Tag: ai

Fake IDs have been around for decades, but today’s fraudsters aren’t just printing counterfeit driver’s licenses — they’re using artificial intelligence (AI) to create synthetic identities. These AI fake IDs bypass traditional security checks, making it harder for businesses to distinguish real customers from fraudsters. To stay ahead, organizations need to rethink their fraud prevention solutions and invest in advanced tools to stop bad actors before they gain access. The growing threat of AI Fake IDs   AI-generated IDs aren’t just a problem for bars and nightclubs; they’re a serious risk across industries. Fraudsters use AI to generate high-quality fake government-issued IDs, complete with real-looking holograms and barcodes. These fake IDs can be used to commit financial fraud, apply for loans or even launder money. Emerging services like OnlyFake are making AI-generated fake IDs accessible. For $15, users can generate realistic government-issued IDs that can bypass identity verification checks, including Know Your Customer (KYC) processes on major cryptocurrency exchanges.1 Who’s at risk? AI-driven identity fraud is a growing problem for: Financial services – Fraudsters use AI-generated IDs to open bank accounts, apply for loans and commit credit card fraud. Without strong identity verification and fraud detection, banks may unknowingly approve fraudulent applications. E-commerce and retail – Fake accounts enable fraudsters to make unauthorized purchases, exploit return policies and commit chargeback fraud. Businesses relying on outdated identity verification methods are especially vulnerable. Healthcare and insurance – Fraudsters use fake identities to access medical services, prescription drugs or insurance benefits, creating both financial and compliance risks. The rise of synthetic ID fraud Fraudsters don’t just stop at creating fake IDs — they take it a step further by combining real and fake information to create entirely new identities. This is known as synthetic ID fraud, a rapidly growing threat in the digital economy. Unlike traditional identity theft, where a criminal steals an existing person’s information, synthetic identity fraud involves fabricating an identity that has no real-world counterpart. This makes detection more difficult, as there’s no individual to report fraudulent activity. Without strong synthetic fraud detection measures in place, businesses may unknowingly approve loans, credit cards or accounts for these fake identities. The deepfake threat AI-powered fraud isn’t limited to generating fake physical IDs. Fraudsters are also using deepfake technology to impersonate real people. With advanced AI, they can create hyper-realistic photos, videos and voice recordings to bypass facial recognition and biometric verification. For businesses relying on ID document scans and video verification, this can be a serious problem. Fraudsters can: Use AI-generated faces to create entirely fake identities that appear legitimate Manipulate real customer videos to pass live identity checks Clone voices to trick call centers and voice authentication systems As deepfake technology improves, businesses need fraud prevention solutions that go beyond traditional ID verification. AI-powered synthetic fraud detection can analyze biometric inconsistencies, detect signs of image manipulation and flag suspicious behavior. How businesses can combat AI fake ID fraud Stopping AI-powered fraud requires more than just traditional ID checks. Businesses need to upgrade their fraud defenses with identity solutions that use multidimensional data, advanced analytics and machine learning to verify identities in real time. Here’s how: Leverage AI-powered fraud detection – The same AI capabilities that fraudsters use can also be used against them. Identity verification systems powered by machine learning can detect anomalies in ID documents, biometrics and user behavior. Implement robust KYC solutions – KYC protocols help businesses verify customer identities more accurately. Enhanced KYC solutions use multi-layered authentication methods to detect fraudulent applications before they’re approved. Adopt real-time fraud prevention solutions – Businesses should invest in fraud prevention solutions that analyze transaction patterns and device intelligence to flag suspicious activity. Strengthen synthetic identity fraud detection – Detecting synthetic identities requires a combination of behavioral analytics, document verification and cross-industry data matching. Advanced synthetic fraud detection tools can help businesses identify and block synthetic identities. Stay ahead of AI fraudsters AI-generated fake IDs and synthetic identities are evolving, but businesses don’t have to be caught off guard. By investing in identity solutions that leverage AI-driven fraud detection, businesses can protect themselves from costly fraud schemes while ensuring a seamless experience for legitimate customers. At Experian, we combine cutting-edge fraud prevention, KYC and authentication solutions to help businesses detect and prevent AI-generated fake ID and synthetic ID fraud before they cause damage. Our advanced analytics, machine learning models and real-time data insights provide the intelligence businesses need to outsmart fraudsters. Learn more *This article includes content created by an AI language model and is intended to provide general information. 1 https://www.404media.co/inside-the-underground-site-where-ai-neural-networks-churns-out-fake-ids-onlyfake/

As Valentine’s Day approaches, hearts will melt, but some will inevitably be broken by romance scams. This season of love creates an opportune moment for scammers to prey on individuals feeling lonely or seeking connection. Financial institutions should take this time to warn customers about the heightened risks and encourage vigilance against fraud. In a tale as heart-wrenching as it is cautionary, a French woman named Anne was conned out of nearly $855,000 in a romance scam that lasted over a year. Believing she was communicating with Hollywood star Brad Pitt; Anne was manipulated by scammers who leveraged AI technology to impersonate the actor convincingly. Personalized messages, fabricated photos, and elaborate lies about financial needs made the scam seem credible. Anne’s story, though extreme, highlights the alarming prevalence and sophistication of romance scams in today’s digital age. According to the Federal Trade Commission (FTC), nearly 70,000 Americans reported romance scams in 2022, with losses totaling $1.3 billion—an average of $4,400 per victim. These scams, which play on victims’ emotions, are becoming increasingly common and devastating, targeting individuals of all ages and backgrounds. Financial institutions have a crucial role in protecting their customers from these schemes. The lifecycle of a romance scam Romance scams follow a consistent pattern: Feigned connection: Scammers create fake profiles on social media or dating platforms using attractive photos and minimal personal details. Building trust: Through lavish compliments, romantic conversations, and fabricated sob stories, scammers forge emotional bonds with their targets. Initial financial request: Once trust is established, the scammer asks for small financial favors, often citing emergencies. Escalation: Requests grow larger, with claims of dire situations such as medical emergencies or legal troubles. Disappearance: After draining the victim’s funds, the scammer vanishes, leaving emotional and financial devastation in their wake. Lloyds Banking Group reports that men made up 52% of romance scam victims in 2023, though women lost more on average (£9,083 vs. £5,145). Individuals aged 55-64 were the most susceptible, while those aged 65-74 faced the largest losses, averaging £13,123 per person. Techniques scammers use Romance scammers are experts in manipulation. Common tactics include: Fabricated sob stories: Claims of illness, injury, or imprisonment. Investment opportunities: Offers to “teach” victims about investing. Military or overseas scenarios: Excuses for avoiding in-person meetings. Gift and delivery scams: Requests for money to cover fake customs fees. How financial institutions can help Banks and financial institutions are on the frontlines of combating romance scams. By leveraging technology and adopting proactive measures, they can intercept fraud before it causes irreparable harm. 1. Customer education and awareness Conduct awareness campaigns to educate clients about common scam tactics. Provide tips on recognizing fake profiles and unsolicited requests. Share real-life stories, like Anne’s, to highlight the risks. 2. Advanced data capture solutions Implement systems that gather and analyze real-time customer data, such as IP addresses, browsing history, and device usage patterns. Use behavioral analytics to detect anomalies in customer actions, such as hesitation or rushed transactions, which may indicate stress or coercion. 3. AI and machine learning Utilize AI-driven tools to analyze vast datasets and identify suspicious patterns. Deploy daily adaptive models to keep up with emerging fraud trends. 4. Real-time fraud interception Establish rules and alerts to flag unusual transactions. Intervene with personalized messages before transfers occur, asking “Do you know and trust this person?” Block transactions if fraud is suspected, ensuring customers’ funds are secure. Collaborating for greater impact Financial institutions cannot combat romance scams alone. Partnerships with social media platforms, AI companies, and law enforcement are essential. Social media companies must shut down fake profiles proactively, while regulatory frameworks should enable banks to share information about at-risk customers. Conclusion Romance scams exploit the most vulnerable aspects of human nature: the desire for love and connection. Stories like Anne’s underscore the emotional and financial toll these scams take on victims. However, with robust technological solutions and proactive measures, financial institutions can play a pivotal role in protecting their customers. By staying ahead of fraud trends and educating clients, banks can ensure that the pursuit of love remains a source of joy, not heartbreak. Learn more

In today's evolving financial landscape and with delinquincies rising, debt collection remains a critical function for financial institutions. However, traditional methods often fall short in efficiency and customer satisfaction. Enter artificial intelligence (AI), a game-changer poised to revolutionize the debt collection industry. This blog post explores the benefits and uses of AI in debt collection, shedding light on how financial institutions can leverage this technology to enhance their strategies. Understanding AI in debt collection Artificial intelligence – which encompasses machine learning, natural language processing, and other advanced technologies – is transforming various industries, including debt collection. AI in debt collection involves using these technologies to automate and optimize processes, making them more efficient and effective. Examples of AI technologies in debt collection include chatbots, predictive analytics, and automated communication systems. Uses Predictive analytics Predictive debt collection analytics is a powerful tool in AI collections. By analyzing patterns and trends in debtor behavior, AI can forecast the likelihood of repayment. This information allows financial institutions to tailor their collection strategies to individual debtors, improving the chances of successful recovery. Chatbots and virtual assistants AI-powered chatbots and virtual assistants handle routine customer interactions, providing instant responses to common queries. These tools can escalate complex issues to human agents when necessary, ensuring that customers receive the appropriate level of support. By automating routine tasks, chatbots free up human agents to focus on more complex cases. Automated communication AI can automate communication with debtors, sending payment reminders and notifications through various channels such as email, SMS, and phone calls. These messages can be customized based on debtor profiles, ensuring that communication is personalized and effective. Automated communication helps maintain consistent contact with debtors, increasing the likelihood of timely payments. Benefits Improved operational efficiency One of the most significant advantages of AI in debt collection is improved operational efficiency. AI can automate repetitive tasks such as sending payment reminders and processing payments, reducing the need for manual intervention. This automation speeds up the process, reduces costs, and minimizes human errors, ensuring more accurate and timely collections. Enhanced customer experience AI-driven chatbots and virtual assistants can provide personalized communication, enhancing the customer experience. These AI tools are available 24/7, allowing customers to get instant responses to their queries at any time. By offering a seamless and responsive service, financial institutions can improve customer satisfaction and engagement strategies. Better decision making AI collections leverage predictive analytics to assess debtor risk and provide data-driven insights. This information enables financial institutions to develop more effective collection strategies and prioritize high-risk accounts. By making informed decisions based on predictive models, institutions can optimize collections processes and increase their chances of successful debt recovery. Cost savings Automation through AI can lead to significant cost savings. Financial institutions can achieve higher profitability by reducing the need for human intervention and lowering operational costs. Additionally, increased recovery rates due to better cure strategies contribute to overall cost efficiency. Challenges and considerations While AI offers numerous benefits, there are challenges and considerations to keep in mind. Data privacy and security are paramount, as financial institutions must ensure compliance with regulations such as General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). Balancing automation with the need for a human touch is also crucial, as some customers may prefer interacting with human agents. Additionally, addressing potential biases in AI algorithms is essential to ensure fair and equitable treatment of all debtors. Future Trends in AI and debt collection The future of AI in debt collection looks promising, with emerging technologies poised to make a significant impact. Integration of AI with other technologies such as blockchain and the Internet of Things (IoT) could further enhance the efficiency and security of debt collection processes. As AI continues to evolve, financial institutions must stay abreast of these trends to remain competitive and effective in their collection strategies. Our debt management and collection solutions With more than 25 years of experience and a comprehensive suite of collection products, our enhanced decisioning, improved processes, and account prioritization can enable your organization to move toward a customer-centric approach that helps reduce losses and control costs. AI in debt collection offers a myriad of benefits, from improved efficiency and enhanced customer experience to better decision-making and cost savings. By leveraging AI technologies such as predictive analytics, chatbots, and automated communication, financial institutions can optimize their debt collection strategies and achieve higher recovery rates. As the industry continues to evolve, embracing AI will be crucial for financial institutions looking to stay ahead of the curve. Click below to learn more about how we can help your organization optimize your debt collection strategies to lose less and recover more. Learn more Watch our webinar on-demand This article includes content created by an AI language model and is intended to provide general information.

Scott Brown presents at Reuters Next “If I were to look into a crystal ball, traditional lending methodology and processes will not be replaced; they will be augmented by consumers connecting to banks via APIs, contributing the data they are comfortable with, and banks using that in conjunction with historical credit data to offer newer and better products they didn’t have access to before. The convergence of data, tech and AI leads to more financial inclusion and a more modern way of underwriting.”Scott Brown, Group President Financial Services, Experian North America Scott Brown, Group President of Financial Services for Experian North America, recently presented at Reuters Next discussing the transformative power of AI and data analytics in financial services. The session also covered the top challenges that financial institutions face today and how advances in technology are helping organizations overcome those challenges. This keynote presentation was a timely follow-up to Brown’s previous appearance at the Money20/20 conference in Las Vegas, where he revealed the details of Experian’s latest innovation in GenAI technology, Experian Assistant. Brown, in a conversation with TV writer, producer and anchor Del Irani, spoke about the ethical considerations of AI innovation, what the future of underwriting may look like, and how open banking can drive financial inclusion and have a significant positive impact on both businesses and consumers. “If you are extending a line of credit to a given consumer, how do you do so in a way that’s integrated into their everyday lives? That’s where the concept of embedded finance comes in, and how to embed finance into a consumer’s life, and not the other way around.”Scott Brown, Group President Financial Services, Experian North America By embedding finance into consumers’ lives, and not the other way around, organizations can develop better strategies to balance risk and generate more revenue. He also focused on three foundational steps to take advantage of the capabilities AI offers: data quality, transparency, and responsibility. Areas of focus for implementing AI As organizations rely on more sophisticated approaches, data quality inputs are more important than ever. Inaccurate data can lead to poor business decisions that can have a negative impact on organizations’ bottom line. Transparency is also a crucial component of implementing AI solutions. Companies should be able to explain how their models work and why the end results make sense while avoiding biases. Leveraging data with AI tools allows organizations to get a better view of the consumer, which is a goal of most banks and lending institutions. Using that consumer data responsibly is important for financial institutions to establish and maintain trust with the people who use their services. While incorporating AI solutions into everyday business operations is important for financial institutions to better serve their consumers and remain competitive in the industry, a lack of access to AI tools can prevent some organizations from doing so. A fragmented approach leads to higher costs, lower efficiency, and greater risk Until recently, financial institutions have had to rely on several different technology providers and tools to optimize customer experience and operational efficiency while protecting consumers from the risk of identity theft and fraud. This fragmented approach can result in increased costs for organizations and higher risk for consumers. Now, AI technology is solving this issue by integrating functionality into a single platform, such as the Experian Ascend Technology Platform™. This streamlined access to a comprehensive suite of tools can help accelerate time-to-value while also eliminating compliance risks. Full interview available now Brown’s full interview at Reuters Next reveals more details about how Experian is empowering organizations to better serve their consumers’ financial needs through AI innovation while also helping more than 100 million Americans who don’t have access to the mainstream credit ecosystem due to being credit invisible, unscoreable, or have a low credit score. Watch the full interview to learn more about how Experian is continuing to bring financial power to all through innovative technology. Watch the full interview now

For businesses across all sectors solutions that improve productivity are more important than ever. As technology advances, organizations across industries are looking to capitalize by investing in artificial intelligence (AI) solutions. Studies have recently shown that productivity is a leading measure of how well these AI tools are performing. About 60% of organizations surveyed are using “improved productivity” as a metric to measure the success of implementing AI solutions.[1] Experian research shows it takes an average of 15 months to build a model and put it into production. This can hinder productivity and the ability to quickly go to market. Without a deep understanding of key data points, organizations may also have difficulty realizing time to value efficiently. To improve upon the modeling lifecycle, businesses must examine the challenges involved in the process. The challenges of model building One of the most significant challenges of the modeling lifecycle is speed. Slow modeling processes can cause delays and missed opportunities for businesses which they may have otherwise capitalized on. Another difficulty organizations face is having limited access to high-quality data to build more efficient models. Without the right data, businesses can miss out on actionable insights that could give them a competitive edge. In addition, when organizations have inefficient resources, expenses can skyrocket due to the need for experts to intervene and address ongoing issues. This can result in a steep learning curve as new tools and platforms are adopted, making it difficult for organizations to operate efficiently without outside help. Businesses can combat these challenges by implementing tools such as artificial intelligence (AI) to drive efficiency and productivity. The AI journey While generative AI and large language models are becoming more prevalent in everyday life, the path to incorporating a fully functional AI tool into an organization’s business operations involves multiple steps. Beginning with a proof of concept, many organizations start their AI journey with building ideas and use cases, experimentation, and identifying and mitigating potential pitfalls, such as inaccurate or irrelevant information. Once a proof of concept reaches an acceptable state of validity, organizations can move on to production and value at scale. During this phase, organizations will select specific use cases to move into production and measure their performance. Analyzing the results can help businesses glean valuable information about which techniques work most effectively, so they can apply those techniques to new use cases. Following successful iterations of an efficiently functioning AI, the organization can then implement that AI as a part of their business by working the technology into everyday operations. This can help organizations drive productivity at scale across various business processes. Experian’s AI journey has been ongoing, with years of expertise in implementing AI into various products and services. With a goal of providing greater insights to both businesses and consumers while adhering to proper consumer data privacy and compliance, Experian is committed to responsibly using AI to combat fraud and foster greater financial access and inclusion. Our most recent AI innovation, Experian Assistant, is redefining how financial organizations improve productivity with data-driven insights. Introducing Experian Assistant Experian Assistant, a new GenAI tool announced in October at Money20/20 in Las Vegas, is helping organizations take their productivity to the next level by drastically speeding up the modeling lifecycle. To drive automation and greater intelligence for Experian partners, Experian Assistant enables users to interact with a virtual assistant in real time and offers customized guidance and code generation for our suite of software solutions. Our experts – Senior Director of Product Management Ankit Sinha and Director of Analyst Relations Erin Haselkorn – recently revealed the details of how Experian Assistant can cut down model-development timelines from months to days, and in some cases even hours. The webinar, which took place on November 7th, covered a wide range of features and benefits of the new tool, including:  Spending less time writing code  Enhancing understanding of data and attributes Accelerating time to value Improving regulatory compliance A case study in building models faster Continental Finance Company, LLC’s Chief Data Scientist shared their experience using Experian Assistant and how it has improved their organization’s modeling capabilities: “With Experian Assistant, there is a lot of efficiency and improvement in productivity. We have reduced the time spent on data building by almost 75%, so we can build a model much quicker, and the code being generated by Experian Assistant is very high quality, enabling us to move forward much faster.” For businesses looking to accelerate their modeling lifecycle and move more quickly with less effort, Experian Assistant provides a unique opportunity to significantly improve productivity and efficiency. Experian Assistant tech showcase Did you miss the Experian Assistant Tech Showcase webinar? Watch it on demand here and visit our website to learn more. Visit our website [1] Forrester’s Q2 AI Pulse Survey, 2024

The advent of artificial intelligence (AI) is significantly transforming the landscape of real estate fraud, enabling criminals to execute complex schemes like deed theft with greater ease. A notable case involves Spelling Manor, a $137.5 million mansion in Los Angeles, where the owner alleges they are entangled in deed fraud. Scammers reportedly filed fraudulent documents that have prevented the owner from selling the estate, thwarting offers from buyers, including former Google CEO Eric Schmidt. Understanding deed/title fraud Deed fraud, also known as title or property fraud, occurs when someone illegally transfers ownership of a property without the owner’s knowledge or consent. Typically, fraudsters create fake documents or forge the owner’s signature on a deed to make it look like the property has been legally transferred to them. Once the title is in their name, they may try to sell or mortgage the property, leaving the original owner unaware until it’s too late. How deed fraud works Identify a target: Fraudsters often look for properties that appear vulnerable, such as vacant land, unoccupied homes, or properties owned by elderly individuals who may not check their records frequently. Forge documentation: Using fake IDs and forged signatures, scammers create documents that appear to show a legitimate transfer of ownership. With modern technology, these documents can look highly convincing. Record the fake deed: Fraudsters then file these documents with the local county clerk or recorder’s office. This officially changes the ownership records, making it seem as if the scammer is the legitimate owner. Exploit the ownership: Once listed as the owner, the fraudster may sell the property to an unsuspecting buyer, take out loans against it, or even rent it out. The impact on victims In the summer of 2024, Elvis Presley’s family got confronted to a forged deed scam. A fake firm, Naussany Investments, falsely claimed Lisa Marie Presley owed millions and used Graceland as collateral. They placed a foreclosure notice and attempted to auction the estate. Riley Keough filed a lawsuit, exposing the firm as fraudulent and halting the foreclosure through a judge’s injunction. Lisa Jeanine Findley, who forged documents and posed as firm employees, was arrested and charged with deed forgery fraud and identity theft. She faces up to 22 years in prison if convicted.  The FBI's Internet Crime Complaint Center does not specifically monitor deed fraud. However, in 2023, it processed a total of 9,521 real estate-related complaints defined as the loss of funds from a real estate investment, resulting in more than $145 million in losses. Victims of deed fraud can face severe financial and legal issues. They may discover the fraud only when trying to sell, refinance, or even pay taxes on the property. Reversing deed fraud typically requires a costly and time-consuming legal process, as courts must determine that the transfer was fraudulent and restore the original owner’s rights. Prevention and safeguards There are several preventive measures and fraud prevention solutions that can be established to help mitigate the risks associated with deed/title fraud. These include: For lending institutions: Enhanced ID verification: Implement multi-factor identity checks at the loan approval stage. Regular portfolio audits: Conduct periodic audits to detect unusual property transfers and title changes in their loan portfolios. For title companies: AI-driven document verification tools: Use machine learning algorithms to identify inconsistencies in deed and ownership documents. Real-time fraud monitoring: Employ analytics to track suspicious behavior patterns, such as rapid ownership changes. Seller authentication: Require biometric or multi-step identity verification for anyone claiming ownership or initiating sales. For realtors: Training and awareness: Educate realtors on how to spot warning signs of fraudulent listings and seller impersonations. Pre-transaction verification: Collaborate with title companies to validate ownership early in the listing process. 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 deed/title-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 powers 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. Visit our website to see how these solutions can help your business prevent deed fraud. Learn more

Getting customers to respond to your credit offers can be difficult. With the advent of artificial intelligence (AI) and machine learning (ML), optimizing credit prescreen campaigns has never been easier or more efficient. In this post, we'll explore the basics of prescreen and how AI and ML can enhance your strategy.  What is prescreen?  Prescreen involves evaluating potential customers to determine their eligibility for credit offers. This process takes place without the consumer’s knowledge and without any negative impact on their credit score.  Why optimize your prescreen strategy?  In today's financial landscape, having an optimized prescreen strategy is crucial. Some reasons include:  Increased competition: Financial institutions face stiff competition in acquiring new customers. An optimized prescreen strategy helps you stand out by targeting the right individuals with tailored offers, increasing the chances of conversion.  Customer expectations: Modern customers expect personalized and relevant offers. An effective prescreen strategy ensures that your offers resonate with the specific needs and preferences of potential customers.  Strict budgets: Organizations today are faced with a limited marketing budget. By determining the right consumers for your offers, you can minimize prescreen costs and maximize the ROI of your campaigns.  Regulatory compliance: Compliance with regulations such as the Fair Credit Reporting Act (FCRA) is essential. An optimized prescreen strategy helps you stay compliant by ensuring that only eligible individuals are targeted for credit offers.  Financial inclusion: 49 million American adults don’t have conventional credit scores. An optimized prescreen strategy allows you to send offers to creditworthy consumers who you may have missed due to a lack of traditional credit history.  How AI and ML can enhance your strategy  AI and ML can revolutionize your prescreen strategy by offering advanced analytics and custom response modeling capabilities.  AI-driven data analytics  AI analytics allow financial institutions to analyze vast amounts of data quickly and accurately. This enables you to identify patterns and trends that may not be apparent through traditional analysis. By leveraging data-centric AI, you can gain deeper insights into customer behavior and preferences, allowing for more precise targeting and increased response rates.  LEARN MORE: Explore the benefits of AI for credit unions.  Custom response modeling  Custom response models enable you to better identify individuals who fall within your credit criteria and are more likely to respond to your credit offers. These models consider various factors such as credit history, spending habits, and demographic information to predict future behavior. By incorporating custom response models into your prescreen strategy, you can select the best consumers to engage, including those you may have previously overlooked.  LEARN MORE: AI can be leveraged for numerous business needs. Learn about generative AI fraud detection.   Get started today  Incorporating AI and ML into your prescreen campaigns can significantly enhance their effectiveness and efficiency. By leveraging Experian's Ascend Intelligence Services™ Target, you can better target potential customers and maximize your marketing spend.   Our optimized prescreen solution leverages:  Full-file credit bureau data on over 245 million consumers and over 2,100 industry-leading credit attributes.  Exclusive access to the industry's largest alternative datasets from nontraditional lenders, rental data inputs, full-file public records, and more.  24 months of trended data showing payment patterns over time and over 2,000 attributes that help determine your next best action.  When it comes to compliance, Experian leverages decades of regulatory experience to provide the documentation needed to explain lending practices to regulators. We use patent-pending ML explainability to understand what contributed most to a decision and generate adverse action codes directly from the model.  For more insights into Ascend Intelligence Services Target, view our infographic or contact us at 855 339 3990. View infographic This article includes content created by an AI language model and is intended to provide general information. 

Experian’s award-winning platform now brings together market-leading data, generative AI and cutting-edge machine learning solutions for analytics, credit decisioning and fraud into a single interface — simplifying the deployment of analytical models and enabling businesses to optimize their practices. The platform updates represent a notable milestone, fueled by Experian’s significant investments in innovation over the last eight years as part of its modern cloud transformation.  “The evolution of our platform reaffirms our commitment to drive innovation and empower businesses to thrive. Its capabilities are unmatched and represent a significant leap forward in lending technology, democratizing access to data in compliant ways while enabling lenders of all sizes to seamlessly validate their customers’ identities with confidence, help expand fair access to credit and offer awesome user and customer experiences,” said Alex Lintner CEO Experian Software Solutions. The enhanced Experian Ascend Platform dramatically reduces time to install and offers streamlined access to many of Experian's award-winning integrated solutions and tools through a single sign-on and a user-friendly dashboard. Leveraging generative AI, the platform makes it easy for organizations of varying sizes and experience levels to pivot between applications, automate processes, modernize operations and drive efficiency. In addition, existing clients can easily add new capabilities through the platform to enhance business outcomes. Read Press Release Learn More Check out Experian Ascend Platform in the media: Transforming Software for Credit, Fraud and Analytics with Experian Ascend Platform™ (Episode 160) Reshaping the Future of Financial Services with Experian Ascend Platform Introducing Experian’s Cloud-based Ascend Technology Platform with GenAI Integration 7 enhancements of Experian Ascend Platform

Model explainability has become a hot topic as lenders look for ways to use artificial intelligence (AI) to improve their decision-making. Within credit decisioning, machine learning (ML) models can often outperform traditional models at predicting credit risk.  ML models can also be helpful throughout the customer lifecycle, from marketing and fraud detection to collections optimization. However, without explainability, using ML models may result in unethical and illegal business practices.  What is model explainability?  Broadly defined, model explainability is the ability to understand and explain a model's outputs at either a high level (global explainability) or for a specific output (local explainability).1  Local vs global explanation: Global explanations attempt to explain the main factors that determine a model's outputs, such as what causes a credit score to rise or fall. Local explanations attempt to explain specific outputs, such as what leads to a consumer's credit score being 688. But it's not an either-or decision — you may need to explain both.  Model explainability can also have varying definitions depending on who asks you to explain a model and how detailed of a definition they require. For example, a model developer may require a different explanation than a regulator.  Model explainability vs interpretability  Some people use model explainability and interpretability interchangeably. But when the two terms are distinguished, model interpretability may refer to how easily a person can understand and explain a model's decisions.2 We might call a model interpretable if a person can clearly understand:  The features or inputs that the model uses to make a decision.  The relative importance of the features in determining the outputs.  What conditions can lead to specific outputs.  Both explainability and interpretability are important, especially for credit risk models used in credit underwriting. However, we will use model explainability as an overarching term that encompasses an explanation of a model's outputs and interpretability of its internal workings below.  ML models highlight the need for explainability in finance  Lenders have used credit risk models for decades. Many of these models have a clear set of rules and limited inputs, and they might be described as self-explanatory. These include traditional linear and logistic regression models, scorecards and small decision trees.3  AI analytics solutions, such as ML-powered credit models, have been shown to better predict credit risk. And most financial institutions are increasing their budgets for advanced analytics solutions and see their implementation as a top priority.4  However, ML models can be more complex than traditional models and they introduce the potential of a “black box." In short, even if someone knows what goes into and comes out of the model, it's difficult to explain what's happening without an in-depth analysis.  Lenders now have to navigate a necessary trade-off. ML-powered models may be more predictive, but regulatory requirements and fair lending goals require lenders to use explainable models.  READ MORE: Explainability: ML and AI in credit decisioning  Why is model explainability required?  Model explainability is necessary for several reasons:  To comply with regulatory requirements: Decisions made using ML models need to comply with lending and credit-related, including the Fair Credit Reporting Act (FCRA) and Equal Credit Opportunity Act (ECOA). Lenders may also need to ensure their ML-driven models comply with newer AI-focused regulations, such as the AI Bill of Rights in the U.S. and the E.U. AI Act.  To improve long-term credit risk management: Model developers and risk managers may want to understand why decisions are being made to audit, manage and recalibrate models.  To avoid bias: Model explainability is important for ensuring that lenders aren't discriminating against groups of consumers.  To build trust: Lenders also want to be able to explain to consumers why a decision was made, which is only possible if they understand how the model comes to its conclusions.  There's a real potential for growth if you can create and deploy explainable ML models. In addition to offering a more predictive output, ML models can incorporate alternative credit data* (also known as expanded FCRA-regulated data) and score more consumers than traditional risk models. As a result, the explainable ML models could increase financial inclusion and allow you to expand your lending universe.  READ MORE: Raising the AI Bar  How can you implement ML model explainability?  Navigating the trade-off and worries about explainability can keep financial institutions from deploying ML models. As of early 2023, only 14 percent of banks and 19 percent of credit unions have deployed ML models. Over a third (35 percent) list explainability of machine learning models as one of the main barriers to adopting ML.5  Although a cautious approach is understandable and advisable, there are various ways to tackle the explainability problem. One major differentiator is whether you build explainability into the model or try to explain it post hoc—after it's trained.  Using post hoc explainability  Complex ML models are, by their nature, not self-explanatory. However, several post hoc explainability techniques are model agnostic (they don't depend on the model being analyzed) and they don't require model developers to add specific constraints during training.  Shapley Additive Explanations (SHAP) is one used approach. It can help you understand the average marginal contribution features to an output. For instance, how much each feature (input) affected the resulting credit score.  The analysis can be time-consuming and expensive, but it works with black box models even if you only know the inputs and outputs. You can also use the Shapley values for local explanations, and then extrapolate the results for a global explanation.  Other post hoc approaches also might help shine a light into a black box model, including partial dependence plots and local interpretable model-agnostic explanations (LIME).  READ MORE: Getting AI-driven decisioning right in financial services  Build explainability into model development  Post hoc explainability techniques have limitations and might not be sufficient to address some regulators' explainability and transparency concerns.6 Alternatively, you can try to build explainability into your models. Although you might give up some predictive power, the approach can be a safer option.  For instance, you can identify features that could potentially lead to biased outcomes and limit their influence on the model. You can also compare the explainability of various ML-based models to see which may be more or less inherently explainable. For example, gradient boosting machines (GBMs) may be preferable to neural networks for this reason.7  You can also use ML to blend traditional and alternative credit data, which may provide a significant lift — around 60 to 70 percent compared to traditional scorecards — while maintaining explainability.8  READ MORE: Journey of an ML Model  How Experian can help  As a leader in machine learning and analytics, Experian partners with financial institutions to create, test, validate, deploy and monitor ML-driven models. Learn how you can build explainable ML-powered models using credit bureau, alternative credit, third-party and proprietary data. And monitor all your ML models with a web-based platform that helps you track performance, improve drift and prepare for compliance and audit requests. *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 and can be used interchangeably.  1-3. FinRegLab (2021). The Use of Machine Learning for Credit Underwriting  4. Experian (2022). Explainability: ML and AI in credit decisioning  5. Experian (2023). Finding the Lending Diamonds in the Rough  6. FinRegLab (2021). The Use of Machine Learning for Credit Underwriting  7. Experian (2022). Explainability: ML and AI in credit decisioning  8. Experian (2023). Raising the AI Bar 

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

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.

From chatbots to image generators, artificial intelligence (AI) has captured consumers' attention and spurred joy — and sometimes a little fear. It's not too different in the business world. There are amazing opportunities and lenders are increasingly turning to AI-driven lending decision engines and processes. But there are also open questions about how AI can work within existing regulatory requirements, how new regulations will impact its use and how to implement advanced analytics in a way that increases equitable inclusion rather than further embedding disparities. How are lenders using AI today? Many financial institutions have implemented — or at least tested — AI-driven tools throughout the customer lifecycle to: Target the right consumers: With tools like Ascend Intelligence ServicesTM Target (AIS Target), lenders can better identify consumers who match their credit criteria and send right-sized offers, which enables them to maximize their acceptance rates. Detect and prevent fraud: Fraud detection tools have used AI and machine learning techniques to detect and prevent fraud for years. These systems may be even more important as new fraud risks emerge, from tried-and-true methods to generative AI (GenAI) fraud. Assess creditworthiness: ML-based models can incorporate a range of internal and external data points to more precisely evaluate creditworthiness. When combined with traditional and alternative credit data*, some lenders can even see a Gini uplift of 60 to 70 percent compared to a traditional credit risk model. Manage portfolios: Lenders can also use a more complete picture of their current customers to make better decisions. For example, AI-driven models can help lenders set initial credit limits and suggest when a change could help them increase wallet share or reduce risk. Lenders can also use AI to help determine which up- and cross-selling offers to present and when (and how) to reach out. Improve collections: Models can be built to ease debt collection processes, such as choosing where to assign accounts, which accounts to prioritize and how to contact the consumer. Additionally, businesses can implement AI-powered tools to increase their organizations' productivity and agility. GenAI solutions like Experian Assistant accelerate the modeling lifecycle by providing immediate responses to questions, enhancing model transparency and parsing through multiple model iterations quickly, resulting in streamlined workflows, improved data visibility and reduced expenses. WATCH: Explore best practices for building, fine-tuning and deploying robust machine learning models for credit risk. The benefits of AI in lending Although lenders can use machine learning models in many ways, the primary drivers for adoption in underwriting include: Improving credit risk assessment Faster development and deployment cycles for new or recalibrated models Unlocking the possibilities within large datasets Keeping up with competing lenders Some of the use cases for machine learning solutions have a direct impact on the bottom line — improving credit risk assessment can decrease charge-offs. Others are less direct but still meaningful. For instance, machine learning models might increase efficiency and allow further automation. This takes the pressure off your underwriting team, even when application volume is extremely high, and results in faster decisions for applicants, which can improve your customer experience. Incorporating large data sets into their decisions also allows lenders to expand their lending universe without taking on additional risk. For example, they may now be able to offer risk-appropriate credit lines to consumers that traditional scoring models can't score. And machine learning solutions can increase customer lifetime value when they're incorporated throughout the customer lifecycle by stopping fraud, improving retention, increasing up- or cross-selling and streamlining collections. Hurdles to adoption of machine learning in lending There are clear benefits and interest in machine learning and analytics, but adoption can be difficult, especially within credit underwriting. A recent Forrester Consulting study commissioned by Experian found that the top pain points for technology decision makers in financial services were reported to be automation and availability of data. Explainability comes down to transparency and trust. Financial institutions have to trust that machine learning models will continue to outperform traditional models to make them a worthwhile investment. The models also have to be transparent and explainable for financial institutions to meet regulatory fair lending requirements. A lack of resources and expertise could hinder model development and deployment. It can take a long time to build and deploy a custom model, and there's a lot of overhead to cover during the process. Large lenders might have in-house credit modeling teams that can take on the workload, but they also face barriers when integrating new models into legacy systems. Small- and mid-sized institutions may be more nimble, but they rarely have the in-house expertise to build or deploy models on their own. The models also have to be trained on appropriate data sets. Similar to model building and deployment, organizations might not have the human or financial resources to clean and organize internal data. And although vendors offer access to a lot of external data, sometimes sorting through and using the data requires a large commitment. How Experian is shaping the future of AI in lending Lenders are finding new ways to use AI throughout the customer lifecycle and with varying types of financial products. However, while the cost to create custom machine learning models is dropping, the complexities and unknowns are still too great for some lenders to manage. But that's changing. Experian built the Ascend Intelligence Services™ to help smaller and mid-market lenders access the most advanced analytics tools. The managed service platform can significantly reduce the cost and deployment time for lenders who want to incorporate AI-driven strategies and machine learning models into their lending process. The end-to-end managed analytics service gives lenders access to Experian's vast data sets and can incorporate internal data to build and seamlessly deploy custom machine learning models. The platform can also continually monitor and retrain models to increase lift, and there's no “black box" to obscure how the model works. Everything is fully explainable, and the platform bakes regulatory constraints into the data curation and model development to ensure lenders stay compliant. Learn more * When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions as regulated by the Fair Credit Reporting Act (FCRA). Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably.

As credit volumes recover from lows observed in 2021, lenders face new challenges – from increasing demand in customer expectations, to heightened competition, market volatility and a fierce war on talent. Many lenders have incorporated the foundational elements of credit analytics and seen significant initial returns. Now, it’s time for lenders to unlock even greater growth opportunities and operational efficiencies by exploring AI-powered solutions. Experian presented on a recent webinar hosted by Lendit Fintech, where Srikanth Geedipali, Senior Vice President of Global Analytics and AI for Experian, joined a panel of industry experts with representation from OPY and Citibank, to speak on how lenders can differentiate themselves by unlocking the power of advanced technologies such as AI and ML to address these emerging challenges. Watch the full webinar, NextGen Applications of AI in the Credit Lifecycle, and learn more about: Emerging trends in the AI/ML space that will drive innovation and differentiated solutions Use-cases for AI/ML across the lending lifecycle and how to leverage MLOps to industrialize analytics and improve speed and agility of decision-making How advanced technologies have driven impact for lenders of all sizes     This webinar is a part of Lendit Fintech’s webinar series. To learn more about how leveraging AI/ML can help optimize your lending strategies, contact us today. Learn more about Ascend Intelligence Services

Artificial intelligence is here to stay, and businesses who are adopting the newest AI technology are ahead of the game. From targeting the right prospects to designing effective collections efforts, AI-driven strategies across the entire customer lifecycle are no longer a nice to have - they are a must.  Many organizations are late to the game of AI and/or are spending too much time and money designing and redesigning models and deploying them over weeks and months. By the time these models are deployed, markets may have already shifted again, forcing strategy teams to go back to the drawing board. And if these models and strategies are not being continuously monitored, they can become less effective over time and lead to missed opportunities and lost revenue. By implementing artificial intelligence in predictive modeling and strategy optimization, financial institutions and lenders can design and deploy their decisioning strategies faster than ever before and make incremental changes on the fly to adapt to evolving market trends.  While most organizations say they want to incorporate artificial intelligence and machine learning into their business strategy, many do not know where to start. Targeting, portfolio management, and collections are some of the top use cases for AI/ML strategy initiatives.  Targeting  One way businesses are using AI-driven modeling is for targeting the audiences that will most likely meet their credit criteria and respond to their offers. Financial institutions need to have the right data to inform a decisioning strategy that recognizes credit criteria, can respond immediately when prospects meet that criteria and can be adjusted quickly when those factors change. AI-driven response models and optimized decision strategies perform these functions seamlessly, giving businesses the advantage of targeting the right prospects at the right time.  Credit portfolio management  Risk models optimized with artificial intelligence and machine learning, built on comprehensive data sets, are being used by credit lenders to acquire new revenue and set appropriate balance limits. Strategies built around AI-driven risk models enable businesses to send new offers and cross-sell offers to current customers, while appropriately setting initial credit limits and managing limits over time for increased wallet share and reduced risk.   Collections  AI- and ML-driven analytics models are also optimizing collections strategies to improve recovery rates. Employing AI-powered balance and response models, credit lenders can make smarter collections decisions based on the most predictive and accurate information available.   For lending businesses who are already tight on resources, or those whose IT teams cannot meet the demand of quickly adapting to ever-changing market conditions and decisioning criteria, a managed service for AI-powered models and strategy design might be the best option. Managed service teams work closely with businesses to determine specific use cases, develop models to meet those use cases, deploy models quickly, and monitor models to ensure they keep producing and predicting optimally.  Experian offers Ascend Intelligence Services, the only managed service solution to provide data, analytics, strategy and performance monitoring. Experian’s data scientists provide expert guidance as they collaborate with businesses in developing and deploying models and strategies around targeting, acquisitions, limit-setting, and collections. Once those strategies are deployed, Experian continually monitors model health to ensure scores are still predictive and presents challenger models so credit lenders can always have the most accurate decisioning models for their business. Ascend Intelligence Services provides an online dashboard for easy visibility, documentation for regulatory compliance, and cloud capabilities to deliver scores and decisions in real-time.  Experian’s Ascend Intelligence Services makes getting into the AI game easy. Start realizing the power of data and AI-driven analytics models by using our ROI calculator below: initIframe('611ea3adb1ab9f5149cf694e'); For more information about Ascend Intelligence Services, visit our webpage or join our upcoming webinar on October 21, 2021.  Learn more Register for webinar

The COVID-19 pandemic has created shifting economic conditions and rapidly evolving consumer preferences. Lenders must keep up by re-evaluating their strategies to accelerate growth and beat the competition. Here's how AI/ML can help your organization evolve post-COVID-19: With the democratization of AI/ML, lenders of all sizes can now use this technology to grow their lending and optimize for strategic growth. Register for our upcoming webinar to see how lenders like Elevate have incorporated this new technology into their business processes. Register now