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This article was updated on February 6, 2024. Lenders looking to gain a competitive edge need to improve their credit underwriting process in the coming years. The most obvious developments are the advances in artificial intelligence (AI) — machine learning in particular — the increased available computing capacity, and access to vast amounts of data. But when it comes to credit underwriting models, those are tools you can use to reach your goals, not a strategy for success. The evolution of credit underwriting Credit underwriters have had the same goal for millennia — assess the creditworthiness of a borrower to determine whether to offer them a loan. But the process has changed immensely, and the pace of change has recently increased. Fewer than 50 years ago, an underwriter might consider an applicant's income, occupation, marital status, and sex to make a decision. The Equal Credit Opportunity Act didn't pass until 1974. And it wasn't expanded to prohibit lending discrimination based on other factors, such as color, age, and national origin, until two years later. Regulatory changes can have an immediate and immense impact on credit underwriting, but there were also slower changes developing. As credit bureaus centralized and computers became more readily available, credit decisioning systems offered new insights. The systems could segment groups and help lenders make more complex and profitable decisions at scale, such as setting risk-appropriate credit limits and terms. INFOGRAPHIC: Data-driven decisioning journey map With access to more data and computing power, lenders get a more complete picture of applicants and their current customers. Technological advances also lead to automated decisions, which can improve lenders' workflows and customer satisfaction. In the late 2000s, fintech lenders entered the scene and disrupted the ecosystem with a completely online underwriting and funding process. More recently, AI and machine learning started as buzzwords, but quickly became business necessities. In fact, 66% of businesses believe advanced analytics, including machine learning and artificial intelligence, are going to rapidly change the way they do business.1 The latest explainable machine learning models can increase automation and efficiency while outperforming traditional modeling approaches. Access to increased computing power is, once again, helping power this shift.2 But it's also only possible because of the lenders access to alternative credit data.* WATCH: Why Advanced Analytics is Now Available for All Future-proofing your credit underwriting strategy Today's leading lenders use innovative technology and comprehensive data to improve their credit decisioning — including fraud detection, underwriting, account management, and collections. To avoid getting left behind, you need to consider how you can incorporate new tools and processes into your strategy. Get comfortable with machine learning models Although machine learning models have repeatedly shown they can offer performance improvements, lenders may hesitate to adopt them if they can't explain how the models work. It's smart to be cautious as so-called “black box" models generally don't pass regulatory muster — even if they can offer a greater lift. But there is a middle ground, and credit modelers use machine learning techniques to develop more effective models that are fully explainable. READ MORE: Explainability: ML and AI in credit decisioning Explore new data sources Machine learning models are great at recognizing patterns, but you need to train them on large data sets if you want to unlock their full potential. Lenders' internal data can be important, especially if they're developing custom models. But lenders should also try leveraging various types of alternative credit data to train models and more accurately assess an applicant's creditworthiness. This can include data from public records, rental payments, alternative financial services, and consumer-permissioned data. READ MORE: 2023 State of Alternative Credit Data Report Focus on financial inclusion Using new data sources can also help you more accurately understand the risk of an applicant who isn't scorable with traditional models. For example, Lift Premium™ uses machine learning and a combination of traditional consumer bureau credit data and alternative credit data to score 96 percent of U.S. consumers — 15 percent more than conventional scores.3 As a result, lenders can expand their lending universe and offer right-sized terms to people and groups who might otherwise be overlooked. Use AI to fuel automation Artificial intelligence can accelerate automation throughout the credit life cycle. Machine learning models do this within underwriting by more precisely estimating the creditworthiness of applicants. The more accurate a model is, the better it will be at identifying applicants who lenders want to approve or deny. Consider your decisioning strategy Although a machine learning model might offer more precise insight, lenders still need to set their decisioning strategy and business rules, including the cutoff points. Credit decisioning software can help lenders implement these decisions with speed, accuracy, and scalability. CASE STUDY: Experian partnered with OneAZ Credit Union to upgrade to an advanced credit decisioning platform and automate its underwriting strategy. The credit union increased load funding rates by 26 percent within one month and reduced manual reviews by 25 percent. Use underwriting as a component of strategic optimization Advanced analytics allow companies to move away from simpler rule-based decisions and toward strategies that take the business's overall goals into account. For example, lenders may be able to optimize decisions that involve competing goals — such as targets for volume and bad debt — to help the business reach its goals. Test and benchmark Underwriting is an iterative process. Lenders can use machine learning techniques to build and test challenger models and see how well they perform. You can also compare the results to industry benchmarks to see if there's likely room for more improvement. Why lenders choose Experian Lenders have used Experian's consumer and business credit data to underwrite loans for decades, but Experian is also a leader in advanced analytics. As lenders try to figure out how they'll approach underwriting in the coming years, they can partner with Experian's data scientists, who understand how to develop and deploy the latest types of compliant and explainable credit underwriting models. Experian also offers credit underwriting software and cloud-based and integrated decisioning platforms, along with modular solutions, such as access to alternative credit data, predictive attributes and scores. And lenders can explore collaborative approaches to developing ML-aided models that incorporate internal and third-party data. If you're not sure where to start, a business review can help you identify a few quick wins and create a road map for future improvements. Explore our credit decisioning solutions. * When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions as regulated by the Fair Credit Reporting Act (FCRA). Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably. 1Experian (2022). Explainability: ML and AI in credit decisioning2Experian (2022). Webinar: Driving Growth During Economic Uncertainty with AI/ML Strategies 3Experian (2022). Lift Premium

Insights from the Cyber Risk Summit Beverly Hills – October 2023 Authored by Ryan Coyne I recently participated in a panel with industry experts, delving into third-party cyber risks. The panel shed light on best practices, challenges, and strategies to mitigate the impact of third-party incidents. Panel Participants: Stu Panensky (Moderator) – FisherBroyles, LLP Ryan Coyne – Experian Tom Egglestone – Resilience Mark Grazman – Fenix24  Matthew Saidel – FTI Consulting Agenda: Incident Best Practices: Collaboration & Coordination on IR Action Items Upstream Risk of Third Parties: Vendors, Suppliers & Business Partners Downstream Risk in the Policyholder Supply Chain The Cyber Risk Summit held in Beverly Hills provided valuable insights into the risks of engaging unsecured third parties. Key Takeaways Understanding the Significance Tom emphasized the longstanding nature of cyber risk exposure tied to third-party relationships. The increasing reliance on external vendors in a tech-enabled world has heightened this risk, especially with the surge in outsourcing and software adoption. Tom highlighted that, even in 2019, Gartner research indicated that 60% of surveyed companies worked with over 1000 third parties in their supply chain, setting the stage for the escalated risk environment post-pandemic. Crisis Communications in Third-Party Incidents Matt shared insights into the challenges faced when third-party incidents unfold. The necessity of involving crisis communications consultants early in the process, especially for upstream and downstream, was stressed. Preserving the right to operate and maintaining client trust amid incidents were key points Matt made.Hands-On Restoration PerspectiveMark, providing a hands-on restoration perspective, discussed the rarity of involvement at the inception of an event. His emphasis on locking down infrastructure, understanding the threat actor’s persistency, and encouraging robust backup strategies showcased the intricacies involved in restoration efforts.“Restoration efforts often kick in when patient zero is unidentified. Locking down the infrastructure and focusing on repairing affected elements are essential” – Mark Grazman, Fenix24 Notification Strategies and Legal Implications Representing Experian, I shared my perspective on notification complexities that the average consumer may not be aware of, such as notifying everyone upfront versus opt-in processes. The legal implications of notifying on behalf of others and coordinating with multiple parties. The nuanced approach to call center communication and the crucial factor of making details clear in notification letters in minimizing confusion for recipients.I want to emphasize a point I made earlier in the panel on the downstream impact of notification strategies and the need to customize communication for recipients.“For these incidents, it’s most important to minimize complexity on the notification side and minimize confusion for the recipient of your notification letter.” – Ryan Coyne, Experian Insights from an Insurance Claims Handler Tom, as an insurance claims handler, underscored the importance of understanding vendor contracts, particularly clauses related to defense and indemnity. He highlighted the need for transparency in the vendor’s incident response process, especially when the insured isn’t in control, adding a layer of complexity to communication and expectation setting. Crafting a Seamless Notification Process: Public-Private Partnerships Stu Panensky, Moderator: Public-private partnerships emerged as a recurring theme during the panel discussions. The need for collaboration between law enforcement, insurance companies, and businesses became evident. Stu emphasized the role of public-private partnerships in influencing better outcomes and impacting data protection, regulation, and litigation. The insights from the 2023 Beverly Hills Cyber Risk Summit underline the interconnected nature of cyber risks and the critical importance of proactive measures. Stakeholders are urged to adopt a collaborative approach, navigate legal complexities, and stay vigilant in the face of evolving challenges. I welcome you to watch the full discussion on-demand. Watch the panel session on-demand now

This article was updated on February 5, 2024. Identity management can refer to how a company creates, verifies, stores, and uses its customers' digital identities. Traditionally, many large organizations relied on a highly segmented and siloed approach. For example, marketing, risk, and support departments might each have a limited view of a customer, and the tools and systems that support their specific purpose. Organizations are now shifting to a more holistic approach to enterprise identity management. By working together, departments help contribute to building a more complete, single view of a customer. Some companies have renewed or increased their focus on the transformation during the pandemic, and the transition to an enterprise-wide identity management strategy can have long-lasting benefits. But it isn't always easy. Challenges of an enterprise-wide identity management strategy Gathering the initial momentum needed to break out of a siloed approach can be particularly challenging for large organizations when each business unit has an ingrained identity system that meets the unit's needs. Smaller organizations might have an easier time gathering consensus, but budget or technological limitations may be serious constraints. Even after a decision is made and the budget gets set aside, organizations need to think through how they'll create and manage a new enterprise-wide identity management system. It's not a one-and-done upgrade. For the strategy to succeed, you'll need to have processes in place to onboard, verify, secure, and activate the new digital identities. READ: What is Effective Multifactor Identity Authentication? Why use an enterprise-wide approach? Motivations and specifics can vary depending on an organization's size and structure, but some companies find a more holistic approach to customer identity management helps them: Improve customer experiences Save money by removing redundancies Boost sales with better-targeted marketing Better understand customers' needs Provide faster and more relevant support Make more informed decisions Detect and prevent fraud These benefits can play out across the entire customer lifecycle, and identity management systems are able to achieve this by pulling in data from various sources to build robust consumer identities and systems. Your internal, first-party data will be the most valuable and insightful, but you can append multidimensional data from third-party sources, such as consumer credit databases, demographic data or device data. And second-party data from partner brands or organizations. READ: Experian 2023 Identity and Fraud Report Consider the regulatory and security challenges An enterprise identity data management approach can also mean re-evaluating the applicable regulations and security challenges. The passage of the E.U.'s General Data Protection Regulation and California Consumer Privacy Act marked an important shift in how companies need to handle consumers' personal information — but that was only the start. Some U.S. states have also passed or are currently considering data privacy laws. Industry-specific regulations can apply as well, particularly in the healthcare and financial services industries. It's not as if a siloed approach lets an organization avoid regulation, but keeping current and upcoming laws in mind can be important during a large digital transformation. Additionally, consider how going beyond the minimum requirements could be beneficial. In a 2023 Experian white paper, we found that 61 percent of consumers want complete control over how companies use their personal data.1 Security also needs to be top of mind for any organization that collects and stores consumers' personal information. An enterprise-wide identity management system may make managing increasing amounts of data easier, which could help decrease fraud risks. And your customers may be willing to help — 67 percent are open to sharing data if it will increase security and help prevent fraud.2 Keeping customers' desires front and center Experian partnered with Aite-Novarica to study enterprise-wide identity management. All but one of the 12 executives interviewed said client experience is a primary or predominant driver in the transformation of their identity management programs.3 Once implemented, a holistic view of customers can increase the experience in many ways: Meaningful engagement: You can deliver relevant and timely offers if you understand when, where and why consumers are interested in your products and services. Similarly, you'll know who isn't a good fit and won't bother them (or waste money) by showing them ads. Verification: Using a single, persistent identity could make the initial and ongoing identity verification an easier process that doesn't disrupt consumers' lives or lead to frustration. Ongoing recognition: Nearly 70 percent of all consumers want businesses to recognize them across multiple visits.3 But you'll need to study your customers to determine how much friction is acceptable. Some people prefer security over convenience and are willing to trade a little time to use extra verification methods. Customer service: Having more insight into a customer's entire history and interactions with your organization can help you quickly respond when an issue arises, or even anticipate and solve potential problems. Security: Nearly two-thirds (64 percent) of consumers say they're very or somewhat concerned with online security.4 Companies that can quickly and accurately identify consumers can also help keep them safe from fraud and identity theft. While these may be some consumers' top concerns today, continue listening to your customers to better understand their wants and needs. WATCH: Webinar: Identity Evolved — Building consumer trust and engagement Implementing an enterprise-wide identity management strategy Identity management can become a daunting task, particularly as new data sources begin to flow. As a result, many organizations turn to outside partners who can help manage part, or all, of the process. For example, an identity management solution may offer identity resolution and help create and host an identity graph (the database that stores the unique digital identities). A more robust offering may also help with other parts of identity management, including ongoing data hygiene and helping you turn your unique customer insights into actionable marketing campaigns. Experience managing vast amounts of data is also important, as is access to additional offline and online data sources. In 2023, Experian found that 85 percent of companies said poor quality customer contact data negatively impacted their operation's processes and efficiency.5 An enterprise-wide system that allows business units to update a single customer profile with the latest contact information might help. But working with a data provider that appends the latest info from outside databases could be a better way to ensure you have customers' latest contact info. When researching potential partners, also consider how their offerings and approach align with your goals. If, like others, improving the customer experience is a priority, make sure the solution provider also has a customer-first approach. In turn, this means security is a top priority — it's what customers want and it's important for protecting you and your reputation. Learn more about Experian's identity management solutions and how you can benefit from working with a company that understands identities are personal. Learn more 1Experian (2023). White paper: Making identities personal 2Ibid. 3Aite-Novarica and Experian (2022). Enterprise Identity Management: Evolving Aspirations and Improved Collaboration Are Transforming the Discipline 4Experian (2023). Identity and Fraud Report 5Experian (2023). White paper: Making identities personal