Data & Analytics
AI, machine learning, and Big Data – these are no longer just buzzwords. The advanced analytics techniques and analytics-based tools that are available to financial institutions today are powerful but underutilized. And the 30% of banks, credit unions and fintechs successfully deploying them are driving better data-driven decisions, more positive customer experiences and stronger profitability. As the opportunities surrounding advanced analytics continue to grow, more lenders are eager to adopt these capabilities to make the most of their datasets. And it’s understandable that financial institution are excited at the possibilities and insights that advanced analytics can bring to their business. However, there are some key considerations to keep in mind as you begin this important digital transformation. Here are three things you should do as your financial institution begins its advanced analytics journey. Ensure consistent and clean data quality Companies have a plethora of data and information on their customers. The main hurdles that many organizations face is being able to turn this information into a clean and cohesive dataset and formulating an effective and long-term data management strategy. Trying to implement advanced analytic capabilities while lacking an effective data governance strategy is like building a house on a poor foundation – likely to fail. Data quality issues, such as inconsistent data, data gaps, and incomplete and duplicated data, also haunt many organizations, making it difficult to complete their analytics objectives. Ensuring that issues in data quality are managed is the key to gaining the correct insights for your business. Establish and maintain a single view of customers The power of advanced analytics can only be as strong as the data provided. Unfortunately, many companies don’t realize that advanced analytics is much more powerful when companies are able to establish a single view of their customers. Companies need to establish and maintain a single view of customers in order to begin implementing advanced analytic capabilities. According to Experian research, a single customer view is a consistent, accurate and holistic view of your organization’s customers, prospects, and their data. Having full visibility and a 360 view into your customers paves the way for companies to make personalized, relevant, timely and precise decisions. But as many companies have begun to realize, getting this single view of customers is easier said than done. Organizations need to make sure that data should always be up-to-date, unique and available in order to begin a complete digital transformation. Ensure the right resources and commitment for your advanced analytics initiative It’s important to have the top-down commitment within your organization for advanced analytics. From the C-suite down, everyone should be on the same page as to the value analytics will bring and the investment the project might require. Organizations that want to move forward with implementing advanced analytic capabilities need to make sure to set aside the right financial and human resources that will be needed for the journey. This may seem daunting, but it doesn’t have to be. A common myth is that the costs of new hardware, new hires and the costs required to maintain, configure, and set up new technology will make advanced analytics implementation far too expensive and difficult to maintain. However, many organizations don’t realize that it’s not necessary to allocate large capital expenses to implement advanced analytics. All it takes is finding the right-sized solution with configurations to fit the team size and skill level in your organization. Moreover, finding the right partner and team (whether internal or external) can be an efficient way to fill temporary skills gaps on your team. No digital transformation initiative is without its challenges. However, beginning your advanced analytics journey on the right footing can deliver unparalleled growth, profitability and opportunities. Still not sure where to begin? At Experian, we offer a wide range of solutions to help you harness the full power and potential of data and analytics. Our consultants and development teams have been a game-changer for financial institutions, helping them get more value, insight and profitability out of their data and modeling than ever before. Learn More
Fintech is quickly changing. The word itself is synonymous with constant innovation, agile technology structures and being on the cusp of the future of finance. The rapid rate at which fintech challengers are becoming established, is in turn, allowing for greater consumer awareness and adoption of fintech platforms. It would be easy to assume that fintech adoption is predominately driven by millennials. However, according to a recent market trend analysis by Experian, adoption is happening across multiple generational segments. That said, it’s important to note the generational segments that represent the largest adoption rates and growth opportunities for fintechs. Here are a few key stats: Members of Gen Y (between 24-37 years old) account for 34.9% of all fintech personal loans, compared to just 24.9% for traditional financial institutions. A similar trend is seen for Gen Z (between 18-23 years old). This group accounts for 5% of all fintech personal loans as compared to 3.1% for traditional Let’s take a closer look at these generational segments… Gen Y represents approximately 19% of the U.S. population. These consumers, often referred to as “millennials,” can be described as digital-centric, raised on the web and luxury shoppers. In total, millennials spend about $600 billion a year. This group has shown a strong desire to improve their credit standing and are continuously increasing their credit utilization. Gen Z represents approximately 26% of the U.S. population. These consumers can be described as digital centric, raised on the social web and frugal. The Gen Z credit universe is growing, presenting a large opportunity to lenders, as the youngest Gen Zers become credit eligible and the oldest start to enter homeownership. What about the underbanked as a fintech opportunity? The CFPB estimates that up to 45 million people, or 24.2 million households, are “thin-filed” or underbanked, meaning they manage their finances through cash transactions and not through financial services such as checking and savings accounts, credit cards or loans. According to Angela Strange, a general partner at Andreessen Horowitz, traditional financial institutions have done a poor job at serving underbanked consumers affordable products. This has, in turn, created a trillion-dollar market opportunity for fintechs offering low-cost, high-tech financial services. Why does all this matter? Fintechs have a unique opportunity to engage, nurture and grow these market segments early on. As the fintech marketplace heats up and the overall economy begins to soften, diversifying revenue streams, building loyalty and tapping into new markets is a strategic move. But what are the best practices for fintechs looking to build trust, engage and retain these unique consumer groups? Join us for a live webinar on November 12 at 10:00 a.m. PST to hear Experian experts discuss financial inclusion trends shaping the fintech industry and tactical tips to create, convert and extend the value of your ideal customers. Register now
Retailers are already starting to display their Christmas decorations in stores and it’s only early November. Some might think they are putting the cart ahead of the horse, but as I see this happening, I’m reminded of the quote by the New York Yankee’s Yogi Berra who famously said, “It gets late early out there.” It may never be too early to get ready for the next big thing, especially when what’s coming might set the course for years to come. As 2019 comes to an end and we prepare for the excitement and challenges of a new decade, the same can be true for all of us working in the lending and credit space, especially when it comes to how we will approach the use of alternative data in the next decade. Over the last year, alternative data has been a hot topic of discussion. If you typed “alternative data and credit” into a Google search today, you would get more than 200 million results. That’s a lot of conversations, but while nearly everyone seems to be talking about alternative data, we may not have a clear view of how alternative data will be used in the credit economy. How we approach the use of alternative data in the coming decade is going to be one of the most important decisions the lending industry makes. Inaction is not an option, and the time for testing new approaches is starting to run out – as Yogi said, it’s getting late early. And here’s why: millennials. We already know that millennials tend to make up a significant percentage of consumers with so-called “thin-file” credit reports. They “grew up” during the Great Recession and that has had a profound impact on their financial behavior. Unlike their parents, they tend to have only one or two credit cards, they keep a majority of their savings in cash and, in general, they distrust financial institutions. However, they currently account for more than 21 percent of discretionary spend in the U.S. economy, and that percentage is going to expand exponentially in the coming decade. The recession fundamentally changed how lending happens, resulting in more regulation and a snowball effect of other economic challenges. As a result, millennials must work harder to catch up financially and are putting off major life milestones that past generations have historically done earlier in life, such as homeownership. They more often choose to rent and, while they pay their bills, rent and other factors such as utility and phone bill payments are traditionally not calculated in credit scores, ultimately leaving this generation thin-filed or worse, credit invisible. This is not a sustainable scenario as we enter the next decade. One of the biggest market dynamics we can expect to see over the next decade is consumer control. Consumers, especially millennials, want to be in the driver’s seat of their “credit journey” and play an active role in improving their financial situations. We are seeing a greater openness to providing data, which in turn enables lenders to make more informed decisions. This change is disrupting the status quo and bringing new, innovative solutions to the table. At Experian, we have been testing how advanced analytics and machine learning can help accelerate the use of alternative data in credit and lending decisions. And we continue to work to make the process of analyzing this data as simple as possible, making it available to all lenders in all verticals. To help credit invisible and thin-file consumers gain access to fair and affordable credit, we’ve recently announced Experian Lift, a new suite of credit score products that combines exclusive traditional credit, alternative credit and trended data assets to create a more holistic picture of consumer creditworthiness that will be available to lenders in early 2020. This new Experian credit score may improve access to credit for more than 40 million credit invisibles. There are more than 100 million consumers who are restricted by the traditional scoring methods used today. Experian Lift is another step in our commitment to helping improve financial health of consumers everywhere and empowers lenders to identify consumers who may otherwise be excluded from the traditional credit ecosystem. This isn’t just a trend in the United States. Brazil is using positive data to help drive financial inclusion, as are others around the world. As I said, it’s getting late early. Things are moving fast. Already we are seeing technology companies playing a bigger role in the push for alternative data – often powered by fintech startups. At the same time, there also has been a strong uptick in tech companies entering the banking space. Have you signed up for your Apple credit card yet? It will take all of 15 seconds to apply, and that’s expected to continue over the next decade. All of this is changing how the lending and credit industry must approach decision making, while also creating real-time frictionless experiences that empower the consumer. We saw this with the launch of Experian Boost earlier this year. The results speak for themselves: hundreds of thousands of previously thin-file consumers have seen their credit scores instantly increase. We have also empowered millions of consumers to get more control of their credit by using Experian Boost to contribute new, positive phone, cable and utility payment histories. Through Experian Boost, we’re empowering consumers to play an active role in building their credit histories. And, with Experian Lift, we’re empowering lenders to identify consumers who may otherwise be excluded from the traditional credit ecosystem. That’s game-changing. Disruptions like Experian Boost and newly announced Experian Lift are going to define the coming decade in credit and lending. Our industry needs to be ready because while it may seem early, it’s getting late.
It seems like artificial intelligence (AI) has been scaring the general public for years – think Terminator and SkyNet. It’s been a topic that’s all the more confounding and downright worrisome to financial institutions. But for the 30% of financial institutions that have successfully deployed AI into their operations, according to Deloitte, the results have been anything but intimidating. Not only are they seeing improved performance but also a more enhanced, positive customer experience and ultimately strong financial returns. For the 70% of financial institutions who haven’t started, are just beginning their journey or are in the middle of implementing AI into their operations, the task can be daunting. AI, machine learning, deep learning, neural networks—what do they all mean? How do they apply to you and how can they be useful to your business? It’s important to demystify the technology and explain how it can present opportunities to the financial industry as a whole. While AI seems to have only crept into mainstream culture and business vernacular in the last decade, it was first coined by John McCarthy in 1956. A researcher at Dartmouth, McCarthy thought that any aspect of learning or intelligence could be taught to a machine. Broadly, AI can be defined as a machine’s ability to perform cognitive functions we associate with humans, i.e. interacting with an environment, perceiving, learning and solving problems. Machine learning vs. AI Machine learning is not the same thing as AI. Machine learning is the application of systems or algorithms to AI to complete various tasks or solve problems. Machine learning algorithms can process data inputs and new experiences to detect patterns and learn how to make the best predictions and recommendations based on that learning, without explicit programming or directives. Moreover, the algorithms can take that learning and adapt and evolve responses and recommendations based on new inputs to improve performance over time. These algorithms provide organizations with a more efficient path to leveraging advanced analytics. Descriptive, predictive, and prescriptive analytics vary in complexity, sophistication, and their resulting capability. In simplistic terms, descriptive algorithms describe what happened, predictive algorithms anticipate what will happen, and prescriptive algorithms can provide recommendations on what to do based on set goals. The last two are the focus of machine learning initiatives used today. Machine learning components - supervised, unsupervised and reinforcement learning Machine learning can be broken down further into three main categories, in order of complexity: supervised, unsupervised and reinforcement learning. As the name might suggest, supervised learning involves human interaction, where data is loaded and defined and the relationship to inputs and outputs is defined. The algorithm is trained to find the relationship of the input data to the output variable. Once it delivers accurately, training is complete, and the algorithm is then applied to new data. In financial services, supervised learning algorithms have a litany of uses, from predicting likelihood of loan repayment to detecting customer churn. With unsupervised learning, there is no human engagement or defined output variable. The algorithm takes the input data and structures it by grouping it based on similar characteristics or behaviors, without a defined output variable. Unsupervised learning models (like K-means and hierarchical clustering) can be used to better segment or group customers by common characteristics, i.e. age, annual income or card loyalty program. Reinforcement learning allows the algorithm more autonomy in the environment. The algorithm learns to perform a task, i.e. optimizing a credit portfolio strategy, by trying to maximize available rewards. It makes decisions and receives a reward if those actions bring the machine closer to achieving the total available rewards, i.e. the highest acquisition rate in a customer category. Over time, the algorithm optimizes itself by correcting actions for the best outcomes. Even more sophisticated, deep learning is a category of machine learning that involves much more complex architecture where software-based calculators (called neurons) are layered together in a network, called a neural network. This framework allows for much broader, complex data ingestion where each layer of the neural network can learn progressively more complex elements of the data. Object classification is a classic example, where the machine ‘learns’ what a duck looks like and then is able to automatically identify and group images of ducks. As you might imagine, deep learning models have proved to be much more efficient and accurate at facial and voice recognition than traditional machine learning methods. Whether your financial institution is already seeing the returns for its AI transformation or is one of the 61% of companies investing in this data initiative in 2019, having a clear picture of what is available and how it can impact your business is imperative. How do you see AI and machine learning impacting your customer acquisition, underwriting and overall customer experience?
Over the years, businesses have gathered a plethora of datasets on their customers. However, there is no value in data alone. The true value comes from the insights gained and actions that can be derived from these datasets. Advanced analytics is the key to understanding the data and extracting the critical information needed to unlock these insights. AI and machine learning in particular, are two emerging technologies with advanced analytics capabilities that can help companies achieve their business goals. According to an IBM survey, 61% of company executives indicated that machine learning and AI are their company’s most significant data initiatives in 2019. These leaders recognize that advanced analytics is transforming the way companies traditionally operate. It is no longer just a want, but a must. With a proper strategy, advanced analytics can be a competitive differentiator for your financial institution. Here are some ways that advanced analytics can empower your organization: Provide Personalized Customer Experiences Business leaders know that their customers want personalized, frictionless and enhanced experiences. That’s why improving the customer experience is the number one priority for 80 percent of executives globally, according to an Experian study. The data is already there – companies have insights into what products their customers like, the channels they use to communicate, and other preferences. By utilizing the capabilities of advanced analytics, companies can extract more value from this data and gain better insights to help create more meaningful, personalized and profitable lending decisions. Reduce Costs Advanced analytics allows companies to deploy new models and strategies more efficiently – reducing expenses associated with managing models for multiple lending products and bureaus. For example, OneMain Financial, was able to successfully drive down risk modeling expenses after implementing a solution with advanced analytics capabilities. Improve Accuracy and Speed to Market To stay ahead of the competition, companies need to maintain fast-moving environments. The speed, accuracy and power of a company’s predictive models and forecasts are crucial for success. Being able to respond to changing market conditions with insights derived from advanced analytics is a key differentiator for future-forward companies. Advanced analytic capabilities empower companies to anticipate new trends and drive rapid development and deployment, creating an agile environment of continual improvement. Drive Growth and Expand Your Customer Base With the rise of AI, machine learning and big data, the opportunities to expand the credit universe is greater than ever. Advanced analytic capabilities allow companies to scale datasets and get a bird’s eye view into a consumer’s true financial position – regardless of whether they have a credit history. The insights derived from advanced analytics opens doors for thin file or credit invisible customers to be seen – effectively allowing lenders to expand their customer base. Meet Compliance Requirements Staying on top of model risk and governance should always remain top of mind for any institution. Analytical processing aggregates and pulls new information from a wide range of data sources, allowing your institution to make more accurate and faster decisions. This enables lenders to lend more fairly, manage models that stand up to regulatory scrutiny, and keep up with changes in reporting practices and regulations. Better, faster and smarter decisions. It all starts with advanced analytics. Businesses must take advantage of the opportunities that come with implementing advanced analytics, or risk losing their customers to more future-forward organizations. At Experian, we believe that using big data can help power opportunities for your company. Learn how we can help you leverage your data faster and more effectively. Learn More
To provide consumers with clear-cut protections against disturbance by debt collectors, the Consumer Financial Protection Bureau (CFPB) issued a Notice of Proposed Rulemaking (NPRM) to implement the Fair Debt Collection Practices Act (FDCPA) earlier this year. Among many other things, the proposal would set strict limits on the number of calls debt collectors may place to reach consumers weekly and clarify requirements for consumer-facing debt collection disclosures. A bigger discussion Deliberation of the debt collection proposal was originally scheduled to begin on August 18, 2019. However, to allow commenters to further consider the issues raised in the NPRM and gather data, the comment period was extended by 20 days to September 18, 2019. It is currently still being debated, as many argue that the proposed rule does not account for modern consumer preferences and hinders the free flow of information used to help consumers access credit and services. The Association of Credit and Collection Professionals (ACA International) and US House lawmakers continue to challenge the proposal, stating that it doesn’t ensure that debt collectors’ calls to consumers are warranted, nor does it do enough to protect consumers’ privacy. Many consumer advocates have expressed doubts about how effective the proposed measures will be in protecting debtors from debt collector harassment and see the seven-calls-a-week limit on phone contact as being too high. In fact, it’s difficult to find a group of people in full support of the proposal, despite the CFPB stating that it will help clarify the FDCPA, protect lenders from litigation and bring consumer protection regulation into the 21st century. What does this mean? Although we don’t know when, or if, the proposed rule will go into effect, it’s important to prepare. According to the Federal Register, there are key ways that the new regulation would affect debt collection through the use of newer technologies, required disclosures and limited consumer contact. Not only will the proposed rules apply to debt collectors, but its provisions will also impact creditors and servicers, making it imperative for everyone in the financial services space to keep watch on the regulation’s status and carefully analyze its proposed rules. At Experian, our debt collection solutions automate and moderate dialogues and negotiations between consumers and collectors, making it easier for collection agencies to connect with consumers while staying compliant. Our best-in-class data and analytics will play a key role in helping you reach the right consumer, in the right place, at the right time. Learn more
Retail banking leaders in a variety of industries (including risk management, credit, information technology and other departments) want to incorporate more data into their business strategies. By doing so, consumer banks and other financial companies benefit by expanding their markets, controlling risk, improving compliance and the customer experience. However, many companies don’t know how or where to start. The challenges? There’s just too much data – and it’s overwhelming. Technical integration issues Maintaining regulatory data and attribute governance and compliance The slow speed of adoption Join Jim Bander, PhD, analytics and optimization leader at Experian, in an upcoming webinar with the Consumer Bankers Association on Tuesday, Oct. 1, 2019 at 9:00-10:00 a.m. PT. The webinar will discuss how some of the country’s best banks – big and small – are making better, faster and more profitable decisions by using the right set of data sources, while avoiding data overload. Key topics will include: Technology Trends: Discover how the latest technology, including the cloud and machine learning, makes it easier than ever to access data, define and manage attributes throughout the enterprise and perform complex calculations in real time. Time to Market: Discover how consumer banks and other financial companies that have mastered data and attribute management are able to integrate data and attributes quickly and seamlessly. Business Benefits: Understand how advanced analytics helps financial institutions of all sizes make better business decisions. This includes growing their portfolios, mitigating fraud and credit risk, controlling operating expenses, improving compliance and enhancing the customer experience. Critical Success Factors: Learn how to stay ahead of ever-evolving business and data requirements and continuously improve your lending operations. Join us as we unveil the secrets to avoiding data overload in consumer banking. Special Offer For non-current CBA members, this webinar costs $95 to attend. However, with special discount code: EX1001, non-CBA members can attend for FREE. Register Now
The average new vehicle loan hit $32,119 in Q2 2019. Average used vehicle loan amounts reached $20,156 in Q2 2019.
The future is, factually speaking, uncertain. We don\'t know if we\'ll find a cure for cancer, the economic outlook, if we\'ll be living in an algorithmic world or if our work cubical mate will soon be replaced by a robot. While futurists can dish out some exciting and downright scary visions for the future of technology and science, there are no future facts. However, the uncertainty presents opportunity. Technology in today\'s world From the moment you wake up, to the moment you go back to sleep, technology is everywhere. The highly digital life we live and the development of our technological world have become the new normal. According to The International Telecommunication Union (ITU), almost 50% of the world\'s population uses the internet, leading to over 3.5 billion daily searches on Google and more than 570 new websites being launched each minute. And even more mind-boggling? Over 90% of the world\'s data has been created in just the last couple of years. With data growing faster than ever before, the future of technology is even more interesting than what is happening now. We\'re just at the beginning of a revolution that will touch every business and every life on this planet. By 2020, at least a third of all data will pass through the cloud, and within five years, there will be over 50 billion smart connected devices in the world. Keeping pace with digital transformation At the rate at which data and our ability to analyze it are growing, businesses of all sizes will be forced to modify how they operate. Businesses that digitally transform, will be able to offer customers a seamless and frictionless experience, and as a result, claim a greater share of profit in their sectors. Take, for example, the financial services industry - specifically banking. Whereas most banking used to be done at a local branch, recent reports show that 40% of Americans have not stepped through the door of a bank or credit union within the last six months, largely due to the rise of online and mobile banking. According to Citi\'s 2018 Mobile Banking Study, mobile banking is one of the top three most-used apps by Americans. Similarly, the Federal Reserve reported that more than half of U.S. adults with bank accounts have used a mobile app to access their accounts in the last year, presenting forward-looking banks with an incredible opportunity to increase the number of relationship touchpoints they have with their customers by introducing a wider array of banking products via mobile. Be part of the movement Rather than viewing digital disruption as worrisome and challenging, embrace the uncertainty and potential that advances in new technologies, data analytics and artificial intelligence will bring. The pressure to innovate amid technological progress poses an opportunity for us all to rethink the work we do and the way we do it. Are you ready? Learn more about powering your digital transformation in our latest eBook. Download eBook Are you an innovation junkie? Join us at Vision 2020 for future-facing sessions like: - Cloud and beyond - transforming technologies - ML and AI - real-world expandability and compliance
In today’s age of digital transformation, consumers have easy access to a variety of innovative financial products and services. From lending to payments to wealth management and more, there is no shortage in the breadth of financial products gaining popularity with consumers. But one market segment in particular – unsecured personal loans – has grown exceptionally fast. According to a recent Experian study, personal loan originations have increased 97% over the past four years, with fintech share rapidly increasing from 22.4% of total loans originated to 49.4%. Arguably, the rapid acceleration in personal loans is heavily driven by the rise in digital-first lending options, which have grown in popularity due to fintech challengers. Fintechs have earned their position in the market by leveraging data, advanced analytics and technology to disrupt existing financial models. Meanwhile, traditional financial institutions (FIs) have taken notice and are beginning to adopt some of the same methods and alternative credit approaches. With this evolution of technology fused with financial services, how are fintechs faring against traditional FIs? The below infographic uncovers industry trends and key metrics in unsecured personal installment loans: Still curious? Click here to download our latest eBook, which further uncovers emerging trends in personal loans through side-by-side comparisons of fintech and traditional FI market share, portfolio composition, customer profiles and more. Download now
Experian has been named one of the 10 participants, and only credit bureau, in the initial rollout of the SSA's new eCBSV service.
Experian Boost provides a unique opportunity to help dealers build loyalty while helping consumers.
What do movie actors Adam Sandler and Hugh Grant, jazz singer Michael Bublé, Russian literary giant Leo Tolstoy, and Colonel Sanders, the founder of KFC, have in common? Hint, it’s not a Nobel Prize for Literature, a Golden Globe, a Grammy Award, a trademark goatee, or a “finger-lickin’ good” bucket of chicken. Instead, they were all born on September 9, the most common birth date in the U.S. Baby Boom According to real birth data compiled from 20 years of American births, September is the most popular month to give birth to a child in America – and December, the most popular time to make one. With nine of the top 10 days to give birth falling between September 9 and September 20, one may wonder why the birth month is so common. Here are some theories: Those who get to choose their child’s birthday due to induced and elective births tend to stay away from the hospital during understaffed holiday periods and may plan their birth date around the start of the school year. Several of the most common birth dates in September correspond with average conception periods around the holidays, where couples likely have more time to spend together. Some studies within the scientific community suggest that our bodies may actually be biologically disposed to winter conceptions. While you may not be feeling that special if you were born in September, the actual differences in birth numbers between common and less common birthdays are often within just a few thousand babies. For example, September 10, the fifth most common birthday of the year, has an average birth rate of 12,143 babies. Meanwhile, April 20, the 328th most common birthday, has an average birth rate of 10,714 newborns. Surprisingly, the least common birthdays fall on Christmas Eve, Christmas Day and New Year’s Day, with Thanksgiving and Independence Day also ranking low on the list. Time to Celebrate – but Watch out! Statistically, there’s a pretty good chance that someone reading this article will soon be celebrating their birthday. And while you should be getting ready to party, you should also be on the lookout for fraudsters attempting to ruin your big day. It’s a well-known fact that cybercriminals can use your birth date as a piece of the puzzle to capture your identity and commit identity theft – which becomes a lot easier when it’s being advertised all over social media. It’s also important for employers to safeguard their organization from fraudsters who may use this information to break into corporate accounts. While sharing your birthday with a lot of people could be a good or bad thing depending on how much undivided attention you enjoy – you’re in great company! Not only can you plan a joint party with Michelle Williams, Afrojack, Cam from Modern Family, four people I went to high school with on Facebook and a handful of YouTube stars that I’m too old to know anything about, but there will be more people ringing in your birthday than any other day of the year! And that’s pretty cool.
Pickups are the most common vehicle in operation at 20% share today and hold 16.5% of new vehicle registrations in the market in Q1 2019.
Many companies rely on attributes for decisioning but lack the resources needed to invest in developing, managing, and updating the attributes themselves. Experian is there to guide you every step of the way with our Attribute Toolbox – our source independent solution that provides maximum flexibility and multiple data sources you can use in the calculation and management of attributes. To create and manage our attributes, Experian has established development principles and created a set methodology to ensure that our attribute management system works across the attribute life cycle. Here’s how it works: Develop Attributes The attribute development process includes: discovery, exploratory data analysis, filter leveling, and the development of attributes. When we create attributes, Experian takes great care to ensure that we: Analyze the available data elements and how they are populated (the frequencies of fields). Determine a “sensible” definition of the attribute. Evaluate attribute frequencies. Review consumer credit reports, where possible. Refine the definition and assess more frequencies and examples. Test Attributes Before implementing, Experian performs an internal audit of filters and attributes. Defining, coding and auditing filters is 80% of the attribute development process. The main objective of the auditing process is to ensure both programming and logical accuracy. This involves electronic and manual auditing and requires a thorough review of all data elements used in development. Deploy Attributes Deployment is very similar to attribute testing. However, in this case, the primary objective of the deployment audit is to ensure both the programming and logical accuracy of the output is executing correctly on various platforms. We aim to maintain consistency among various business lines and products, between batch and online environments across the life cycle, and wherever your models are deployed: on premises, in the cloud, and off-site in your partners’ systems. Govern Attributes Experian places a robust attribute governance process in place to ensure that our attributes remains up-to-date and on track with internal and external compliance regulations and audits. New learnings, industry and regulatory changes can lead to updated attributes or new attributes over time. Because attributes are ever-changing, we take great care to expand, update and add new attributes over time based on three types of external changes: economic, bureau, and reporting changes. Fetch Data While we gather the data, we ensure that you can integrate a variety of external data sources, including: consumer bureau, business, fraud, and other data sources. Attributes need to be: Highly accurate. Suitable for use across the Customer Life Cycle. Suitable for use in credit decisioning and model development. Available and consistent across multiple platforms. Supportive and adaptable to ever-evolving regulatory considerations. Thoroughly documented and monitored. Monitor Performance We generate attribute distribution reports and can perform custom validations using data from credit reporting agencies (CRAs) and other data providers. This is based on monthly monitoring to ensure continued integrity and stability to stand up to regulatory scrutiny and compliance regulations. Variations that exceed predetermined thresholds are identified, quantified, and explained. If new fields or data values within existing fields are announced, we assess the impact and important of these values on attributes – to determine if revisions are needed. Maintain Attributes Credit bureau data updates, new attributes in response to market needs, compliance requirements, corrections in logic where errors are identified or improvements to logic often lead to new version releases of attributes. With each new version release, Experian takes care to conduct thorough analyses comparing the previous and current set of attributes. We also make sure to create detailed documentation on what’s changed between versions, the rationale for changes and the impact on existing attributes. Experian Attributes are the key to unlocking consistent, enhanced and more profitable decisions. Our data analysts and statisticians have helped hundreds of clients build custom attributes and custom models to solve their business problems. Our Attribute Toolbox makes it easier to deploy and manage attributes across the customer lifecycle. We give companies the power to code, manage, test, and deploy all types of attributes, including: Premier AttributesSM, Trended 3DTM, and custom attributes – without relying on a third-party. We do the heavy lifting so that you don’t have to. Learn More
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