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This series will dive into our monthly State of the Economy report, providing a snapshot of the top monthly economic and credit data for those in financial services to proactively shape their business strategies.  As 2024 unfolds, the economy is beginning to shift from last year’s trends. Instead of focusing on rate hikes, we’re looking at the potential for rate cuts. Our labor market is beginning to ease, and inflation is moving closer to the Federal Reserve’s 2% mark. Each month’s data gives us a clearer picture of our economic trajectory and the Federal Reserve’s (Fed) policy moving forward, as well as new and direct implications on credit metrics. Data highlights from this month’s report include: The U.S. economy added 216,000 jobs in December, but after November and October levels were revised, three-month average job creation now sits below the pre-pandemic level. While there was no change in November, annual core inflation, which excludes the volatile food and energy components, cooled in December from 4.0% to 3.9%. Consumer sentiment rose 14% in December, reversing the past four monthly declines, driven by increased optimism toward the trajectory of inflation. Check out our report for a deep dive into the rest of this month’s data, including student loans, consumer spending, the housing market, and delinquencies. To have a holistic view of our current environment, we must understand our economic past, present, and future. Keep an eye out for this year’s chartbook for a comprehensive view of the past year and download our latest forecast for a view of what’s to come. Download report View forecast For more economic trends and market insights, visit Experian Edge.

This article was updated on January 26, 2024. Marketers are facing new challenges as third-party cookies crumble, and people use more devices throughout the day. Someone might comparison shop on their laptop in the morning, do more research on a tablet in the afternoon and finally decide to make a purchase on their phone before falling asleep at night. Being able to track these movements and insert yourself where appropriate can be difficult, but it's not impossible. One solution that's becoming increasingly attractive is creating a unified identity for each customer — and matching every piece of data and touchpoint to the single profile. For this to work, you need identity resolution. What is identity resolution? Identity resolution is the ongoing process of linking various identifying elements to create and expand a unique identity. The multi-step process can include: Securely onboarding data into a system Hashing or tokenizing personal information to improve security and privacy Setting aside information that can't be matched to an identity yet Matching or linking identifiers to a known unique identity Verifying that the identities and identifiers are accurate An identity graph (ID graph) is an essential part of identity resolution. It's the proprietary database that can pull in and store data from different sources and link them to a unique identifier — also known as a persistent identification number. Depending on the system and purpose, identity resolution may focus on creating a single identity for a person, household, or business. The information can come from internal sources, including a customer relationship management (CRM) tool, email marketing platforms, event management platforms, social media accounts, point-of-sales systems, and other digital and offline touchpoints. Additionally, third-party data sources, such as credit or demographic data, can contribute to building a more complete identity. And second-party data — information that's shared between brands or companies — can also be helpful. As new digital and offline information is created or found, it's linked to the existing persistent identification number in the ID graph. The process can happen in different ways. The resolution system could accurately match an engagement to a person with deterministic data, such as a hashed email address, assuming they logged in. If the person didn't log in, a probabilistic model may be able to accurately attribute the session to the person's identity based on indicators that it's likely the same person, such as a device ID or behavioral data. A hybrid approach combines deterministic and probabilistic approaches, which could be important for scaling. The goal and end result is often called a holistic, single-unified, or 360-degree view of a customer. READ MORE: Making identities personal Why does identity resolution matter? Identity resolution lets you know with whom you're connecting, which can be important throughout a customer's lifecycle. From marketing to collections, you want to be able to engage the right person on the right channel with the right offer. And that's only possible when you can accurately identify people. Consistent and accurate identity resolution is difficult, though. Experian's 2023 Identity and Fraud Report found that 92% of businesses have a strategy in place for identifying consumers online. But 63% of consumers are either "somewhat confident" or "not very confident" that businesses can accurately recognize them online. What are the benefits of identity resolution? It's a worthy goal to push toward, because you can use identity resolution solutions to: Consolidate your view of customers Companies may have multiple profiles of the same customer — one from an email list, another from their loyalty program and a third from an outdated system. Your customers are also interacting with you in different ways, perhaps logging into an account from their laptop in the morning while visiting your site from a phone at night. Identity resolution lets you connect all these elements to create a single profile. Build targeted and measurable marketing campaigns Once you have a single and consistent view of your customers, you can more accurately segment and target your marketing campaigns. Personalizing messages can increase engagement and effectiveness. And, equally important, knowing to who you don't want to send messages can help you avoid wasting marketing spending. Some identity resolution services can also help you track anonymous visitors and customize your marketing with look-alike models, which can identify people who are likely part of your target audience. You'll also be able to more accurately measure the effectiveness of a campaign. With a single customer view, it's easier to know if and how a targeted social media ad, television spot and emailed coupon worked together to create a sale. Increase customer experiences across brands When implemented throughout an organization, you can also use the single view of a customer to create a consistent experience across brands and business units. Each can benefit from a more holistic understanding of the customer and can contribute to building out customers' profiles.  Seamlessly confirm identities Identity resolution can also create a more frictionless experience for customers who want to create or log into your site, and it can help with detecting fraud and high-risk consumers. But keep data security top of mind. Consumers rank privacy (79%) and security (78%) much higher than login convenience (38%) when considering their online experience. What does an identity resolution solution look like? The need for and type of identity resolution can vary depending on a business' challenges and goals. For instance, large retailers often have a lot of first-party data — so much that it may be overwhelming. For them, an identity resolution solution that can organize internal data while enhancing it with external data points could be a priority. In contrast, a business with infrequent touchpoints might not have as much first-party data and could benefit from a solution that offers as much external information as possible. Some organizations are building their own internal identity resolution services to address these challenges, but many are looking to outside partners for identity resolution. When comparing partners, consider: Flexibility and scalability: Understand which data the solution can onboard and how quickly it can onboard data. Consider whether you'll want to be able to use real-time APIs or batch processing, and the limitations on how much data the provider can process at a time. Additionally, consider whether the ID graph will use persistent IDs that can change as you scale. Matching and analysis: Ask about the solution's approach and success with matching online and offline data and the options to integrate or append second and third-party data. If you want to be able to securely and privately share anonymized identities internally or with partners, make sure that's an option as well. Integration: Research whether the provider can easily integrate your existing services and vendors. Privacy: 73% of consumers say it's a business's responsibility to protect them online. Ask about the provider's experience and approach to storing and anonymizing data. Some solutions also have built-in activation tools. These let you build and launch omni-channel campaigns. They also analyze and report on how well your campaigns are performing. Get started today To learn more about the importance of digital identity and Experian's identity solutions, visit us today. Learn more

In today’s complex business landscape, data-based decision-making has become the norm, with advanced technologies and analytics tools facilitating faster and more accurate modeling and predictions. However, with the increased reliance on models, the risk of errors has also increased, making it crucial for organizations to have a comprehensive model risk management framework. In this blog post, we will dive deeper into model risk management, its importance for organizations, and the key elements of a model risk management framework. What is model risk? First, let's define what we mean by model risk. Many institutions use models to forecast and predict the future performance of investments, portfolios or consumers' creditworthiness. Model risk can happen when the results produced by these models are inaccurate or not fit for the intended purpose. This risk arises due to several factors, like data limitations, model assumptions and inherent complexities in the underlying modeled processes. For example, in the credit industry, an inaccurately calibrated credit risk model may incorrectly assess a borrower's default risk, resulting in erroneous credit decisions and impacting overall portfolio performance. What is a risk management model and why is it important? A risk management model, or model risk management, refers to a systematic approach to manage the potential risks associated with the use of models and, more specifically, quantitative models built on data. Since models are based on a wide range of assumptions and predictions, it's essential to recognize the possibility of errors and acknowledge its impact on business decisions. The goal of model risk management is to provide a well-defined and structured approach to identifying, assessing, and mitigating risks associated with model use. The importance of model risk management for institutions that leverage quantitative risk models in their decisioning strategies cannot be overstated. Without proper risk management models, businesses are vulnerable to significant consequences, such as financial losses, regulatory enforcement actions and reputational damage. Model risk management: essential elements The foundation of model risk management includes standards and processes for model development, validation, implementation and ongoing monitoring. This includes: Policies and procedures that provide a clear framework for model use and the associated risks. Model inventory and management that captures all models used in an organization. Model development and implementation that documents the policies for developing and implementing models, defining critical steps and role descriptions. Validation and ongoing monitoring to ensure the models meet their stated objectives and to detect drift. In addition to these essential elements, a model risk management framework must integrate an ongoing system of transparency and communication to ensure that each stakeholder in model risk governance is aware of the policies, processes and decisions that support model use. Active engagement with modelers, validators, business stakeholders, and audit functions, among other stakeholders, is essential and should be included in the process. How we can help Experian® provides solutions and risk mitigation tools to help organizations of all sizes establish a solid model risk management framework to meet regulatory and model risk governance requirements, improve overall model performance and identify and mitigate potential risk. We provide services for back testing, benchmarking, sensitivity analysis and stress testing. In addition, our experts can review your organization’s current model risk management practices, conduct a gap analysis and assist with audit preparations. Learn more *This article includes content created by an AI language model and is intended to provide general information.