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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

Ghost student fraud is a serious and alarming issue in the educational sector. Learn how to spot it and safeguard your institution.

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

Fraudsters have evolved their techniques to capitalize on homeowners and lenders by shifting their focus from home purchases to HELOC fraud.

Explore the advantages of using both instant and permissioned verification and how they can synergistically enhance coveragea and reduce costs.

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

The Fed launched FedNow, a new instant payment service. While it offers advantages, there's concerns that fraudsters may exploit it with fraud schemes.

Continually managing credit risk matters because there's always a balancing act. Learn how to create an effective credit risk managment strategy.

By leveraging an array of tools and technologies, businesses can tailor their fraud prevention strategies to suit the specific needs of their customers.

View our digital lending trends report for a full analysis on origination volume trends, delinquency trends, and consumer behavior insights. Read more!

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

Business leaders accross industries are using predictive analytics to make informed decisions.

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Dealers are always looking for reasons to connect with consumers. From back-to-school or graduation specials to holiday offers, dealers leverage seasonal and routine aspects of daily life to connect with consumers. Tax season offers a unique annual opportunity to position your vehicles and dealership for purchase by a consumer expecting a tax refund. In many cases, even consumers not receiving a hefty tax refund will be receptive to the tax time message. With the right strategy, message, and audience, you can market to consumers who are a few thousand dollars richer! Consider a tax refund match program Even if you are not in a position to offer consumers extraordinary sales offers, you may be able to create some special dealership-level seasonal offers that take your tax refund message to the next level. For example, offering a Tax Refund match program that offers consumers a discount off a vehicle matching the tax refund applied as a down payment would surely make your dealership stand out! Target consumers with service incentives What about consumers who did not expect refunds or have already spent them? Perhaps offering service incentives such as offering free tax filing software with the purchase of a prepaid service plan would be appealing. Or simply incentivize consumers to receive a discount coupon book during tax season to lighten the burden tax season brings.Tax season often sets the stage for the spring and summer vehicle sales season. Setting the stage by offering service incentives and tax refund matching programs creates rapport with your consumers that you can build upon. Start developing more effective marketing strategies The Experian Marketing Engine (EME) gives dealers and agencies the ability to build effective marketing plans by providing comprehensive market analysis along with powerful audience list creation. Tax time is just one of many messages dealers can deploy utilizing EME's solutions. At Experian Automotive, we leverage our world-class data set to give our dealer and agency clients unparalleled information to market effectively. If you find this topic interesting, you should read one of our others blogs, How to Effectively Use Audiences for Traditional and Online Marketing.