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In today’s fast-paced world, the telecommunications industry is not just about connecting calls or sending messages. It’s about creating seamless digital experiences, especially when onboarding new customers. However, with the rise of digital services, the industry faces an increasing challenge: the need to mitigate fraud while streamlining the onboarding process.  The digital onboarding revolution Digital onboarding has transformed the way customers join telecommunications services. No longer are people required to visit a physical store or wait for lengthy paperwork. Instead, they can sign up for mobile, internet or TV services from the comfort of their homes, often within minutes. The convenience, however, has opened new doors for fraudsters. As the onboarding process happens online, the risk of identity theft, synthetic identity fraud and other fraudulent activities has surged. So, how can telecom companies provide fritctionless experiences while keeping fraud at bay? Mitigating fraud in telecommunications onboarding Know your customer (KYC) verification: Implement robust KYC solutions to verify the identity of new customers. This may include identity document checks, facial recognition or biometric authentication. Device and location data; and velocity: Analyze the device and location data of applicants. Does the device match the customer’s claimed location? Unusual patterns could signal potential fraud.  Behavioral analysis: Monitor user behavior during the onboarding process. Frequent changes in information or suspicious browsing activity may indicate fraudulent intent.   Machine learning (ML) and artificial intelligence (AI): Leverage AI/ML algorithms to detect patterns and anomalies humans might miss. These technologies can adapt and evolve to stay ahead of fraudsters.   Document verification: Use document verification services to ensure that documents provided by customers are genuine. This can include checks for altered or forged documents. Industry data sharing–consortia: Collaborate with industry databases and share fraud-related information to help identify applicants with a history of fraudulent activity or reveal patterns. The balancing act While it’s crucial to mitigate fraud, telecommunication companies must strike a balance between security and a seamless onboarding experience. Customers demand a hassle-free process, and overly stringent security measures can deter potential subscribers. By combining advanced technology, behavioral analysis and proactive fraud prevention strategies, telecom companies can create a secure digital onboarding journey that minimizes risk without compromising user experience. In doing so, they empower customers to embrace the convenience of digital services while staying one step ahead of fraudsters in today’s interconnected world.  Learn more about Experian and the telecom industry Learn more about our fraud and identity solutions

Model governance is growing increasingly important as more companies implement machine learning model deployment and AI analytics solutions into their decision-making processes. Models are used by institutions to influence business decisions and identify risks based on data analysis and forecasting. While models do increase business efficiency, they also bring their own set of unique risks. Robust model governance can help mitigate these concerns, while still maintaining efficiency and a competitive edge. What is model governance? Model governance refers to the framework your organization has in place for overseeing how you manage your development, model deployment, validation and usage.1 This can involve policies like who has access to your models, how they are tested, how new versions are rolled out or how they are monitored for accuracy and bias.2 Because models analyze data and hypotheses to make predictions, there's inherent uncertainty in their forecasts.3 This uncertainty can sometimes make them vulnerable to errors, which makes robust governance so important. Machine learning model governance in banks, for example, might include internal controls, audits, a thorough inventory of models, proper documentation, oversight and ensuring transparent policies and procedures. One significant part of model governance is ensuring your business complies with federal regulations. The Federal Reserve Board and the Office of the Comptroller of the Currency (OCC) have published guidance protocols for how models are developed, implemented and used. Financial institutions that utilize models must ensure their internal policies are consistent with these regulations. The OCC requirements for financial institutions include: Model validations at least once a year Critical review by an independent party Proper model documentation Risk assessment of models' conceptual soundness, intended performance and comparisons to actual outcomes Vigorous validation procedures that mitigate risk Why is model governance important — especially now? More and more organizations are implementing AI, machine learning and analytics into their models. This means that in order to keep up with the competition's efficiency and accuracy, your business may need complex models as well. But as these models become more sophisticated, so does the need for robust governance.3 Undetected model errors can lead to financial loss, reputation damage and a host of other serious issues. These errors can be introduced at any point from design to implementation or even after deployment via inappropriate usage of the model, drift or other issues. With model governance, your organization can understand the intricacies of all the variables that can affect your models' results, controlling production closely with even greater efficiency and accuracy. Some common issues that model governance monitors for include:2 Testing for drift to ensure that accuracy is maintained over time. Ensuring models maintain accuracy if deployed in new locations or new demographics. Providing systems to continuously audit models for speed and accuracy. Identifying biases that may unintentionally creep into the model as it analyzes and learns from data. Ensuring transparency that meets federal regulations, rather than operating within a black box. Good model governance includes documentation that explains data sources and how decisions are reached. Model governance use cases Below are just three examples of use cases for model governance that can aid in advanced analytics solutions. Credit scoring A credit risk score can be used to help banks determine the risks of loans (and whether certain loans are approved at all). Governance can catch biases early, such as unintentionally only accepting lower credit scores from certain demographics. Audits can also catch biases for the bank that might result in a qualified applicant not getting a loan they should. Interest rate risk Governance can catch if a model is making interest rate errors, such as determining that a high-risk account is actually low-risk or vice versa. Sometimes changing market conditions, like a pandemic or recession, can unintentionally introduce errors into interest rate data analysis that governance will catch. Security challenges One department in a company might be utilizing a model specifically for their demographic to increase revenue, but if another department used the same model, they might be violating regulatory compliance.4 Governance can monitor model security and usage, ensuring compliance is maintained. Why Experian? Experian® provides risk mitigation tools and objective and comprehensive model risk management expertise that can help your company implement custom models, achieve robust governance and comply with any relevant federal regulations. In addition, Experian can provide customized modeling services that provide unique analytical insights to ensure your models are tailored to your specific needs. Experian's model risk governance services utilize business consultants with tenured experience who can provide expert independent, third-party reviews of your model risk management practices. Key services include: Back-testing and benchmarking: Experian validates performance and accuracy, including utilizing statistical metrics that compare your model's performance to previous years and industry benchmarks. Sensitivity analysis: While all models have some degree of uncertainty, Experian helps ensure your models still fall within the expected ranges of stability. Stress testing: Experian's experts will perform a series of characteristic-level stress tests to determine sensitivity to small changes and extreme changes. Gap analysis and action plan: Experts will provide a comprehensive gap analysis report with best-practice recommendations, including identifying discrepancies with regulatory requirements. Traditionally, model governance can be time-consuming and challenging, with numerous internal hurdles to overcome. Utilizing Experian's business intelligence and analytics solutions, alongside its model risk management expertise, allows clients to seamlessly pass requirements and experience accelerated implementation and deployment. Experian can optimize your model governance Experian is committed to helping you optimize your model governance and risk management. Learn more here. References 1Model Governance," Open Risk Manual, accessed September 29, 2023. https://www.openriskmanual.org/wiki/Model_Governance2Lorica, Ben, Doddi, Harish, and Talby, David. "What Are Model Governance and Model Operations?" O'Reilly, June 19, 2019. https://www.oreilly.com/radar/what-are-model-governance-and-model-operations/3"Comptroller's Handbook: Model Risk Management," Office of the Comptroller of the Currency. August 2021. https://www.occ.treas.gov/publications-and-resources/publications/comptrollers-handbook/files/model-risk-management/pub-ch-model-risk.pdf4Doddi, Harish. "What is AI Model Governance?" Forbes. August 2, 2021. https://www.forbes.com/sites/forbestechcouncil/2021/08/02/what-is-ai-model-governance/?sh=5f85335f15cd

Have you heard about the mischievous ghosts haunting our educational institutions? No, I am not talking about Casper's misfit pals. These are the infamous ghost students! They are not here for a spooky study session, oh no! They are cunning fraudsters lurking in the shadows, pretending to be students who never attend classes. It is taking ghosting to a whole new level. Understanding ghost student fraud Ghost student fraud is a serious and alarming issue in the educational sector. The rise of online classes due to the pandemic has made it easier for fraudsters to exploit application systems and steal government aid meant for genuine students. Community colleges have become primary targets due to slower adoption of cybersecurity defenses. It is concerning to hear that a considerable number of applications, such as in California (where Social Security numbers are not required at enrollment), are fictitious, with potential losses in financial aid meant for students in need. The use of stolen or synthetic identities in creating bot-powered applications further exacerbates the problem. The consequences of enrollment fraud can have a profound impact on institutions and students. The recent indictment of individuals involved in enrollment fraud, where identities were stolen to receive federal student loans, highlights the severity of the issue. Unfortunately, the lack of awareness and inadequate identity document verification processes in many institutions make it difficult to fully grasp the extent of the problem. What is a ghost student? Scammers use different methods to commit ghost student loan fraud, including creating fake schools or enrolling in real colleges. Some fraudsters use deceitful tactics to obtain the real identities of students, and then they use it to fabricate loan applications. Types of ghost loan fraud, include: Fake loan offers: Fraudsters contact students via various channels, claiming to offer exclusive student loan opportunities with attractive terms and low interest rates. They often request personal and financial information including their SSN and bank account information and use it to create ghost loans. Identity theft: Threat actors will steal personal info through data breaches or phishing. They will then forge loan applications using the victim’s identity. Targeting vulnerable individuals: Ghost student loan fraud tends to prey on those already burdened by debt. Scammers may target borrowers with poor credit history, promising loan forgiveness or debt consolidation plans in exchange for a fee. Once the victim pays, the fraudsters disappear. Ultimately, addressing ghost student fraud requires a multi-faceted approach involving collaboration between educational institutions, government agencies, and law enforcement to safeguard the accessibility and integrity of education for all deserving students. Safeguarding the financial integrity of educational institutions One powerful weapon in the battle against ghost student fraudsters is the implementation of robust identity verification solutions. Financial institutions, online marketplaces, and government entities have long employed such tools to verify the authenticity of individuals, and their application in the educational domain can be highly effective. By leveraging these tools, institutions can swiftly and securely carry out synthetic fraud detection and confirm the identity of applicants by cross-referencing multiple credible sources of information. For instance, government-issued IDs can be verified against real-time selfies, email addresses can be screened against reliable databases, and personally identifiable information (PII) can be compared to third-party dark web data to detect compromised identities. Clinching evidence from various sources renders it nearly impossible for fraudsters to slip past the watchful eyes of enrollment officers. Moreover, implementation of identity verification measures can be facilitated through low-code implementation, ensuring seamless integration into existing enrollment workflows without requiring extensive technical expertise or incurring exorbitant development costs. To further fortify security measures, educational institutions may consider incorporating biometric enrollment and authentication solutions. By requiring face or voice biometrics for accessing school resources, institutions can create an additional layer of protection against fraudsters and their ethereal counterparts. The reluctance of fraudsters to enroll their own biometric data serves as a powerful deterrent against their intrusive activities. Taking action By adopting these robust measures, higher educational institutions can fortify their defenses against ghost student fraud and maintain the integrity of their finances. The use of online identity verification methods and biometric authentication systems not only strengthens the enrollment process but serves as a stringent reminder that there is no resting place for fraudsters within the hallowed halls of education. To learn more about how Experian can help you leverage fraud prevention solutions, visit us online or request a call. *The SSN Verification tool, better known as eCBSV is also a tool that can be utilized to verify SSN.  *This article leverages/includes content created by an AI language model and is intended to provide general information.

The deprecation of third-party cookies is one of the biggest changes to the automotive digital marketing landscape in recent years. Third-party cookies have long been used to track users across the web, which allows advertisers to target them with relevant ads. However, privacy concerns have led to the deprecation of third-party cookies in major browsers, such as Google Chrome and Safari. This change will have a significant impact on automotive marketers, as it will make it more difficult to track users and target them with ads. However, there are several things that auto marketers can do to prepare for the cookieless future. Here are some marketing tips when the cookie deprecates: Focus on first-party data. First-party data is data that you collect directly from your customers, such as email addresses, contact information, and purchase history. This data is more valuable than third-party data, as it is more accurate and reliable. You can use first-party data to create targeted ad campaigns and personalize your marketing messages. Work with a third-party aggregator. Automotive marketers can tackle a cookie-less world by using other sources of consumer data insights. For instance, a third-party data aggregator, like Experian, has access to numerous sources, platforms, and websites. Beyond that, we have access to a vast range of specific consumer data insights, including vehicle ownership, registrations, vehicle history data, and lending data. We take all that information and help marketers segment audiences and predict what consumers will do next. Leverage Universal Identifiers. Universal Identifiers provide a shared identity to identity across the supply chain without syncing cookies. First-party data (such as CRM data) and offline data can be used to create Universal Identifiers. Use contextual targeting and audience modeling. Contextual targeting involves targeting ads based on the content that a user is viewing. Contextual targeting is a privacy-friendly way to target ads and it can be effective in reaching relevant audiences. Utilize Identity Graphs. An identity graph combines Personally Identifiable Information (PII) with non-PIIs like first-party cookies and publisher IDS. Identity graphs will allow cross-channel and cross-platform tracking and targeting. Experian’s Graph precisely connects digital identifiers such as MAIDS, IPs, cookies, universal IDs, and hashed emails to households providing marketers with a consolidated view of consumers’ digital IDs. The deprecation of third-party cookies will be a challenge for auto marketers, but it's also an opportunity to rethink marketing strategies and focus on building stronger relationships with customers. Here are some additional cookieless marketing tips: Start preparing now. Don't wait until the last minute to start preparing for the cookieless future. Start collecting first-party data from your customers now. Be transparent with your customers. Let your customers know what data you are collecting and how you are using it. Make sure that you have their consent to collect and use their data. Be creative with your marketing campaigns. There are several ways to reach your target audience without relying on cookies. Be creative with your marketing campaigns and experiment with different strategies. Sample audience segments include: Consumers in market Loan status In positive equity Driving a specific year/make/model 1000+ lifestyle events such as new baby, marriage, new home Geography, demographics, psychographics To take it to the next level, we can use predictive analytics to go beyond what cookie data could provide by predicting who is ready to purchase a vehicle. For example, an auto marketer may have used cookie data to find buyers who had shown interest in a hybrid sedan, but that’s where it ended. When combining audience segmentation with a predictive model, marketers can target and identify consumers in-market and most likely ready to purchase a specific model. In this way, the data-driven insights from a third-party data provider specializing in automotive insights can replace the cookie-driven approach and take it a significant step beyond. The cookieless future is coming, but marketers who are prepared will be able to succeed. By focusing on first-party data, contextual targeting, and partnerships, auto marketers can reach their target audiences and achieve marketing goals.  

In financial crime, fraudsters are always looking for new avenues to exploit. The mortgage industry has traditionally been a primary target for fraudsters. But with the 30-year fixed-mortgage rate average above 7.19% for the month of September, it has caused an inherent slowdown in the volume of home purchases. As a result, criminals are turning to other lucrative opportunities in mortgage transactions. They have evolved their techniques to capitalize on unsuspecting homeowners and lenders by shifting their focus from home purchases to Home Equity Line of Credit (HELOC), as they see it as a more compelling option.  Understanding mortgage fraud  Mortgage fraud occurs when individuals or groups intentionally misrepresent information during the mortgage application process for personal gain. The most common forms of mortgage fraud include income misrepresentation, false identity, property flipping schemes, and inflated property appraisals. Over the years, financial institutions and regulatory bodies have implemented robust measures to combat such fraudulent activities.  As the mortgage industry adapts to counter established forms of fraud, perpetrators are constantly seeking new opportunities to circumvent detection. This has led to a shift in fraud trends, with fraudsters turning their focus to alternative aspects of the mortgage market. One area that has captured recent attention is HELOC fraud, also known as home equity loan fraud.  HELOC fraud: An attractive target for fraudsters  What is a HELOC?  HELOCs are financial products that allow homeowners to borrow against the equity in their homes, often providing flexible access to funds. While HELOCs can be a valuable financial tool for homeowners, they also present an attractive opportunity for fraudsters due to their unique characteristics.  HELOC fraud schemes  An example of a home equity loan fraud scheme is a fraudster misrepresenting himself to deceive a credit union call center employee into changing a member’s address and phone number. Three days later, the fraudster calls back to reset the member’s online banking password, allowing the fraudster to login to the member’s account. Once logged in, the fraudster orders share drafts to be delivered to the new address they now control. The fraudster then forges three share drafts totaling $309,000 and funds them through unauthorized advances against the member’s HELOC through online banking platforms.   Why HELOCs are becoming the next target for mortgage fraud  Rising popularity: HELOCs have gained significant popularity in recent years, enticing fraudsters seeking out opportunities with larger potential payouts.  Vulnerabilities in verification: The verification process for HELOCs might be less rigorous than traditional mortgages. Fraudsters could exploit these vulnerabilities to manipulate property valuations, income statements, or other critical information.  Lack of awareness: Unlike conventional mortgages, there may be a lack of awareness among homeowners and lenders regarding the specific risks associated with HELOCs. This knowledge gap can make it easier for fraudsters to perpetrate their schemes undetected.  Home equity loans do not have the same arduous process that traditional first mortgages do. These loans do not require title insurance, have less arduous underwriting processes, and do not always require the applicant to be physically present at a closing table to gain access to cash. The result is that those looking to defraud banks can apply for multiple HELOC loans simultaneously while escaping detection.  Prevention and safeguards  There are several preventive measures and fraud prevention solutions that can be established to help mitigate the risks associated with HELOCs. These include:  Education and awareness: Homeowners and lenders must stay informed about the evolving landscape of mortgage fraud, including the specific risks posed by HELOCs. Awareness campaigns and educational materials can play a significant role in spreading knowledge and promoting caution.   Enhanced verification protocols: Lenders should implement advanced verification processes and leverage data analytics and modeling thorough property appraisals, income verification, and rigorous background checks. Proper due diligence can significantly reduce the chances of falling victim to HELOC-related fraud.  Collaboration and information sharing: Collaboration between financial institutions, regulators, and law enforcement agencies is essential to combat mortgage fraud effectively. Sharing information, best practices, and intelligence can help identify emerging fraud trends and deploy appropriate countermeasures.  Acting with the right solution  Mortgage fraud is a constant threat that demands ongoing vigilance and adaptability. As fraudsters evolve their tactics, the mortgage industry must stay one step ahead to safeguard homeowners and lenders alike. With concerns over HELOC-related fraud rising, it is vital to raise awareness, strengthen preventive measures, and foster collaboration to protect the integrity of the mortgage market. By staying informed and implementing robust safeguards, we can collectively combat and prevent mortgage fraud from disrupting the financial security of individuals and the industry.  Experian mortgage is powering advanced capabilities across the mortgage lifecycle by gaining market intelligence, enhancing customer experience to remove friction and tapping into industry leading data sources to gain a complete view of borrower behavior.   To learn more about our HELOC fraud prevention solutions, visit us online or request a call.  *This article leverages/includes content created by an AI language model and is intended to provide general information.

In today’s age, where speed and convenience are paramount, lenders must transform their digital income verification experience to meet customer expectations. Leveraging the benefits of instant verification is crucial to delivering a seamless experience. However, there are situations where instant verification may not be available or unable to verify customers. This is where the value of incorporating user-permissioned verification into your workflow becomes evident.   Let’s explore the advantages of using a combination of instant and permissioned verification and how they can synergistically enhance coverage, reduce costs, improve efficiency, and deliver an exceptional customer experience.  Instant verification: The epitome of efficiency and experience  Instant verification technology enables lenders to access real-time customer data, making it the pinnacle of verification efficiency. Its ability to deliver immediate insights facilitates quick decision-making, ensuring a seamless and frictionless experience for lenders and customers. There are several benefits to streamlining your verification process, including:   Speed and efficiency: Eliminate the time-consuming process of manually gathering and analyzing data to expedite loan approvals and reduce customer waiting times.  Enhanced user experience: With real-time results, customers can complete their applications quickly and effortlessly, leading to increased satisfaction and higher conversion rates.  Reduced risk: Assess applicant information promptly, maintaining the security and integrity of lending processes.  Permissioned verification: Expanding coverage and engaging customers  While instant verification technology offers numerous advantages, it may not always be available or suitable for every customer. This is where permissioned verification plays a vital role. By integrating permissioned verification into the verification workflow, lenders can expand coverage and keep customers engaged in a digital channel, reducing abandonment rates. The benefits of leveraging permissioned verification include:  Convenience and speed: By granting permissioned access, customers avoid the hassle of uploading or submitting documents manually. This saves time and effort, resulting in a faster verification process.  Increased coverage and reduced abandonment: Permissioned verification ensures a higher coverage rate by minimizing the potential for customer abandonment during the application process. Since the information is retrieved seamlessly, customers are more likely to complete the application without frustration.  Privacy and control: Customers retain control over their data by explicitly granting permission for access. This enhances transparency and empowers individuals to manage their financial information securely.  Creating a verification "waterfall" for optimal results  To harness the combined power of instant and permissioned verification, lenders can establish a verification "waterfall" approach. This approach involves a cascading verification process where instant verification is the first step, followed by permissioned verification if instant verification is not available or unable to verify the customer.                                              Example of Experian Verify’s automated verification waterfall.  There are numerous advantages to adopting a “waterfall” approach, including:   Cost efficiency: Lenders who prioritize instant verification save on operational costs associated with manual verification processes. The seamless transition to permissioned verification reduces the need for manual intervention, minimizing expenses and improving efficiency.  Improved verification success rate: A verification waterfall ensures that alternative verification methods are readily available if the initial instant verification is unsuccessful. This increases the overall success rate of verifying customer data and reduces the likelihood of losing potential borrowers.  Enhanced customer experience: The combination of instant and permissioned verification creates a streamlined and frictionless customer experience. Customers can progress seamlessly through the verification process, reducing frustration and increasing satisfaction levels.  Propelling your business forward  In the dynamic landscape of lending, a combination of instant and permissioned verification technologies provides significant value to lenders and customers. While instant verification delivers unparalleled efficiency and experience, incorporating permissioned verification ensures expanded coverage, reduced abandonment rates, and a seamless digital journey for customers. By implementing a verification "waterfall" approach, lenders can optimize verification processes, reduce costs, improve efficiency, and ultimately deliver an exceptional customer experience.  Learn more about our solutions The advantages of instant and permissioned verification *This article leverages/includes content created by an AI language model and is intended to provide general information.

From science fiction-worthy image generators to automated underwriting, artificial intelligence (AI), big data sets and advances in computing power are transforming how we play and work. While the focus in the lending space has often been on improving the AI models that analyze data, the data that feeds into the models is just as important. Enter: data-centric AI. What is a data-centric AI? Dr. Andrew Ng, a leader in the AI field, advocates for data-centric AI and is often credited with coining the term. According to Dr. Ng, data-centric AI is, ‘the discipline of systematically engineering the data used to build an AI system.’1 To break down the definition, think of AI systems as a combination of code and data. The code is the model or algorithm that analyzes data to produce a result. The data is the information you use to train the model or later feed into the model to request a result. Traditional approaches to AI focus on the code — the models. Multiple organizations download and use the same data sets to create and improve models. But today, continued focus on model development may offer a limited return in certain industries and use cases. A data-centric AI approach focuses on developing tools and practices that improve the data. You may still need to pay attention to model development but no longer treat the data as constant. Instead, you try to improve a model's performance by increasing data quality. This can be achieved in different ways, such as using more consistent labeling, removing noisy data and collecting additional data.2 Data-centric AI isn't just about improving data quality when you build a model — it's also part of the ongoing iterative process. The data-focused approach should continue during post-deployment model monitoring and maintenance. Data-centric AI in lending Organizations in multiple industries are exploring how a data-centric approach can help them improve model performance, fairness and business outcomes. For example, lenders that take a data-centric approach to underwriting may be able to expand their lending universe, drive growth and fulfill financial inclusion goals without taking on additional risk. Conventional credit scoring models have been trained on consumer credit bureau data for decades. New versions of these models might offer increased performance because they incorporate changes in the economic landscape, consumer behavior and advances in analytics. And some new models are built with a more data-centric approach that considers additional data points from the existing data sets — such as trended data — to score consumers more accurately. However, they still solely rely on credit bureau data. Explainability and transparency are essential components of responsible AI and machine learning (a type of AI) in underwriting. Organizations need to be able to explain how their models come to decisions and ensure they are behaving as expected. Model developers and lenders that use AI to build credit risk models can incorporate new high-quality data to supplement existing data sets. Alternative credit data can include information from alternative financial services, public records, consumer-permissioned data, and buy now, pay later (BNPL) data that lenders can use in compliance with the Fair Credit Reporting Act (FCRA).* The resulting AI-driven models may more accurately predict credit risk — decreasing lenders' losses. The models can also use alternative credit data to score consumers that conventional models can't score. Infographic: From initial strategy to results — with stops at verification, decisioning and approval — see how customers travel across an Automated Loan Underwriting Journey. Business benefit of using data-centric AI models Financial services organizations can benefit from using a data-centric AI approach to create models across the customer lifecycle. That may be why about 70 percent of businesses frequently discuss using advanced analytics and AI within underwriting and collections.3 Many have gone a step further and implemented AI. Underwriting is one of the main applications for machine learning models today, and lenders are using machine learning to:4 More accurately assess credit risk models. Decrease model development, deployment and recalibration timelines. Incorporate more alternative credit data into credit decisioning. AI analytics solutions may also increase customer lifetime value by helping lenders manage credit lines, increase retention, cross-sell products and improve collection efforts. Additionally, data-centric AI can assist with fraud detection and prevention. Case study: Learn how Atlas Credit, a small-dollar lender, used a machine learning model and loan automation to nearly doubled its loan approval rates while decreasing its credit risk losses. How Experian helps clients leverage data-centric AI for better business outcomes During a presentation in 2021, Dr. Ng used the 80-20 rule and cooking as an analogy to explain why the shift to data-centric AI makes sense.5 You might be able to make an okay meal with old or low-quality ingredients. However, if you source and prepare high-quality ingredients, you're already 80% of the way toward making a great meal. Your data is the primary ingredient for your model — do you want to use old and low-quality data? Experian has provided organizations with high-quality consumer and business credit solutions for decades, and our industry-leading data sources, models and analytics allow you to build models and make confident decisions. If you need a sous-chef, Experian offers services and has data professionals who can help you create AI-powered predictive analytics models using bureau data, alternative data and your in-house data. Learn more about our AI analytics solutions and how you can get started today. 1DataCentricAI. (2023). Data-Centric AI.2Exchange.scale (2021). The Data-Centric AI Approach With Andrew Ng.3Experian (2021). Global Insights Report September/October 2021.4FinRegLab (2021). The Use of Machine Learning for Credit Underwriting: Market & Data Science Context. 5YouTube (2021). A Chat with Andrew on MLOps: From Model-Centric to Data-Centric AI *Disclaimer: 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. Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.

The Federal Reserve (Fed) took a big step towards revolutionizing the U.S. payment landscape with the official launch of FedNow, a new instant payment service, on July 20, 2023. While the new payment network offers advantages, there are concerns that fraudsters may be quick to exploit the new real-time technology with fraud schemes like automated push payment (APP) fraud. How is FedNow different from existing payment networks? To keep pace with regions across the globe and accelerate innovation, the U.S. created a alternative to the existing payment network known as The Clearing House (TCH) Real-Time Payment Network (RTP). Fraudsters can use the fact that real-time payments immediately settle to launder the stolen money through multiple channels quickly. The potential for this kind of fraud has led financial regulators to consider measures to better protect against it. While both FedNow and RTP charge a comparable fee of 4.5 cents per originated transaction, the key distinction lies in their governance. RTP is operated by a consortium of large banks, whereas FedNow falls under the jurisdiction of the Federal Reserve Bank. This distinction could give FedNow an edge in the market. One of the advantages of FedNow is its integration with the extensive Federal Reserve network, allowing smaller local banks across the country to access the service. RTP estimates accessibility to institutions holding approximately 90% of U.S. demand deposit accounts (DDAs), but currently only reaches 62% of DDAs due to limited participation from eligible institutions. What are real-time payments? Real-time payments refer to transactions between bank accounts that are initiated, cleared, and settled within seconds, regardless of the time or day. This immediacy enhances transparency and instills confidence in payments, which benefits consumers, banks and businesses.Image sourced from JaredFranklin.com Real-time payments have gained traction globally, with adoptions from over 70 countries on six continents. In 2022 alone, these transactions amounted to a staggering $195 billion, representing a remarkable year-over-year growth of 63%. India leads the pack with its Unified Payments Interface platform, processing a massive $89.5 billion in transaction volume. Other significant markets include Brazil, China, Thailand, and South Korea. The fact that real-time payments cannot be reversed promotes trust and ensures that contracts are upheld. This also encourages the development of new methods to make processes more efficient, like the ability to pay upon receiving the goods or services. These advancements are particularly crucial for small businesses, which disproportionately bear the burden of delayed payments, amounting to a staggering $3 trillion globally at any given time. The launch of FedNow marks a significant milestone in the U.S. financial landscape, propelling the country towards greater efficiency, transparency, and innovation in payments. However, it also brings a fair share of challenges, including the potential for increased fraud. Are real-time payments a catalyst for fraud? As the financial landscape evolves with the introduction of real-time payment systems, fraudsters are quick to exploit new technologies. One particular form of fraud that has gained prominence is authorized push payment (APP) fraud. APP fraud is a type of scam where fraudsters trick individuals or businesses into authorizing the transfer of funds from their bank accounts to accounts controlled by the fraudsters. The fraudster poses as a legitimate entity and deceives the victim into believing that there is an urgent need to transfer money. They gain the victim's trust and provide instructions for the transfer, typically through online or telephone banking channels. The victim willingly performs the payment, thinking it is legitimate, but realizes they have been scammed when communication halts. APP fraud is damaging as victims authorize the payments themselves, making it difficult for banks to recover the funds. To protect against APP fraud, it's important to be cautious, verify the legitimacy of requests independently, and report any suspicious activity promptly. Fraud detection and prevention with real-time payments Advances in fraud detection software, including machine learning and behavioral analytics, make unusual urgent requests and fake invoices easier to spot — in real time — but some governments are considering legislation to ensure more support for victims. For example, in the U.K., frameworks like Confirmation of Payee have rolled out instant account detail checks against the account holder’s name to help prevent cases of authorized push payment fraud. The U.K.’s real-time payments scheme Pay.UK also introduced the Mule Insights Tactical Solution (MITS), which tracks the flow of fraudulent transactions used in money laundering through bank and credit union accounts. It identifies these accounts and stops the proceeds of crimes from moving deeper into the system – and can help victims recover their funds. While fraud levels related to traditional payments have slowly come down, real-time payment-related fraud has recently skyrocketed. India, one of the primary innovators in the space, recorded a 23% rise in fraud related to its real-time payments system in 2022. The same ACI report stated that the U.S., making up only 1.2% of all real-time payment transactions in 2022, had, for now, avoided the effects. However, “there is no reason to assume that without action, the U.S. will not follow the path to crisis levels of APP scams as seen in other markets.” FedNow currently has no specific plans to bake fraud detection into their newly launched technology, meaning the response is left to financial institutions. Fight instant fraud with instant answers Artificial Intelligence (AI) holds tremendous potential in combating the ever-present threat of fraud. With AI technologies, financial institutions can process vast amounts of data points faster and enhance their fraud detection capabilities. This enables them to identify and flag suspicious transactions that deviate from the norm, mitigating identity risk and safeguarding customer accounts. The ability of AI-powered systems to ingest and analyze real-time information empowers institutions to stay one step ahead in the battle against account takeover fraud. This type of fraud, which poses a significant challenge to real-time payment systems, can be better addressed through AI-enabled tools. With ongoing monitoring of account behavior, such as the services provided by FraudNet, financial institutions gain a powerful weapon against APP fraud. In addition to behavioral analysis, location data has emerged as an asset in the fight against fraud. Incorporating location-based information into fraud detection algorithms has proven effective in pinpointing suspicious activities and reducing fraudulent incidents. As the financial industry continues to grapple with the constant evolution of fraud techniques, harnessing the potential of AI, coupled with comprehensive data analysis and innovative technologies, becomes crucial for securing the integrity of financial transactions. Taking your next step in the fight against fraud Ultimately, the effectiveness of fraud prevention measures depends on the implementation and continuous improvement of security protocols by financial institutions, regulators, and technology providers. By staying vigilant and employing appropriate safeguards, fraud risks in real-time payment systems, such as FedNow, can be minimized. To learn more about how Experian can help you leverage fraud prevention solutions, visit us online or request a call.  *This article leverages/includes content created by an AI language model and is intended to provide general information.

Credit risk refers to the likelihood that a borrower will fail to repay a debt as agreed. Credit risk management is the art and science of utilizing risk mitigation tools to minimize losses while maximizing profits from lending activities.   Lenders can establish credit underwriting criteria for each of their products and utilize risk-based pricing to adjust the terms of a loan or line of credit based on the risk associated with the product and borrower. Credit portfolio management extends beyond originations and individual decisions to encompass portfolios as a whole.   Why is credit risk management important? Continuously managing credit risk matters because there's always a balancing act.   Tightening a credit box – using more restrictive underwriting criteria – might reduce credit losses. However, it can also decrease approval rates, excluding borrowers who would have repaid as agreed. Expanding a credit box might increase approval rates, but it is only beneficial if the profit from good new loans exceeds credit losses.   Fraud is also on the rise and becoming increasingly complex, making fraud management a crucial part of understanding risk. For instance, with synthetic identity fraud, fraudsters might “age an account" or make on-time payments before “busting out” or maxing out a credit card, and then abandoning the account.  If you examine payment activity alone, it may be challenging to classify the loss as either a fraud loss or a credit loss.  Additionally, external economic forces and consumer behavior are constantly in flux. Financial institutions need effective consumer risk management and to adjust their strategies to minimize losses. And they must dynamically adjust their underwriting criteria to account for these changes. You could be pushed off balance if you don't react in time. What does managing credit risk entail? Lenders have used the five C’s of credit to measure credit risk and make lending decisions for decades:  Character: The likelihood a borrower will repay the loan as agreed, often measured by analyzing their credit report and a credit risk score.   Capacity: The borrower's ability to pay, which lenders might measure by reviewing their outstanding debt, income, and debt-to-income ratio.   Capital: The borrower's commitment to the purchase, such as their down payment when buying a vehicle or home.   Collateral: The value of the collateral, such as a vehicle or home, for an auto loan or mortgage.   Conditions: The external conditions that can impact a borrower's ability to afford payments, such as broader economic trends.  Credit risk management considers these within the context of a lender’s goals and its specific lending products. For example, capital and collateral aren't relevant for unsecured personal loans, which makes character and capacity the primary drivers of a decision.   Credit risk management best practices at origination Advances in analytics, computing power and real-time access to additional data sources are helping lenders better measure some of the C’s.   For example, credit risk scores can more precisely assess character for a lender's target market than generic risk scores. Open banking data enables lenders to more accurately assess a borrower's capacity by directly analyzing their cash flows.   With these advances in mind, leading lenders:  View underwriting as a dynamic process: Lenders have always had to respond to changing forces, and the pandemic highlighted the need to be nimble. Consider how you can utilize analytical insights to quickly adjust your strategies.   Test the latest credit risk modeling techniques: Artificial intelligence (AI) and machine learning (ML) techniques can improve credit risk model performance and drive automated credit risk decisioning.  Use multiple data sources: Alternative credit data and consumer-permissioned data offer increased and real-time visibility into borrowers' creditworthiness to help lenders more accurately assess credit risk. These additional data sources can score those who are unscoreable by conventional models and help fuel ML credit risk models. Experian helps lenders measure and manage credit risk Experian is a leading provider of traditional credit data, alternative credit data and credit risk analytics.   For those who want to quickly benefit from the latest technological advancements, our Lift Premium credit risk model utilizes both traditional and alternative data to score up to 96 percent of U.S. consumers — compared to the 81 percent that conventional models can score.¹  Experian’s Ascend Platform and Ascend Intelligence Services™ can help lenders develop, deploy and monitor custom credit risk models to optimize their decisions.    With end-to-end platforms, our account and portfolio management services can help you limit risk, detect fraud, automate underwriting and identify opportunities to grow your business.   Learn more about credit risk management ¹Experian (2023). Lift Premium™ and Lift Plus™

Banking uncertainty creates opportunity for fraud The recent regional bank collapses left anxious consumers scrambling to withdraw their funds or open new accounts at other institutions. Unfortunately, this situation has also created an opportunity for fraudsters to take advantage of the chaos. Criminals are exploiting the situation and posing as legitimate customers looking to flee their current bank to open new accounts elsewhere. Financial institutions looking to bring on these consumers as new clients must remain vigilant against fraudulent activity. Fraudsters also prey on vulnerable individuals who may be financially stressed and uncertain about the future. This creates a breeding ground for scams as fear and uncertainty cloud judgment and make people more susceptible to manipulation. Beware of fraudulent tactics Now, it is more important than ever for financial institutions to be vigilant in their due diligence processes. As they navigate this period of financial turbulence, they must take extra precautions to ensure that new customers are who they say they are by verifying customer identities, conducting thorough background checks where necessary, and monitoring transactions for any signs of suspicious activity. Consumers should also maintain vigilance — fraudulent schemes come in many forms, from phishing scams to fake investment opportunities promising unrealistic returns. To protect yourself against these risks, it is important to remain vigilant and take precautions such as verifying the legitimacy of any offers or investments before investing, monitoring your bank and credit card statements regularly for suspicious activity, and being skeptical of unsolicited phone calls, emails, or text messages. Security researcher Johannes Ulrich reported that threat actors are jumping at the opportunity, registering suspicious domains related to Silicon Valley Bank (SVB) that are likely to be used in attacks. Ulrich warned that the scammers might try to contact former clients of SVB to offer them a support package, legal services, loans, or other fake services relating to the bank's collapse. Meanwhile, on the day of the SVB closure, synthetic identity fraud began to climb from an attack rate of .57 to a first peak of 1.24% on the Sunday following the closure, or an increase of 80%. After the first spike reduced on March 14, we only saw a return of an even higher spike on March 21 to 1.35%, with bumps continuing since then. As the economy slows and fraud rises, don’t let your guard down The recent surge in third-party attack rates on small business and investment platforms is a cause for concern. There was a staggering nearly 500% increase in these attacks between March 7th and 11th, which coincided with the release of negative news about SVB. Bad actors had evidently been preparing for this moment and were quick to exploit vulnerabilities they had identified across our financial system. They used sophisticated bots to create multiple accounts within minutes of the news dropping and stole identities to perpetrate fraudulent activities. This underscores the need for increased vigilance and proactive measures to protect against cyber threats impacting financial institutions. Adopting stronger security measures like multi-factor authentication, real-time monitoring, and collaboration with law enforcement agencies for timely identification of attackers is of paramount importance to prevent similar fraud events in the future. From frictionless to friction-right As businesses seek to stabilize their operations in the face of market turbulence, they must also remain vigilant against the threat of fraud. Illicit activities can permeate a company's ecosystem and disrupt its operations, potentially leading to financial losses and reputational damage. Safeguarding against fraud is not a simple task. Striking a balance between ensuring a smooth customer experience and implementing effective fraud prevention measures can be a challenging endeavor. For financial institutions in particular, being too stringent in fraud prevention efforts may drive customers away, while being too lenient can expose them to additional fraud risks. This is where a waterfall approach, such as that offered by Experian CrossCore®, can prove invaluable. By leveraging an array of fraud detection tools and technologies, businesses can tailor their fraud prevention strategies to suit the specific needs and journeys of different customer segments. This layered, customized approach can help protect businesses from fraud while ensuring a seamless customer experience. Learn more

The unsecured personal loan, one of the most popular products in the financial space, has seen ebbs and flows over the last several years due to many factors, including economic volatility, the global pandemic, changing consumer behaviors and expectations, and more. Experian data scientists and analysts took a deep dive into data between 2018 and 2022 to uncover and analyze trends in this important industry segment. Additionally, they recommend fintech lending solutions to help fintechs stay ahead of ever-changing market conditions and discover new opportunities. This analysis shows that digital loans accounted for 45 percent of the market in 2022. While this is down from 52% in 2021, digital loan market share continues to grow. The analysis also provides a detailed look into who the digital borrower is and how they compare to traditional borrowers. As we look to the rest of 2023 and beyond, fintechs must be armed with the best digital lending technology, tools, and data to fuel profitable growth while mitigating as much risk as possible. Download our fintech trends report for a full analysis on origination volume trends, delinquency trends, and consumer behavior insights. Download now

Innovation and inspiration took center stage at Day 2 of Vision. Jennifer Schulz, CEO of Experian, North America opened the day with a look ahead at some of the solutions that are powering opportunities today and tomorrow. Sitting down with Robert Boxberger, President, Decision Analytics, and Scott Brown, President, Consumer Information Services, the group discussed key priorities for business innovation and the need to challenge the status quo. AI came up for discussion – as was no surprise – and while AI isn’t new, the newest versions are transformative. Whether it’s building a model a mile up (mid-flight), or continuously asking if solutions are “first, best or only,” innovation is part of Experian’s DNA as evidenced by two demos that took place on the main stage. Demo: Fraud Sandbox Fraud moves fast. A demo of the Fraud Sandbox showed the audience the importance of looking at consumer insights to pull back fraud signals. By leveraging the right fraud platform, you can turn insight into action. Working only with internal data is limiting, making it hard to detect fraud clusters and organizations open themselves up to millions of dollars in fraud; with external data it's easier to spot multiple uses of same information in multiple applications. Demo: Ascend Ops Ascend Ops connects data across different parts of the business and automates the process of model deployment so you can spend less time deploying and more time testing in market. Keynote: Alexis Ohanian Alexis Ohanian credits his success to a series of very fortunate events. The son and grandson of immigrants, Ohanian saw hustle, self-reliance and grit in his parents that he hopes his own children have. The innovator talked about how important timing is for entrepreneurs, discussing early ideas, starting Reddit and what he looks for in backing startups via his venture capitalist firm Seven Seven Six – named after 776 BCE, the year of the first Olympics. Ohanian also talked about the need to lean into AI – that it can make lean teams more efficient when you can automate to accomplish more, faster. It also enables humans to do work that is creative, strategic and empathetic, with a better quality of life. And to round it out, the self-proclaimed “business dad” talked about how having a bigger why – in the form of children – makes him better at his job. Keynote: Michael Strahan Michael Strahan shared a number of gridiron glory stories, the mental muscle it takes to get into the zone on game day, and the rolodex of injuries he had over the years – and how it taught him to look at people as individuals; an education in sociology. From his father he learned to talk about “when” rather than “if” and he’s developed a “keep going” mentality when it comes to everything he does. From clothing lines and skincare to management and production, Strahan says he’s committed to continuing to say yes and not be afraid of trying anything. Session highlights – day 2 Identity and fraud trends Current considerations that are top of mind for organizations include the speed of change, regulatory landscape, technology and the number of people online. Fraudsters are evolving faster than ever and are returning to the basics – think DDA fraud, check fraud and check washing. It is imperative to balance security with convenience and seamlessness as consumer expectations aren’t waning; therefore, it’s the business’ responsibility to meet and exceed customer expectations and to ultimately protect customers. Consumer credit trends and innovation Retailers and tech titans are pushing further into financial services. What separates them from the industry? People rave about brands more so than they do banks. The session delineated that digital transformation is not the digitization of what institutions were already doing. It requires a new way of thinking. Consumer privacy In 2023, 26 states have introduced comprehensive privacy legislation. It’s top of mind for consumers and top of mind for the government. Experian approaches privacy with strategies focused on security, accuracy, fairness, transparency and inclusion. Operational efficiency A panel of financial institutions experts discussed how they use analytics for operational efficiency. They talked about how they prioritize, the importance of the regulatory wrapper, and what differentiated their methods to reach success and make an impact. Fraud Organizations must consider the risks and rewards of their actions. It is critical to use analytics to stay agile and leverage owned and external data to make smart, fast and safe decisions. The action items for today’s organizations? Model, test, scale, repeat – scale your model based on your growth goals and expectations, and truly know your customer at every point of the interaction.   That’s a wrap on Vision 2023. We can’t wait to build on this momentum and see the conversations we have in store next year!

The science of turning historical data into actionable insights is far from magic. And while organizations have successfully used predictive analytics for years, we're in the midst of a transformation. New tools, vast amounts of data, enhanced computing power and decreasing implementation costs are making predictive analytics increasingly accessible. And business leaders from varying industries and functions can now use the outcomes to make strategic decisions and manage risk. What is predictive analytics? Predictive analytics is a type of data analytics that uses statistical modeling and machine learning techniques to make predictions based on historical data. Organizations can use predictive analytics to predict risks, needs and outcomes. You might use predictive analytics to make an immediate decision. For example, whether or not to approve a new credit application based on a credit score — the output from a predictive credit risk model. But organizations can also use predictive analytics to make long-term decisions, such as how much inventory to order or staff to hire based on expected demand. How can predictive business analytics help a business succeed? Businesses can use predictive analytics in different parts of their organizations to answer common and critical questions. These include forecasting market trends, inventory and staffing needs, sales and risk. With a wide range of potential applications, it’s no surprise that organizations across industries and functions are using predictive analytics to inform their decisions. Here are a few examples of how predictive analytics can be helpful: Financial services: Financial institutions can use predictive analytics to assess credit risk, detect fraudulent applicants or transactions, cross-sell customers and limit losses during recovery. Healthcare: Using data from health records and medical devices, predictive models can predict patient outcomes or identify patients who need critical care. Manufacturing: An organization can use models to predict when machines need to be turned off or repaired to improve their longevity and avoid accidents. Retail: Brick-and-mortar retailers might use predictive analytics when deciding where to expand, what to cross-sell loyalty program members and how to improve pricing. Hospitality: A large hospitality group might predict future reservations to help determine how much staff they need to hire or schedule. Advanced techniques in predictive modeling for financial services Emerging technologies, particularly AI and machine learning (ML), are revolutionizing predictive modeling in the financial sector by providing more accurate, faster and more nuanced insights. Taking a closer look at financial services, consider how an organization might use predictive credit analytics and credit risk scores across the customer lifecycle. Marketing: Segment consumers to run targeted marketing campaigns and send prescreened credit offers to the people who are most likely to respond. AI models can analyze customer data to offer personalized offers and product recommendations. Underwriting: AI technologies enable real-time data analysis, which is critical for underwriting. The outputs from credit risk models can help you to quickly approve, deny or send applications for manual review. Explainable machine learning models may be able to expand automation and outperform predictive models built with older techniques by 10 to 15 percent.1 Fraud detection models can also raise red flags based on suspicious information or behaviors. Account management: Manage portfolios and improve customer retention, experience and lifetime value. The outputs can help you determine when you should adjust credit lines and interest rates or extend offers to existing customers. AI can automate complex decision-making processes by learning from historical data, reducing the need for human intervention and minimizing human error. Collections: Optimize and automate collections based on models' predictions about consumers' propensity to pay and expected recovery amounts. ML models, which are capable of processing vast amounts of unstructured data, can uncover complex patterns that traditional models might miss. Although some businesses can use unsupervised or “black box" models, regulations may limit how financial institutions can use predictive analytics to make lending decisions. Fortunately, there are ways to use advanced analytics, including AI and ML, to improve performance with fully compliant and explainable credit risk models and scores. WHITE PAPER: Getting AI-driven decisioning right in financial services Developing predictive analytics models Going from historical data to actionable analytics insights can be a long journey. And if you're making major decisions based on a model's predictions, you need to be confident that there aren’t any missteps along the way. Internal and external data scientists can oversee the process of developing, testing and implementing predictive analytics models: Define your goal: Determine the predictions you want to make or problems you want to solve given the constraints you must act within. Collect data: Identify internal and external data sources that house information that could be potentially relevant to your goal. Prepare the data: Clean the data to prepare it for analysis by removing errors or outliers and determining if more data will be helpful. Develop and validate models: Create predictive models based on your data, desired outcomes and regulatory requirements. Deciding which tools and techniques to use during model development is part of the art that goes into the science of predictive analytics. You can then validate models to confirm that they accurately predict outcomes. Deploy the models: Once a model is validated, deploy it into a live environment to start making predictions. Depending on your IT environment, business leaders may be able to easily access the outputs using a dashboard, app or website. Monitor results: Test and monitor the model to ensure it's continually meeting performance expectations. You may need to regularly retrain or redevelop models using training data that better reflects current conditions. Depending on your goals and resources, you may want to start with off-the-shelf predictive models that can offer immediate insights. But if your resources and experience allow, custom models may offer more insights. CASE STUDY: Experian worked with one of the largest retail credit card issuers to develop a custom acquisition model. The client's goal was to quickly replace their outdated custom model while complying with their model governance requirements. By using proprietary attribute sets and a patented advanced model development process, Experian built a model that offered 10 percent performance improvements across segments. Predictive modeling techniques Data scientists can use different modeling techniques when building predictive models, including: Regression analysis: A traditional approach that identifies the most important relationships between two or more variables. Decision trees: Tree-like diagrams  show potential choices and their outcomes. Gradient-boosted trees: Builds on the output from individual decision trees to train more predictive trees by identifying and correcting errors. Random forest: Uses multiple decision trees that are built in parallel on slightly different subsets of the training data. Each tree will give an output, and the forest can analyze all of these outputs to determine the most likely result. Neural networks: Designed to mimic how the brain works to find underlying relationships between data points through repeated tests and pattern recognition. Support vector machines: A type of machine learning algorithm that can classify data into different groups and make predictions based on shared characteristics. Experienced data scientists may know which techniques will work well for specific business needs. However, developing and comparing several models using different techniques can help determine the best fit. Implementation challenges and solutions in predictive analytics Integrating predictive analytics into existing systems presents several challenges that range from technical hurdles to external scrutiny. Here are some common obstacles and practical solutions: Data integration and quality: Existing systems often comprise disparate data sources, including legacy systems that do not easily interact. Extracting high-quality data from these varied sources is a challenge due to inconsistent data formats and quality. Implementing robust data management practices, such as data warehousing and data governance frameworks, ensure data quality and consistency. The use  of APIs can facilitate seamless data integration. Scalability: Predictive business analytics models that perform well in a controlled test environment may not scale effectively across the entire organization. They can suffer from performance issues when deployed on a larger scale due to increased data volumes and transaction rates. Invest in scalable infrastructure, such as cloud-based platforms that can dynamically adjust resources based on demand. Regulatory compliance: Financial institutions are heavily regulated, and any analytics tool must comply with existing laws — such as the Fair Credit Reporting Act in the U.S. — which govern data privacy and model transparency. Including explainable AI capabilities helps to ensure transparency and compliance in your predictive models. Compliance protocols should be regularly reviewed to align with both internal audits and external regulations. Expertise: Predictive analytics requires specialized knowledge in data science, machine learning and analytics. Develop in-house expertise through training and development programs or consider partnerships with analytics firms to bridge the gap. By addressing these challenges with thoughtful strategies, organizations can effectively integrate predictive analytics into their systems to enhance decision-making and gain a competitive advantage. From prediction to prescription While prediction analytics focuses on predicting what may happen, prescription analytics focuses on what you should do next. When combined, you can use the results to optimize decisions throughout your organization. But it all starts with good data and prediction models. Learn more about Experian's predictive modeling solutions. 1Experian (2020). Machine Learning Decisions in Milliseconds *This article includes content created by an AI language model and is intended to provide general information.

 With nearly seven billion credit card and personal loan acquisition mailers sent out last year, consumers are persistently targeted with pre-approved offers, making it critical for credit unions to deliver the right offer to the right person, at the right time. How WSECU is enhancing the lending experience As the second-largest credit union in the state of Washington, Washington State Employees Credit Union (WSECU) wanted to digitalize their credit decisioning and prequalification process through their new online banking platform, while also providing members with their individual, real-time credit score. WSECU implemented an instant credit decisioning solution delivered via Experian’s Decisioning as a ServiceSM environment, an integrated decisioning system that provides clients with access to data, attributes, scores and analytics to improve decisioning across the customer life cycle. Streamlined processes lead to upsurge in revenue growth   Within three months of leveraging Experian’s solution, WSECU saw more members beginning their lending journey through a digital channel than ever before, leading to a 25% increase in loan and credit applications. Additionally, member satisfaction increased with 90% of members finding the simplified process to be more efficient and requiring “low effort.” Read our case study for more insight on using our digital credit solutions to: Prequalify members in real-time at point of contact Match members to the right loan products Increase qualification, approval and take rates Lower operational and manual review costs Read case study

It's easy to ignore a phone call—especially from an unknown number—or delete an email without looking past the subject line. Even physical letters get thrown out without being opened. But nearly everyone will quickly open and read a text. Surveys have repeatedly found text message open rates can range from around 90 to 98 percent. And now, debt collectors that are serious about streamlining operations and connecting with consumers via their preferred channel can integrate text messaging into their process. Learn more Using text messages in debt collection It's been a couple of years since the Consumer Financial Protection Bureau (CFPB) revised Regulation F, which implements the Fair Debt Collection Practices Act (FDCPA). The ruling was effective starting November 2021 and confirmed that debt collectors could use emails, text messages and other digital communication channels. Businesses in many other industries have been communicating with customers by text for years. At a high level, the changes to Regulation F allow debt collectors to add new outreach methods to their debt collection tools. However, even with the go-ahead to communicate via text, strategy and compliance must be top of mind. WATCH: Webinar: Keeping pace with collections compliance changes The move to digital debt collections Incorporating text messaging could be part of a larger shift toward digitizing operations. Some debt collection agencies are also using artificial intelligence, big data and automation to help verify consumers' contact information, assist call center agents and follow up with consumers. As the Experian 2022 Global Insights Report reports, 81 percent of consumers think more highly of brands if they have a positive online experience with that brand that involves multiple digital touchpoints. And over half of consumers trust organizations that use AI.1 Your website or mobile app is an important starting point. And digital tools, such as chatbots that can answer common questions and virtual negotiators offering payment plans, could be part of that experience. Your automated and manual text message outreach could also be increasingly important in the coming years. The benefits of debt collection text messages A text message strategy can be part of an omnichannel approach, and it offers debt collectors a few distinct benefits: Get direct access to consumers who will likely see and read your messages. Allow consumers to respond and ask questions via a channel that may be easier or more comfortable for them than a phone call. Start a two-way dialogue and build rapport. Save time by texting multiple consumers simultaneously and automating responses to common questions. However, collection agencies also need to beware of the potential drawbacks. Consumers might see your texts as a nuisance if you frequently send messages or if you're messaging people who truly can't afford a payment right now. Many consumers are also rightly wary of scammers texting them and asking them to click on a link. You'll want to carefully think through your messaging strategy. Starting by getting consent to send a text message while you're on the phone or when the consumer fills out a form online—and then immediately sending a text with an opt-in—can help overcome this potential barrier. How to leverage debt collection text messages Sending payment requests via text to consumers who have a high propensity to repay, and including a link to self-service payment portals, could offer a quick and easy win. However, it may be best to think through how you'll use text messaging to optimize your outreach rather than replace other communication channels. WATCH: Webinar: Adapting to the new collections landscape Perhaps you've spoken directly with someone and helped them set up a payment plan. You could now use automated texts to remind them of upcoming payment due dates and thank them for their payments. It's a simple way to test the water without sending debt collection-related messages that may fall under stricter regulatory requirements. Staying compliant while texting As part of a highly regulated industry, debt collection agencies must consider compliance. And it's especially important to consider when trying new technology that directly interacts with consumers. Laws and rulings may change, and it's important to consult your counsel before making any decisions or implementing a text message strategy. However, at a high level, the Regulation F requires debt collectors to: Prioritize capturing consent.You must obtain direct consent from a consumer or indirect consent from an original creditor that got the consumer's consent. The initial communication before sending a text or email must be written. Debt collectors that use specific procedures for obtaining consent may receive safe harbor protections against inadvertent disclosures to third parties. Make opting out easy. You must send consumers a clear and conspicuous opt-out notice and offer them a reasonable and simple method to opt out of text messaging or other electronic communications. Debt collectors must identify when they receive an opt-out request, even if the request doesn't follow their specific instructions. For example, if a consumer sends “end," you may need to recognize that as an opt-out even if your opt-out instructions tell them to send “stop." Continue complying with FDCPA harassment guidelines. There's no specific federal limit on how often you can text consumers. However, you'll still need to comply with the FDCPA's general rules regarding harassment and contacting consumers at convenient times. In general, you may want to send texts between 8 a.m. and 9 p.m. local time (for the consumer), unless they request a different time. Limiting how many texts you send can also improve consumers' experiences and may lead to better long-term results. Reconfirm consent every 60 days. Even if consumers don't opt out, the implied or expressed consent you received could only be valid for 60 days. To continue texting a consumer, you may need to have them reconfirm their consent or use a complete and accurate database to confirm that their phone number was not reassigned.2 You may also be subject to more stringent state or local laws. For instance, Washington State laws might prohibit debt collectors from sending more than two texts in a day.3 And Washington, D.C. forbids debt collectors from initiating communications with consumers via written or electronic communications (including text messages) during and for at least 60 days following a public health emergency. READ: A Digital Debt Collection Future: Maximizing Collections and Staying Compliant Partnering with Experian Experian offers access to vast data sources, skip tracing tools for collections and advanced analytical capabilities that help debt collectors move into the digital age. From optimizing outreach with the AI-driven Experian Decisioning to verifying real-time phone ownership using Phone Number ID™ with Contact Monitor™, you can integrate the latest technology while remaining compliant. You can then decide the best ways to use text messages, or other electronic communication methods, to make profitable decisions and maximize recovery rates. Learn more about Experian's debt collection solutions. ¹Experian. (April 2022). Experian 2022 Global Insights Report ²Consumer Financial Protection Bureau. (2023). 1006.6 Communications in connection with debt collection. ³Washington State Legislator. (2023). RCW 19.16.250 Prohibited practices