document verification will remain a crucial tool for safeguarding the security and integrity of the global financial system.  For more information on how Experian can help you reduce fraud while delivering a seamless customer experience, visit our fraud management solutions hub.   Learn more

After a period of historic, double-digit rent growth and razor-thin vacancy rates, the rental housing market has shown some signs of softening in recent months. And economic uncertainty still looms. The potential of a downturn this year and the existing economic strains faced by large swaths of renters may impact many rental-housing owners and managers nervous about their ability to find renters who can fulfill their lease terms. In The State of the U.S. Rental Housing Market, a new report from Experian, our data scientists and analysts offer key insights into the U.S. housing market and its impact on renters. The analysis in this report is derived from synthesizing various data samples and sources, including Experian credit attributes and models as well as data from the U.S. Census Bureau and Experian RentBureau®. Experian RentBureau is the largest rental payment database and contains over 4.4 million transactions and more than 25 million renter profiles. This report yields three major takeaways: Soaring interest rates and a slowing mortgage sector over the last year have taken heat out of the homebuying market, leading to more renters remaining in the renter pool. Inflation and other economic strains continue to squeeze renters’ finances. As rent prices increase and negative payment activity becomes more frequent, rental-housing owners and operators are striving to grow without expanding default risk and need to find renters with the best chances of fulfilling the terms of their leases. Among the report’s other notable findings: The average renter spends 38.6% of their income on rent. Households that spend more than 30% of their income on housing costs — including rent or mortgage payments, utilities and other fees — are considered “housing cost burdened” by the U.S. Department of Housing and Urban Development. Experian data shows 28% of renters with negative payment activity in 2022 (negative payment activity is defined as having late charges, insufficient funds, write-offs or outstanding balances). The figure represented an increase of 5.7 percentage points from 2021 and 3.8 percentage points from 2020. Also of note, low-to-moderate income renters are twice as likely to have a negative payment activity compared to other renters. Rent-to-income ratios are highest in the West and the Northeast. Among all 50 states, the leaders are Washington D.C. (40.9%), California (39.7%), Washington state (35.6%), Utah (35.6%) and New York (35.3%). Keep pace with trends in future blog posts that will dive deeper into the current conditions affecting the rental housing market and renters. In the meantime, click here to download your free copy of The State of the U.S. Rental Housing Market Report in full.

Evolving technologies and rising consumer expectations for fast, frictionless experiences highlight an opportunity for credit unions to advance their decisioning and stand out in a crowded market. How a credit union is optimizing their decision-making process With over $7.2 billion in assets and 330,000 members, Michigan State University Federal Credit Union (MSUFCU) aims to provide superior service to their members and employees. Initially reliant on manual reviews, the credit union needed a well-designed decisioning strategy that could help them grow their loan portfolio, increase employee efficiency, and reduce credit risk. The credit union implemented Experian’s decisioning platform, PowerCurve® Originations, to make faster, more accurate credit decisions on their secured and unsecured personal loans, leading to increased approvals and an exceptional member experience. “Day one of using PowerCurve produced a 49% automation rate! We have received amazing feedback from our teams about what a great product was chosen,” said Blake Johnson, Vice President of Lending, Michigan State University Federal Credit Union. After implementing PowerCurve Originations, MSUFCU saw an average monthly automation rate of more than 55% and decreased their application processing time to less than 24 hours. Read the full case study for more insight on how Experian can help power your decisioning to grow your business and member relationships. Download case study