Tag: analytics

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

External fraud generally results from deceptive activity intended to produce financial gain that is carried out by an individual, a group of people or an entire organization. Fraudsters may prey on any organization or individual, regardless of the size or nature of their activities. The tactics used are becoming increasingly sophisticated, requiring a multilayered fraud mitigation strategy. Fraud mitigation involves using tools to reduce the frequency or severity of these risks, ultimately protecting the bottom line and the future of the organization. Fraud impacts the bottom line and so much more According to the Federal Trade Commission, consumers reported losing more than $10 billion to fraud in 2023, a 14% increase over the previous year and the highest dollar amount ever reported. These costs extend beyond the face value of the theft to include fees and interest incurred, fines and legal fees, labor and investigation costs and external recovery expenses. Aside from dollar losses and direct costs, fraud can also pose legal risks that lead to fines and other legal actions and diminish credibility with regulators. Word of deceptive activities can also create risk for the brand and reputation. These factors can, in turn, result in a loss of market confidence, making it difficult to retain clients and engage new business. Leveraging fraud mitigation best practices As the future unfolds, three things are fairly certain: 1) The future is likely to bring more technological advances and, thereby, new ways of working and creating. 2) Fraudsters will continue to look for ways to exploit those opportunities. 3) The future is here, today. Organizations that want to remain competitive in the digital economy should make fraud mitigation and prevention an integral part of their operational strategy. Assess the risk environment While enhancing revenue opportunities, the global digital economy has increased the complexity of risk management. Be aware of situations that require people to enforce fraud risk policies. While informed, experienced people are powerful resources, it is important to automate routine decisions where you can and leverage people on the most challenging cases. It is also critical to consider that not every fraud risk aligns directly to losses. Consider touchpoints where information can be exposed that will later be used to commit fraud. Information that crooks attempt to glean from idle chatter during a customer service call can be a source of unexpected vulnerability. These activities can benefit from greater transparency and automated oversight. Create a tactical plan to prevent and handle fraud Leverage analytics wherever possible to streamline decisions and choose the right level of friction that’s appropriate for the risk, and palatable for good customers. Consumers and small businesses have come to expect a customized and frictionless experience. Employee productivity, and ultimately revenue growth, requires the ability to operate with speed and informed confidence. A viable fraud mitigation strategy should incorporate these goals seamlessly with operational objectives. If not, prevention and mitigation controls may be sidelined to get legitimate business done, creating inroads for fraudsters. Look for a partner who can apply the right friction to situations depending on your risk appetite and use existing data (including your internal data and their own data resources) to better identify individual consumers. This identification process can actually smooth the way for known consumers while providing the right protection against fraudsters and giving consumers who are new to your organization a sense of safety and security when logging in for the first time. It's equally important that everyone in your organization is working together to prevent fraud. Establish and document best practices and controls, beginning with fostering a workplace culture in which fraud mitigation is part of everyone's job. Empower and train all staff to identify and report suspicious activity and ensure they know how to raise concerns. Consider implementing ways to encourage open and swift communication, such as anonymous or confidential reporting channels. Stay vigilant and tap into resources for managing risks It is likely impossible to think of every threat your organization might face. Instead, think of fraud mitigation as an ongoing process to identify and isolate any suspected fraud fast — before the activity can develop into a major threat to the bottom line — and manage any fallout. Incorporating technology and robust data collection can fortify governance best practices. Technology can also help you perform the due diligence faster, ensuring compliance with Know Your Customer (KYC) and other regulations. As necessary, work with risk assessment consultants to get an objective, experienced view.  Learn more about fraud risk mitigation and fraud prevention services. Learn more  

Last year, my wife and I decided to take advantage of Experian’s remote-work policy and move back to my hometown, so we could be closer to family and friends. As excited as we were, the idea of selling and buying a home during the market frenzy was a little intimidating. Surprisingly, finding a home wasn’t our challenge. We lucked out and found what we were looking for in the exact neighborhood we wanted. Our biggest challenge was timing. Our goal was to sell our current home and immediately move into the new one, with no overlap of payments or having to put our belongings in storage while we temporarily stayed with family (or in a short-term rental). Once we sold our home, we had exactly 30 days to close on our new home and move in. Since this wasn’t our first rodeo, I felt confident all would go smoothly. Things were on schedule until it came time to verify our income and employment. Who knew something so simple could be so hard? Let me share my experience with you (crossing my fingers you have a smoother experience in place for your borrowers): Pay statements — I was initially asked to provide pay statements for the previous two months. Simple enough for most borrowers, but it does require accessing your employer payroll system, downloading multiple pay statements and then either uploading them to your lender portal or emailing them to your loan officer (which no borrower should be asked to do). This took me less than 30 minutes to pull together. Verification report — After reviewing my pay statements, my lender told me they needed an official verification report on my current and previous employers. At the time, Experian had just acquired Corporate Cost Control (now part of Experian Employer Services), a company that offers verification-fulfillment services for employees, employers and verifiers. I told my lender I could provide the verification report via Corporate Cost Control and they agreed it would be sufficient. This took me several days to figure out. HR information — Just when I thought we were good, I received an email from my lender asking for one last thing — the HR contact information of my current and previous employers. Obtaining this information from Experian was easy, but I didn’t know where to start with my previous employer. I ended up texting some former colleagues to get the information I needed. This too took several days to figure out. Finally, I got the call from my lender saying everything checked out and I was good to proceed with the underwriting process. Whew! What I thought would take 30 minutes ended up taking a full week and threatened our ability to close on time. And not to mention was a massive headache for me personally. This isn’t how you want your borrowers to feel, which brings me to the title of this blog, it’s 2022, why is mortgage employment verification so painful in today’s digital age? Other industries have figured out how to remove pain and friction from their user experiences? Why is the mortgage industry lagging? Mortgage employment verification made easy If it’s lack of awareness, you should know there are tools that can automate verification decisions. Experian Verify™ is a perfect example where mortgage lenders can instantly verify a borrower’s income and employment information (both current and previous employers), without needing to ask the borrower to track down pay statements or HR contact information. You can literally verify information in seconds — not hours, days, or weeks.  And the service supports Day 1 Certainty® from Fannie Mae — giving you increased peace of mind the data is accurate and trusted. This not only improves the borrower experience but increases efficiency with your loan officers. Tools like Experian Verify are a win-win for you and your borrowers. So, what are you waiting for? Modernize your experience and give your borrowers (like me) the frictionless experience they deserve, and if we’re being honest, are starting to demand. Learn more about income and employment verification for mortgage    

Online transactions face a higher chance of being declined because face-to-face transactions come with a higher degree of confidence. Businesses who fail to address this problem run the risk of losing the customer permanently, damaging their reputation and bottom line. What can e-commerce marketplace merchants do to increase the approval rate of online payments without making fraud worse? Here are three tips: 1. Broaden access to data beyond what’s in the authorization stream. Merchants use a variety of solutions to prevent fraud and verify identities, but typically use very limited data to approve a transaction through the authorization stream between a merchant and issuer. The issuing bank often only compares the purchase data to the address listed on the card owner’s account, which can create discrepancies when a customer is trying to send an order to an alternate address from their primary home. That’s why it’s important for merchants to augment their decisioning with additional data sources to help inform the true customer risk profile. 2. Leverage capabilities that can assess risk for both the transaction and the individual behind it. Today, merchants leverage limited data including email address data, device information and other technologies in silos to augment their address verification capabilities. The challenge with these tools is that each judge the risk of a specific component of the transaction or the individual. Where integration is lacking, false positives are amplified. 3. Collaborate and share expertise and data across merchants and issuers. How can Experian help? Leveraging our multidimensional data, technical expertise and advanced analytics capabilities, we can help businesses frictionlessly authenticate valid customers, thus increasing revenue by increased approval rates, without increasing fraud or operating expenses. Only Experian Link™, our frictionless credit card owner verification solution can associate payment card with its owner. This solution combines Experian’s vast data assets – including over 500 million credit card account numbers on file in the U.S. across 250 million consumers – with our advanced analytics capabilities to match and assess the risk of the identity attributes presented to the merchant to the identity attributes contributed by the credit card’s issuer and to Experian’s network of credit and identity inquiries. The result: Experian Link’s patent-pending REST API simply and frictionlessly improves a merchant’s customer experience and helps increase revenue while reducing their fraud and operating expenses. Get started with Experian Link™ now. Experian Link

Experian recently announced Experian Identity and published an advertorial in American Banker outlining the integrated approach to identity that recognizes the full breadth of the company’s authoritative data solutions that help businesses better connect with their consumers in more personalized, meaningful and secure ways. The efforts address the rapidly changing definition and landscape of identity and take on the importance and needs for identity which span across the entire customer journey. From marketing to a specific consumer’s needs, to facilitating a friction-right customer experience, to protecting personal information. As such, there’s a gap for single-partner providers to help businesses navigate this change, while also putting the needs of the consumer first. “Identity data sets are constantly growing with inputs from new interactions. Many future sources of data have yet to be even conceived or developed,” said Kathleen Peters, Chief Innovation Officer, Experian Decision Analytics. “Staying ahead of the identity market curve is vital, and it requires building and continually evolving an enterprise-scale identity solution that interconnects with your own unique data and systems to create attribute-rich profiles of your customers that work across any identity application. That’s Experian Identity.” Experian Identity underscores the need businesses have to respond to increasing identity needs with interconnected, scalable technology, products and services that optimize the consumer experience.       While the integrated approach announcement is new, the capability is not. Experian has been trusted for decades to secure individuals’ identity around the most important decisions in their lives – think purchasing a car or home, being identified at the doctor’s office, and more. As such, consumers remain at the center of every action. Experian Identity offers identity resolution, verification, authentication and protection, and fraud management solutions that include first- and third-party fraud, account takeover, credit card verification, identity resolution and restoration, risk-based authentication, synthetic identity protection and more. Additionally, we’ve included a special blog post introducing Experian’s identity capabilities from Kathleen Peters on the Experian Global News Blog and additional coverage. Stay tuned for more updates. Experian Global News Blog - Making Identities Personal: Experian Helps Businesses Build Consumer Trust American Banker – Making Identities Personal: Building Trust and Differentiating Your Brand Experian White Paper - Making Identities Personal For more information about Experian Identity, visit www.experian.com/identity-solutions.

“Disruption has caused enormous amounts of innovation,” said Jennifer Schulz, CEO of Experian, North America. “We must continue to be the disruptors in our industry which takes effort, data, technology, bright minds and vision for what the future will be.” Schulz kicked off the 39th Vision conference with a future-focused keynote delivered to a crowd of more than 400 attendees. Alex Lintner, Group President, Experian Consumer Information Services, talked about the next phase of great, highlighting the digital transformation that has taken place in the generations of the past and the disruption and innovation happening today and in the future. Keynote speaker: Dr. Mohamed A. El-Erian Dr. Mohamed A. El-Erian, renowned economist and author, President of Queens’ College, Cambridge, Chief Economic Advisor at Allianz, Chair of President Obama’s Global Development Council and Former CEO and Co-Chief Investment Officer of PIMCO, spoke about the Fed, inflation, negative interest rates and the labor market, as well as the importance of inclusion. El-Erian, who said he reads the Financial Times religiously, acknowledged that we will make mistakes on the journey as we work to be even more inclusive. To navigate what’s ahead, he said we will need resilience, optionality and agility. “It’s important to connect with information, acknowledge the insecurity, in a language people understand, in order to connect,” he said.     Session highlights – day 1 The conference hall was buzzing with conversations, discussions and thought leadership. Buy Now Pay Later A large audience was in attendance for a session that introduced Experian’s Buy Now Pay Later Bureau™ and explored how it’s the first and only solution of its kind — serving consumers, BNPL providers, financial institutions and regulators. Identity Identity is constantly evolving, and while biometrics and authentication may have become ubiquitous, there is much activity around the concepts of eIDs, identity wallets and identity networks. Experian is making identities personal and helping businesses to recognize, manage and connect customer identities in new ways using data, analytics and technology. Marketing In today’s hypercompetitive world, businesses need to engage the freshest data and increase velocity when it comes to time to market. An average of 120 days won’t cut it. Ascend Marketing speeds time to market and helps achieve higher ROI. Regulatory Landscape With so much happening at Capitol Hill, a panel of experts from DC discussed a number of topics and proposals (and their impacts), including the defense for risk-based pricing, the impact of suppressing negative data, and trending topics like Buy Now Pay Later and data portability. All the while, the tech showcase had a constant flow of attendees with demos ranging from data and decisioning to financial inclusion and technology. This is just the beginning. And as Schulz said, “There’s more to do.” More insights from Vision to come. Follow @ExperianVision to see more of the action.

Experian’s in-person Vision conference returns next Monday, April 11 in Los Angeles, Calif. The event is known for premier thought leadership, net-new insights and the latest and greatest in technology, innovation and data science. This year’s agenda promises to have intentional discussions around tomorrow’s trending topics including financial inclusion, buy now pay later, open banking, the future of fraud, alternative data strategies, and much more. A few spotlight sessions include: Top trends including the future application of the cloud and emerging technologies, emerging regulatory legislation and the broader implications and opportunities of DeFi. A deep dive into strategies around the targeting/marketing revolution and how to deliver in the post-COVID-19 market environments and bolster financial inclusion decisions. An introduction to Experian’s Buy Now Pay Later BureauTM, the industry’s first and only solution designed to address the needs of consumers, BNPL providers, financial institutions and regulators alike. A roundup of sessions addressing innovation in action spanning from real-time verifications, to data-driven automation, and unified platforms from data to deployment to decisioning. Several sessions highlighting future-looking strategies and solutions that leverage alternative data that can increase conversion rates while concurrently reducing risk. Multiple sessions centered on the rapidly changing identity environment and combatting emerging fraud threats. The event will also include a Tech Showcase, where attendees can get a taste of tomorrow today with more than 20 demos and the latest innovations at their fingertips. And, as always, the event features marquee keynote speakers sure to inspire. This year’s featured speakers are Dr. Mohamed A. El-Erian, President of Queens’ College, Cambridge, Chief Economic Advisor at Allianz, and Former CEO and Co-Chief Investment Officer of PIMCO; Allyson Felix, Olympic Gold Medalist, co-founder of Saysh, a footwear and lifestyle brand for women, and Right to Play and Play Works ambassador; and the closing keynote will feature actor, investor, entrepreneur and philanthropist, Ashton Kutcher. Stay tuned for additional highlights and insights on our social media platforms throughout the course of the conference. Follow Experian Insights on Twitter and LinkedIn.  

There are many facets to promoting a more equitable society. One major driver is financial inclusion or reducing the racial wealth gap for underserved communities. No other tool has impacted generational wealth more than sustainable homeownership. However, the underserved and underbanked home buyers experience more barriers to entry than any other consumer segment. It is important to recognize the well-documented racial and ethnic homeownership gap; doing so will not only benefit the impacted communities, but also elevate the level of support of those lenders who serve them. What are we doing as an industry to reduce this gap? Many organizations are doing their part in removing barriers to homeownership and systemic inequities. In 2021, the FHFA published their Duty to Serve 2021 plans for Fannie Mae and Freddie Mac to focus on historically underserved markets. A part of this plan includes increasing liquidity of mortgage financing for lower- and moderate-income families. Fannie Mae and Freddie Mac each announced individual refinance offerings for lower-income homeowners – Fannie Mae’s RefiNow™ and Freddie Mac’s Refi PossibleSM. Eligible borrowers meet requirements including income at or below 100% area median income (AMI), a minimum credit score of 620, consideration for loans in forbearance and additional newly expanded flexibilities. As part of the plan, lenders will lower a borrower’s monthly payment by at least a half a percentage point reduction in their interest rate, which can translate into hundreds of dollars of savings per month and sustain their homeownership. Experian has the tools to help mortgage lenders take advantage of this offering  As a leader in data, analytics and technology, we have the tools needed to help lenders recognize opportunities to be inclusive and identify borrowers who may be eligible for Fannie Mae’s and Freddie Mac’s lower-income refinance offerings. To illustrate, we performed a data study and identified over 6M eligible mortgages nationwide (impacting over 8M borrowers) for this plan, and some lenders had as much as 30% of mortgages in their portfolio eligible with lower- and moderate-incomes.1 These insights can have a positive impact on the borrowers you serve by promoting more inclusion and benefit lenders through improved customer retention, strengthened customer loyalty and an opportunity to continue to build generational wealth through housing. We are committed to enabling the industry's DEI evolution As the Consumer’s bureau, empowering consumers is at the heart of everything we do. We’re committed to developing products and services that increase credit access, greater inclusion in homeownership and narrowing the racial wealth gap. Below are a few of our recent initiatives, and be sure to check out our financial inclusion resources here: United for Financial Health: Promotes inclusion in underserved communities through partnerships and have committed to investing our time and resources to create a more inclusive tomorrow for our communities. Project REACh (Roundtable for Economic Access and Change): brings together leaders from banking, business, technology, and national civil rights organizations to reduce barriers that prevent equal and fair participation in the nation’s economy, and we are engaged with the Alternative Credit Scoring Utility group as part of this initiative. Operation Hope: Empowers youth and underserved communities to improve their financial health through education, so they can thrive (not just survive) in the credit ecosystem so they can sustain good credit and responsibly use credit. DEI-Centric Solutions: From Experian Boost to our recent launch of Experian Go, we offer a variety of consumer solutions designed to empower consumers to gain access to credit and build a brighter financial future. What does this mean for you? Our passion, knowledge and partnerships in DEI have enabled us to share best practices and can help lenders prescriptively look at their portfolios to create inclusive growth strategies, identify gaps, and track progress towards diversity objectives. The mortgage industry has a unique opportunity to create paths to homeownership for underserved communities. Together, we can drive impact for generations of Americans to come. Let’s drive inclusivity and revive the American dream of homeownership. 1Experian Ascend™ as of November 2021

According to Experian’s State of Automotive Finance Market: Q3 2021 report, leasing comprised 24.03% of new vehicle financing in Q3 2021.

In Q3 2021, the average new vehicle loan amount increased 8.5% year-over-year, while the average used vehicle loan jumped more than 20% year-over year.

Shri Santhanam, Executive Vice President and General Manager of Global Analytics and Artificial Intelligence (AI) was recently featured on Lendit’s ‘Fintech One-on-One’ podcast. Shri and podcast creator, Peter Renton, discussed advanced analytics and AI’s role in lending and how Experian is helping lenders during what he calls the ‘digital lending revolution.’ Digital lending revolution “Over the last decade and a half, the notion of digital tools, decisioning, analytics and underwriting has come into play. The COVID-19 pandemic has dramatically accelerated that, and we’re seeing three big trends shake up the financial services industry,” said Shri. A shift in consumer expectations More than ever before, there is a deep focus on the customer experience. Five or six years ago, consumers and businesses were more accepting of waiting several days, sometimes even weeks, for loan approvals and decisions. However, the expectation has dramatically changed. In today’s digital world, consumers expect lending institutions to make quick approvals and real-time decisions. Fintechs being quick to act Fintech lenders have been disrupting the traditional financial services space in ways that positively impacts consumers. They’ve made it easier for borrowers to access credit – particularly those who have been traditional excluded or denied – and are quick to identify, develop and distribute market solutions. An increased adoption of machine learning, advanced analytics and AI Fintechs and financial institutions of all sizes are further exploring using AI-powered solutions to unlock growth and improve operational efficiencies. AI-driven strategies, which were once a ‘nice-to-have,’ have become a necessity. To help organizations reduce the resources and costs associated with building in-house models, Experian has launched Ascend Intelligence Services™, an analytics solution delivered on a modern tech AI platform. Ascend Intelligence Services helps streamline model builds and increases decision automation and approval rates. The future of lending: will all lending be done via AI, and what will it take to get there? According to Shri, lending in AI is inevitable. The biggest challenge the lending industry may face is trust in advanced analytics and AI decisioning to ensure lending is fair and transparent. Can AI-based lending help solve for biases in credit decisioning? We believe so, with the right frameworks and rules in place. Want to learn more? Explore our fintech solutions or click below. Listen to Podcast Learn more about Ascend Intelligence Services

When we look at how automotive manufacturers and dealers have marketed vehicles over the last few decades, we can see how through every decade, and every learning, it has led to data-driven marketing strategies that are powering success today. Let’s take a look: Strategic marketing in the 1990’s In the 1990’s dealership marketing included mainly newspaper and radio advertisements complimented by occasional direct mail pieces mailing to every home in a zip code. OEMs purchased ad time on popular Television and their dealer associations and local dealers had an option to do the same. Because the 1990’s was the age of “mass media,” marketing was based off geography. There was less attention on channel or audience and a good deal of spending! Cars were sold. Strategic marketing in the 2000’s The new millennium brought advancements in computers and databases. Dealerships explored the exciting world of internet promotion and email marketing and continued using traditional newspaper, radio and television to drive traffic. mail programs such as the “scratch and win” or the “key to a car you can win” efforts were common, resulting in massive mail-based marketing campaigns. Then came 2008, when many dealers drastically cut back on spending and were focused mainly on surviving the Great Recession. As we all know, many dealers, and even vehicle makes, did not. Strategic marketing in the 2010’s In the beginning of the decade, manufacturers and dealers were still recovering from the recession but were slowly feeling more optimistic about the economy. If there is one thing the industry learned from the recession, it was to be much more strategic when it came to spending. During this decade, ad technology advanced, as did the ability to evaluate marketing spend. Dealers became aware of the true cost of their traditional marketing ways and embarked on new paths of marketing to a smaller but more specific audience. Equity mining and greatly advanced revolutionized the direct mail and related online arena. As the decade drew to a close, marketers leveraged solutions where merge fields enabled customization and personalization for both direct mail and email marketing. With the ability to deliver massive volumes at a lower cost, email blasts grew in popularity. Social media platforms emerged as a force, and dealers experimented to invent new ways to leverage them. Television marketing underwent a massive facelift as consumers left cable for streaming services resulting in new advertising strategies such as addressable and connected TV, OTT (Over The Top) advertising. Strategic marketing in 2020 2020 will forever be remembered as the year of the pandemic. In automotive marketing, it was also the year of reinvention! With many showroom closures, dealers and OEMs found themselves with a reduced advertising budget and a greater need to find more targeted audiences with more effective marketing messaging. How do I master my market share? Who is in-market for my vehicles? How is my website performing? Which customers are in equity? Which customers have added a child to the household? How do I reach them in a digital world? This is where Experian has helped both manufacturers and dealers. Experian’s automotive marketing solutions help marketers utilize vehicle, consumer, lender, and market data to leverage market insights, target the right audience, develop effective messaging strategies, and measure outcomes to continually optimize results. Over the last four decades, automotive marketing strategies have become much more data driven, so having a solution that uses data insights to help retain loyal customers and win new conquest customers, all while reducing total marketing spend, is a key requirement for success in this decade…and beyond. If you're a marketer at a Dealership, learn more about our marketing solutions here. If you're an Auto OEM marketer, learn more about our marketing solutions here.

Lately, I’ve been surprised by the emphasis that some fraud prevention practitioners still place on manual fraud reviews and treatment. With the market’s intense focus on real-time decisions and customer experience, it seems that fraud processing isn’t always keeping up with the trends. I’ve been involved in several lively discussions on this topic. On one side of the argument sit the analytical experts who are incredibly good at distilling mountains of detailed information into the most accurate fraud risk prediction possible. Their work is intended to relieve users from the burden of scrutinizing all of that data. On the other side of the argument sits the human side of the debate. Their position is that only a human being is able to balance the complexity of judging risk with the sensitivity of handling a potential customer. All of this has led me to consider the pros and cons of manual fraud reviews. The Pros of Manual Review When we consider the requirements for review, it certainly seems that there could be a strong case for using a manual process rather than artificial intelligence. Human beings can bring knowledge and experience that is outside of the data that an analytical decision can see. Knowing what type of product or service the customer is asking for and whether or not it’s attractive to criminals leaps to mind. Or perhaps the customer is part of a small community where they’re known to the institution through other types of relationships—like a credit union with a community- or employer-based field of membership. In cases like these, there are valuable insights that come from the reviewer’s knowledge of the world outside of the data that’s available for analytics. The Cons of Manual Review When we look at the cons of manual fraud review, there’s a lot to consider. First, the costs can be high. This goes beyond the dollars paid to people who handle the review to the good customers that are lost because of delays and friction that occurs as part of the review process. In a past webinar, we asked approximately 150 practitioners how often an application flagged for identity discrepancies resulted in that application being abandoned. Half of the audience indicated that more than 50% of those customers were lost. Another 30% didn’t know what the impact was. Those potentially good customers were lost because the manual review process took too long. Additionally, the results are subjective. Two reviewers with different levels of skill and expertise could look at the same information and choose a different course of action or make a different decision. A single reviewer can be inconsistent, too—especially if they’re expected to meet productivity measures. Finally, manual fraud review doesn’t support policy development. In another webinar earlier this year, a fraud prevention practitioner mentioned that her organization’s past reliance on manual review left them unable to review fraud cases and figure out how the criminals were able to succeed. Her organization simply couldn’t recreate the reviewer’s thought process and find the mistake that lead to a fraud loss. To Review or Not to Review? With compelling arguments on both sides, what is the best practice for manually reviewing cases of fraud risk? Hopefully, the following list will help: DO: Get comfortable with what analytics tell you. Analytics divide events into groups that share a measurable level of fraud risk. Use the analytics to define different tiers of risk and assign each tier to a set of next steps. Start simple, breaking the accounts that need scrutiny into high, medium and low risk groups. Perhaps the high risk group includes one instance of fraud out of every five cases. Have a plan for how these will be handled. You might require additional identity documentation that would be hard for a criminal to falsify or some other action. Another group might include one instance in every 20 cases. A less burdensome treatment can be used here – like a one-time-passcode (OTP) sent to a confirmed mobile number. Any cases that remain unverified might then be asked for the same verification you used on the high-risk group. DON’T: Rely on a single analytical score threshold or risk indicator to create one giant pile of work that has to be sorted out manually. This approach usually results in a poor experience for a large number of customers, and a strong possibility that the next steps are not aligned to the level of risk. DO: Reserve manual review for situations where the reviewer can bring some new information or knowledge to the cases they review. DON’T: Use the same underlying data that generated the analytics as the basis of a review. Consider two simplistic cases that use a new address with no past association to the individual. In one case, there are several other people with different surnames that have recently been using the same address. In the other, there are only two, and they share the same surname. In the best possible case, the reviewer recognizes how the other information affects the risk, and they duplicate what the analytics have already done – flagging the first application as suspicious. In other cases, connections will be missed, resulting in a costly mistake. In real situations, automated reviews are able to compare each piece of information to thousands of others, making it more likely that second-guessing the analytics using the same data will be problematic. DO: Focus your most experienced and talented reviewers on creating fraud strategies. The best way to use their time and skill is to create a cycle where risk groups are defined (using analytics), a verification treatment is prescribed and used consistently, and the results are measured. With this approach, the outcome of every case is the result of deliberate action. When fraud occurs, it’s either because the case was miscategorized and received treatment that was too easy to discourage the criminal—or it was categorized correctly and the treatment wasn’t challenging enough. Gaining Value While there is a middle ground where manual review and skill can be a force-multiplier for strong analytics, my sense is that many organizations aren’t getting the best value from their most talented fraud practitioners. To improve this, businesses can start by understanding how analytics can help group customers based on levels of risk—not just one group but a few—where the number of good vs. fraudulent cases are understood. Decide how you want to handle each of those groups and reserve challenging treatments for the riskiest groups while applying easier treatments when the number of good customers per fraud attempt is very high. Set up a consistent waterfall process where customers either successfully verify, cascade to a more challenging treatment, or abandon the process. Focus your manual efforts on monitoring the process you’ve put in place. Start collecting data that shows you how both good and bad cases flow through the process. Know what types of challenges the bad guys are outsmarting so you can route them to challenges that they won’t beat so easily. Most importantly, have a plan and be consistent. Be sure to keep an eye out for a new post where we’ll talk about how this analytical approach can also help you grow your business. Contact us

Establishing a strong digital strategy remains a top priority for most financial institutions. With more eyes on screens and electronic devices, the pandemic-induced shift to digital has increased the need to meet consumers where they are. New Innovations As a Result of an Accelerated Shift to Digital  In Ernst & Young’s 2019 biannual Global Fintech Adoption Index, 46% of American respondents indicated they were using at least one fintech service. Fast forward, COVID-19 has accelerated the American adoption rate to 59%, according to a September survey conducted by Plaid, a leading digital payments infrastructure company. This shift to digital also resulted in an uptick in the creation of banking and savings processes that leverage advanced technologies. For example, digital-first technologies and artificial intelligence (AI) are changing the prescreen landscape as never before. For financial institutions, smart prescreen marketing solutions, coupled with a traditional approach to personalized service, present vast opportunities to build deeper consumer relationships. However, implementing an effective strategy can be challenging. In a recent webinar, Experian’s Vice President of Product Management Jacob Kong tackled the topic of using new analytics and AI to create a digital-first strategy. Joined by Mark Sievewright, founder of Sievewright & Associates and co-author of Digital Life, and Devon Kinkead, CEO of Micronotes.ai, they explored the evolution of banking and the possibilities offered by pairing data with technology in our new digital world. Watch the full webinar, 'Digital-First Strategies: New Analytics and Artificial Intelligence for Marketing,' and learn more about: The shift to digital life and banking, new analytics and AI How data and information value empowers prescreen marketing Emerging technologies and new tools that can support aggressive growth and marketing initiatives while mitigating risk How Experian is joining forces with Micronotes.ai to launch Micronotes ReFI powered by Experian, to help lower customers’ or members’ borrowing costs by refinancing mispriced debt Learn more about Micronotes ReFI powered by Experian To explore how Experian’s solutions and capabilities can power your prescreen and marketing strategies, please visit our solutions page or contact us for more information. Contact Us

For credit unions of all sizes, choosing a strategic partner with the right tools, capabilities, and industry expertise to support growth while minimizing expenses is a decision critical to the bottom line. This is especially important, since the goal of achieving sustainable growth has continued to be a trending topic for credit unions since the start of the pandemic. According to this CU Times analysis of NCUA data, the fourth quarter of 2020 showed that high overhead per assets was the main factor holding down net income, and credit unions with less than $1 billion in assets fared the worst. These high overhead costs kept margins low and served to be a key contributing factor in gauging a credit union’s profitability. Overcoming this problem lies not only in improving operational efficiency, but in seeking out partners that can provide innovative insight and “right-sized,” scalable solutions to help credit unions effectively grow at a strategic pace. The less money a credit union spends earning each dollar, the more operationally efficient and resource-savvy it becomes—which in turn generates more value for both the credit union and its members. So how can a credit union successfully assess a potential partner’s ability to help them achieve goals for sustainable growth? Asking three key questions can reveal a potential partner’s operational prowess and their ability to understand and offer the right solutions tailored for an individual credit union’s need. Minimize Overhead with a Partner Who Can Help Accelerate and Support Sustainable Growth: Evaluation Questions to Ask 1. Does my potential partner offer solutions to ease the strain on staff, or help automate time-consuming, repetitive tasks and processes? Automation is not only for large credit unions. Employees at credit unions with $4 billion and less in assets often wear many hats and manage the full spectrum of credit activities, leaving leaders to ponder how much time staff is spending on rote, manual tasks throughout the end-to-end member lifecycle. As a result, credit unions are turning to automated decisioning to streamline repetitive tasks and meet increasing member expectations, while also reducing risk. To drive sustainable growth, credit unions will want to look at current processes as a means of measuring efficiency. Can existing programs handle growth to scale in all areas of the business? How can digital lending automation be increased and free up more time for staff to focus attention where it is needed most, such as high-value engagements with members and delivering a personalized member experience? Can self-service tools save your credit union valuable time and increase employee satisfaction? 2. Does my potential partner have access to the right data, advanced analytics and technology to help optimize credit decisioning? As credit unions consider different ways to minimize overhead and accelerate growth, the last few years have shown that automation, coupled with advanced analytics and technology, has taken on a second wave of focus and intense interest. A significant opportunity pertaining to automation is supporting decisioning throughout the member lifecycle, again, eliminating the need for manual processes that cannibalize time and resources. For example, access to advanced analytics and data at the onset of account acquisition can quickly inform a lender as to whether a new account should be approved or declined. Furthermore, it also presents an opportunity to lend deeper. Credit unions can leverage expanded datasets to perform an analysis on rejected applicants and make more predictive decisions – leading to incremental loans. Additionally, lenders have identified other areas where automated decisioning could speed up processes that once required manual evaluation – from account and portfolio management, to marketing and prescreening efforts, to managing early and late-stage delinquent accounts. By leveraging a partner who can support optimizing credit decisioning with the freshest data and analytics, credit unions can routinely and consistently be sure they’re making the right offers and decisions to the right customer at the right time. 3. Does my potential partner offer digital-first strategies and solutions that help reduce friction and improve the member experience? More and more members are interacting and engaging with their credit unions via digital channels. To meet their demands, credit unions – who have historically prioritized other initiatives over digital transformation– are quickly pivoting and rethinking their digital strategy to offer best-in-class digital banking and borrowing experiences, while also reducing friction. Part of this strategy includes smart, easy and well-designed applications that support sustainable growth simply by streamlining offers and reducing abandonment. When considering a potential partner, take into consideration their ability to assist with digital-first solutions, including: Real-time income and employment verification, and fraud tools to quickly and accurately confirm important factors, including the legitimacy of members, and streamline the borrowing process with minimal friction. Instant prescreen, self-service prequalification and instant credit to offer fast, easy, and convenient real-time credit decisions for members. Additionally, improving lending economics with a digital-first pre-qualification tool can not only better serve members, but also drive more apps and grow loans. Artificial intelligence, machine learning and other innovative technologies to enhance underwriting and decrease both hard inquiries on applications and the need for extensive underwriter review. Prequalification tools powered by innovative technology solutions can lead to efficient use of underwriter resources and act as a filter in front of the LOS to remove unqualified applications from hard inquiries. Technology that integrates with multiple lending and core systems and delivers solutions that integrate with multiple systems and channels. For example, to help improve conversion, the borrower experience can be offered a simple application that is designed to “get to offer” as fast as possible. This helps reduce abandonment. The process can be further streamlined by integrating data sources for ID verification, auto fill assistance and adding integrations with existing lending and core systems. To learn more about Experian and how our solutions can support and grow your credit union, contact us now. Contact Us