Tag: Decisioning
At some point a lender may need to issue an RFI or an RFP for a credit decisioning system. In this latest installment of “working with vendors” let’s dive into some best practices for writing RFIs and RFPs that will help you more quickly and efficiently understand the capabilities of a vendor. First, have one person (or at most a very small group) review the document before it goes out to vendors. Too often these kinds of documents seem like they’re just cut and pasted together without any concern if they paint a coherent picture. If it’s worth the time to write an RFI/RFP, then it’s worth the time to get it right so that the vendor responses make sense. If your document paints an inconsistent picture, a vendor may not know what products will best serve your requirements. In turn, precious time will be wasted in discussions around what’s being proposed. Here are some things to make clear in the document: For what part of the credit life cycle does this RFI/RFP apply (prospecting, origination, account management or collections)? If the request covers more than one part of the life cycle, make clear which questions apply to which part of the life cycle. Do you need a system that processes in batch or real-time requests (or both)? For example, a credit card account management solution can process accounts in batch (for proactive line management), in real time (for reactive requests) or possibly even both. Let the vendor know what it is you’re trying to do, as there may be different systems involved in processing these requests. Do you want this system hosted at the vendor, a third party (like AWS, Azure, etc.) or installed on premises? If you have a preference, let the vendor know. If you have no preference, ask the vendor what they can support. In general, consider playing down or skip detailed pricing questions. There’s nothing wrong with asking for a price range. For credit decisioning systems, detailed pricing is difficult for the vendor since there are often high levels of unknown customization to do. A better question might be, “What things will the vendor have to know in order to accurately price the solution? What are the logical next steps to get more accurate pricing? What’s the typical range of pricing in a solution such as this and what drives that range?” Will you be acting as an aggregator? Sometimes systems are created as front ends to several lenders. For example, a client may want to create a website where a borrower can “shop” among several lenders. This is certainly doable but carries with it a whole host of legal, compliance, business and technical questions. In my opinion, I’d skip the RFI/RFP in this situation and have a robust sit down directly with the vendors. This option will likely be far more productive. Ask more open-ended questions. “How does the solution perform task X?” as opposed to, “Do you support Y?” Often, there’s more than one way to accomplish a task. Asking more open-ended questions will yield a more comprehensive answer from the vendor rather than a simple yes or no response. It also gives you the opportunity to learn about the latest decisioning techniques. Be careful that you have not copied old RFP questions that are no longer relevant. I’ve had clients ask if we support Bernoulli Boxes (a mid-80s kind of floppy disk), or whether we support OS/2, etc. I’ve even had questions about supporting a particular printer. These kinds of questions are centered on the support of the operating system and not a particular vendor’s credit decisioning software. Instead of asking yes/no technology questions, ask for a typical sample architecture. Ask what kinds of APIs are supported (REST, SOAP/XML, etc.). Ask about the solution’s capabilities to call third-party systems (both internal and external). Ask fewer, but more in-depth questions. If the solution needs screens, be clear which screens you’re talking about. Do you need screens to make rule adjustments or configuration changes? Do you need screens for manual review or some sort of case management? Do you need consumer-facing screens where borrowers can type in their application data? If you need screens, be clear on the task the screens should perform. If you have particular concerns, ask them in an open-ended way. For example, “The solution will have to exchange file-based data with a mainframe. How can your solution best satisfy this requirement?” In general, state your requirement not the technology to use. A preamble or brief executive summary is useful to get the big picture across before the vendor delves into any questions. A paragraph or two can go a long way to help the vendor better assess your requirements and provide more meaningful answers to you. This works well because it’s easier to give the big picture in a few paragraphs as opposed to sprinkled around in multiple questions. To summarize, be clear on your requirements and provide a more open-ended format for the vendor to respond. This will save both you and the vendor a lot of time. In section three, I’ll cover evaluating vendors.
DDigitalization, also known as the process of using digital technology to provide new opportunities for revenue and growth, continues to remain a top priority for many organizations in 2021. In fact, IDC predicts that by 2024, “over 50% of all IT spending will be directly for digital transformation and innovation (up from 31% in 2018).”[1] By combining data and analytics, companies can make better and more instant decisions, meet customer expectations, and automate for greater efficiency. Advances in AI and machine learning are just a few areas where companies are shifting their spend. Download our new white paper to take a deep dive into other ongoing analytics trends that seem likely to gain even greater traction in 2021. These trends will include: Increased digitalization – Data is a company’s most valuable asset. Companies will continue utilizing the information derived from data to make better data-driven decisions. AI for credit decisioning and personalized banking – Artificial intelligence will play a bigger role in the world of lending and financial services. By using AI and custom machine learning models, lending institutions will be able to create new opportunities for a wider range of consumers. Chatbots and virtual assistants – Because customers have come to expect excellent customer services, companies will increase their usage of chatbots and virtual assistants to facilitate conversations. Cloud computing – Flexible, scalable, and cost-effective. Many organizations have already seen the benefits of migrating to the cloud – and will continue their transition in the next few years. Biometrics – Physical and behavioral biometrics have been identified as the next big step for cybersecurity. By investing in these new technologies, companies can create seamless interactions with their consumers. Download Now [1] Gens, F., Whalen, M., Carnelley, P., Carvalho, L., Chen, G., Yesner, R., . . . Wester, J. (2019, October). IDC FutureScape: Worldwide IT Industry 2020 Predictions. Retrieved January 08, 2021,
Perhaps your loan origination system (LOS) doesn’t have the flexibility that you require. Perhaps the rules editor can’t segment variables in the manner that you need. Perhaps your account management system can’t leverage the right data to make decisions. Or perhaps your existing system is getting sunset. These are just some of the many reasons a company may want to investigate the marketplace for new credit decisioning software. But RFIs and RFPs aren’t the only way to find new decisioning software. After working in credit services decisioning for over 20 years — and seeing hundreds of RFPs and presenting thousands of solutions and proposed architectures — I’ve formed a few opinions about how I would go about things if I were in the customer’s seat and have broken that into a three-part series. Part 1 will cover everything up to issuing an RFI or RFP. Part 2 will discuss writing an RFP or RFI. Part 3 will cover evaluating vendors. Let’s go. If you’re looking to buy new decisioning software, your first inclination might be to issue an RFI or an RFP. However, that may not be the best idea. Here’s an issue that I frequently see. Vendors are constantly evolving their products. How a product did feature X two years ago might be completely different now. The terminology that the industry uses might have changed, and new capabilities (like machine learning) might have come about and changed whole sets of functionalities. The first decision point is to ask yourself a question, “Do I know exactly what I want or am I trying to generally learn what is out there?” An RFI or RFP isn’t always the greatest way to exchange information about a product. From a vendor’s standpoint, a feature-rich, complex system has to be reduced down to a few text answers or (worst yet) a series of yes or no answers. It all boils down to nuance. On many occasions, I’ve faced a dilemma when answering an RFP question, “This question is unclear; if the customer means X, the answer is yes; if they mean Y, the answer is no.” If I were in a room with the customer, I could ask them the question, they could provide clarification and I could then provide the accurate answer. There would be more opportunity to have a back and forth, “Oh when you said X, this is what you meant ….” All of that back and forth is lost with an RFI or RFP, or at least delayed until the (hopefully selected) vendor gets a chance to present in front of a live audience. Also, consider that vendors are eager to educate you about their product. They know exactly how the product works and they’re happy to answer your questions. It’s perfectly reasonable to go to a vendor with prewritten questions and thoughts and to pose those questions during a call or demonstration with the vendor. Nothing would prevent a customer from using the same questions for each vendor and evaluating them based on their answers. All of this can be done without issuing an RFI or RFP. In conclusion, I’d offer the following points to think about before issuing an RFI or RFP: A customer can provide questions that they want answered during a demonstration of a credit decisioning product. These same questions can be used to provide an initial assessment of several vendors. A customer’s understanding of a vendor’s capabilities is likely 10x faster and deeper with an interactive session versus reading the answers in a questionnaire. Nuanced and follow-up questions can be asked to gather a complete understanding. Alternative solutions can be explored. This exercise doesn’t have to replace an RFP but instead can better inform the customer about the questions they need answered in order to issue an RFP. Don’t be afraid to talk to a vendor, even if you’re not sure what you want in a new product. In fact, talk to several vendors. More than likely, you’ll learn a lot more via a discussion than you will via an RFI questionnaire. What’s good about an RFI or RFP is coming in with prepared questions. That way, you can judge each vendor using the same criteria but, if possible, get the answers to those questions via an interactive session with the vendors. Next: How to write an effective RFP or RFI.
Dealing with challenges is part of the collections process. But in today’s economic environment, there are even more barriers to overcome. Since it is unclear how long the COVID-19 pandemic and associated financial stress will last, debt collection agencies and departments must evolve and refine their collections and recovery capabilities. As you step into the new collections environment, it will be imperative to keep pace with shifting consumer behaviors and trends and properly react, adapt and engage. Recent data findings show that many consumers are still worried about their finances and ability to pay down existing debt: Revolving Card Credit Line Increases (CLI) are up 78.4% overall1 Almost 3% of auto loans are 30+ Days Past Due (DPD) 2% of unsecured personal loans are 30+ Days Past Due (DPD) New account originations are up 0.8% overall Download our latest white paper to discover more industry trends, outlooks for 2021, and the benefits of leveraging data and advanced analytics to develop better strategies, make more profitable decisions and better serve consumers in times of continued economic uncertainty. Access white paper Learn more 1Findings from Experian\'s Ascend Market Insights Dashboard, data based on number of accounts. Data refreshed: January 24, 2021.
Time – it’s the only resource we can’t get more of, which is why we tend to obsess over saving it. Despite this obsession, it can be hard for us to identify time-wasting activities. From morning habits to credit decisioning, processes and routines that seem, well, routine, can get in the way of maximizing how we use our time. Identifying the Problem Every morning, I used to turn on my coffee maker, walk to the bathroom to take my multivitamin, then walk back into the kitchen to finish making my coffee. This required maybe twenty steps to the bathroom and twenty steps back, and while this isn’t a huge amount of time—half a minute at best—it’s not insignificant, especially in the morning when time feels particularly precious. One day, I realized I could eliminate the waste by moving my multivitamin to the cabinet above my coffeemaker. What if we could all make minor changes to enhance our efficiency both at home and at work? Imagine how much time we could save by cutting out unnecessary steps. And how saving that time could help drive significant revenue increases. Time Equals Money When businesses waste time with unnecessary steps, that’s money from their bottom line, and out of the pockets of people who are connected to them. Over the last several years, a new time saver has emerged – Application Programming Interface (API). APIs allow application programs to communicate with other operating systems or control programs through a series of server requests or API calls, enabling seamless interaction, data sharing and decisioning. Experian’s partners utilize our ever-growing suite of APIs to quickly access better data, making existing processes more effective and routines more efficient. In the past, banks and other partners had to send files back and forth to Experian when they needed decisioning on a customer’s credit-worthiness prior to approving a new loan or extending a credit limit increase. Now, partners can have their origination system call an Experian API and send their data through that. Our system processes it and sends it back in milliseconds, giving the lenders real-time decisioning rather than shuttling information back and forth unnecessarily. Instead of effectively walking away from one process (assisting the customer/making coffee) to start another (retrieving credit info/walking down the hall to take the multivitamin), our partners are able to link these processes up and save time, allowing them to capitalize on the presence and interest of their customer. The Proof When Washington State Employees Credit Union, the second-largest credit union in the state, realized they needed to make a change to keep pace with increasing competition, they turned to Experian. With our solution, the credit union is now able to provide its members with instant credit decisioning through their online banking platform. This real-time decisioning at the point of member-initiated contact increased the credit union’s loan and credit applications by 25%. Additionally, member satisfaction increased, with 90% of members finding the simplified prequalification process to be more efficient. By accessing Experian’s decisioning services through your existing connection, lenders can to save time and match consumers with the products that match their credit profile before they apply – increasing approval rates once the application is submitted. Best of all, the entire process with the consumers is completed within seconds. Find out how Experian’s solutions can help you improve your existing processes and cut out unnecessary steps. Get started
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
Alex Lintner, Group President at Experian, recently had the chance to sit down with Peter Renton, creator of the Lend Academy Podcast, to discuss alternative credit data,1 UltraFICO, Experian Boost and expanding the credit universe. Lintner spoke about why Experian is determined to be the leader in bringing alternative credit data to the forefront of the lending marketplace to drive greater access to credit for consumers. “To move the tens of millions of “invisible” or “thin file” consumers into the financial mainstream will take innovation, and alternative data is one of the ways which we can do that,” said Lintner. Many U.S. consumers do not have a credit history or enough record of borrowing to establish a credit score, making it difficult for them to obtain credit from mainstream financial institutions. To ease access to credit for these consumers, financial institutions have sought ways to both extend and improve the methods by which they evaluate borrowers’ risk. By leveraging machine learning and alternative data products, like Experian BoostTM, lenders can get a more complete view into a consumer’s creditworthiness, allowing them to make better decisions and consumers to more easily access financial opportunities. Highlights include: The impact of Experian Boost on consumers’ credit scores Experian’s take on the state of the American consumer today Leveraging machine learning in the development of credit scores Expanding the marketable universe Listen now Learn more about alternative credit data 1When we refer to \"Alternative Credit Data,\" this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term \"Expanded FCRA Data\" may also apply in this instance and both can be used interchangeably.
Many may think of digital transformation in the financial services industry as something like emailing a PDF of a bank statement instead of printing it and sending via snail mail. After working with data, analytics, software and fraud-prevention experts, I have found that digital transformation is actually much more than PDFs. It can have a bigger and more positive influence on a business’s bottom line – especially when built on a foundation of data. Digital transformation is the new business model. And executives agree. Seventy percent of executives feel the traditional business model will disappear in the next five years due to digital transformation, according to recent Experian research. Our new e-book, Powering digital transformation: Transforming the customer experience with data, analytics and automation, says, “we live in a world of ‘evolve or fail.’ From Kodak to Blockbuster, we’ve seen businesses resist change and falter. The need to evolve is not new. What is new is the speed and depth needed to not only compete, but to survive. Digital startups are revolutionizing industries in months and years instead of decades and centuries.” So how do businesses evolve digitally? First, they must understand that this isn’t a ‘one-and-done’ event. The e-book suggests that the digital transformation life cycle is a never-ending process: Cleanse, standardize and enrich your data to create features or attributes Analyze your data to derive pertinent insights Automate your models and business practices to provide customer-centric experiences Test your techniques to find ways to improve Begin the process again Did you notice the key word or phrase in each of these steps is ‘data’ or ‘powered by data?’ Quality, reliable data is the foundation of digital transformation. In fact, almost half of CEOs surveyed said that lack of data or analytical insight is their biggest challenge to digital transformation. Our digital world needs better access to and insight from data because information derived from data, tempered with wisdom, provides the insight, speed and competitive advantage needed in our hypercompetitive environment. Data is the power behind digital transformation. Learn more about powering your digital transformation in our new e-book>
The universe has been used as a metaphor for many things – vast, wide, intangible – much like the credit universe. However, while the man on the moon, a trip outside the ozone layer, and all things space from that perspective may seem out of touch, there is a new line of access to consumers. In Experian\'s latest 2019 State of Alternative Credit Data report, consumers and lenders alike weigh in on the growing data set and how they are leveraging the data in use cases across the lending lifecycle. While the topic of alternative credit data is no longer as unfamiliar as it may have been a year or two ago, the capabilities and benefits that can be experienced by financial institutions, small businesses and consumers are still not widely known. Did you know?: - 65% of lenders say they are using information beyond the traditional credit report to make a lending decision. - 58% of consumers agree that having the ability to contribute payment history to their credit file make them feel empowered. - 83% of lenders agree that digitally connecting financial account data will create efficiencies in the lending process. These and other consumer and lender perceptions of alternative credit data are now launched with the latest edition of the State of Alternative Credit Data whitepaper. This year’s report rounds up the different types of alternative credit data (from alternative financial services data to consumer-permissioned account data, think Experian BoostTM), as well as an overview of the regulatory landscape, and a number of use cases across consumer and small business lending. In addition, consumers also have a lot to say about alternative credit data: With the rise of machine learning and big data, lenders can collect more data than ever, facilitating smarter and more precise decisions. Unlock your portfolio’s growth potential by tapping into alternative credit data to expand your consumer universe. Learn more in the 2019 State of Alternative Credit Data Whitepaper. Read Full Report View our 2020 State of Alternative Credit Data Report for an updated look at how consumers and lenders are leveraging alternative credit data.
If your company is like many financial institutions, it’s likely the discussion around big data and financial analytics has been an ongoing conversation. For many financial institutions, data isn’t the problem, but rather what could or should be done with it. Research has shown that only about 30% of financial institutions are successfully leveraging their data to generate actionable insights, and customers are noticing. According to a recent study from Capgemini, 30% of US customers and 26% of UK customers feel like their financial institutions understand their needs. No matter how much data you have, it’s essentially just ones and zeroes if you’re not using it. So how do banks, credit unions, and other financial institutions who capture and consume vast amounts of data use that data to innovate, improve the customer experience and stay competitive? The answer, you could say, is written in the sand. The most forward-thinking financial institutions are turning to analytical environments, also known as a sandbox, to solve the business problem of big data. Like the name suggests, a sandbox is an environment that contains all the materials and tools one might need to create, build, and collaborate around their data. A sandbox gives data-savvy banks, credit unions and FinTechs access to depersonalized credit data from across the country. Using custom dashboards and data visualization tools, they can manipulate the data with predictive models for different micro and macro-level scenarios. The added value of a sandbox is that it becomes a one-stop shop data tool for the entire enterprise. This saves the time normally required in the back and forth of acquiring data for a specific to a project or particular data sets. The best systems utilize the latest open source technology in artificial intelligence and machine learning to deliver intelligence that can inform regional trends, consumer insights and highlight market opportunities. From industry benchmarking to market entry and expansion research and campaign performance to vintage analysis, reject inferencing and much more. An analytical sandbox gives you the data to create actionable analytics and insights across the enterprise right when you need it, not months later. The result is the ability to empower your customers to make financial decisions when, where and how they want. Keeping them happy keeps your financial institution relevant and competitive. Isn’t it time to put your data to work for you? Learn more about how Experian can solve your big data problems. >> Interested to see a live demo of the Ascend Sandbox? Register today for our webinar “Big Data Can Lead to Even Bigger ROI with the Ascend Sandbox.”
Big Data is no longer a new concept. Once thought to be an overhyped buzzword, it now underpins and drives billions in dollars of revenue across nearly every industry. But there are still companies who are not fully leveraging the value of their big data and that’s a big problem. In a recent study, Experian and Forrester surveyed nearly 600 business executives in charge of enterprise risk, analytics, customer data and fraud management. The results were surprising: while 78% of organizations said they have made recent investments in advanced analytics, like the proverbial strategic plan sitting in a binder on a shelf, only 29% felt they were successfully using these investments to combine data sources to gather more insights. Moreover, 40% of respondents said they still rely on instinct and subjectivity when making decisions. While gut feeling and industry experience should be a part of your decision-making process, without data and models to verify or challenge your assumptions, you’re taking a big risk with bigger operations budgets and revenue targets. Meanwhile, customer habits and demands are quickly evolving beyond a fundamental level. The proliferation of mobile and online environments are driving a paradigm shift to omnichannel banking in the financial sector and with it, an expectation for a customized but also digitized customer experience. Financial institutions have to be ready to respond to and anticipate these changes to not only gain new customers but also retain current customers. Moreover, you can bet that your competition is already thinking about how they can respond to this shift and better leverage their data and analytics for increased customer acquisition and engagement, share of wallet and overall reach. According to a recent Accenture study, 79% of enterprise executives agree that companies that fail to embrace big data will lose their competitive position and could face extinction. What are you doing to help solve the business problem around big data and stay competitive in your company?
Once a scorecard has been redeveloped, it is important to measure the impact of changes within the strategy by replacing the old model with the new one. This impact assessment can be completed with a swap set analysis. The term swap set refers to “swapping out” a set of bad accounts and replacing them with or “swapping in” a set of good accounts. Different approaches can be used when evaluating swap sets to optimize your strategy and keep: The same overall bad rate while increasing the approval rate. The same approval rate while lowering the bad rate. The same approval and bad rates but an increase in customer activation or customer response rates. When measuring your swap sets, remember to also include the population that doesn’t change — those accounts that would be approved or declined using either the old or new model. Learn more>
With 81% of Americans having a social media profile, you may wonder if social media insights can be used to assess credit risk. When considering social media data as it pertains to financial decisions, there are 3 key concerns to consider. The ECOA requires that credit must be extended to all creditworthy applicants regardless of race, religion, gender, marital status, age and other personal characteristics. Social media can reveal these characteristics and inadvertently affect decisions. Social media data can be manipulated. Individuals can represent themselves as financially responsible when they’re not. On the flip side, consumers can’t manipulate their payment history. When it comes to credit decisions, always remember that the FCRA trumps everything. Data is essential for all aspects of the financial services industry, but it’s still too early to click the “like” button for social media. Make more insightful decisions with credit attributes>
Call it big data, smart data or evidence-based decision-making. It’s not just the latest fad, it’s the future of how business will be guided and grow. Here are a few telling stats that show data is exploding and a new age is upon us: Data is growing faster than ever before, and we’re on track to create about 1.7 megabytes of new information per person every second by 2020. The social universe—which includes every digitally connected person—doubles in size every two years. By 2020, it will reach 44 zettabytes or 44 trillion gigabytes, according to CIO. In 2015, more than 1 billion people used Facebook and sent an average of 31.25 million messages and viewed 2.77 million videos each minute. More than 100 terabytes of data is uploaded daily to the social channel. By 2020, more than 6.1 billion smartphone users will exist globally. And there will be more than 50 billion smart connected devices in the world, all capable of collecting, analyzing and sharing a wealth of data. More than one-third of all data will pass through or exist in the cloud by 2020. The IDC estimates that by 2020, business transactions on the internet—business-to-business and business-to-consumer—will reach 450 billion per day. All of this new data means we’ll be looking at a whole new set of possibilities and a new level of complexity in the years ahead. The data itself is of great value, however, lenders need the right automated decisioning platform to store, collect, quickly process and analyze the volumes of consumer data to gain accurate consumer stories. While lenders don’t necessarily need to factor in decisioning on social media uploads and video views, there is an expectation for immediacy to know if a consumer is approved, denied or conditioned. Online lenders have figured out how to quickly capture and understand big data, and are expected to account for $122 billion in lending by 2020. This places more pressure on banks and credit unions to enhance their technology to cut down on loan approval times and move away from various manual touch points. Critics of automated decisioning solutions used in lending cite compliance issues, complacency by lenders and lack of human involvement. But a robust platform enables lenders to improve and supplement their current decisioning processes because it is: Agile: Experian hosts our client’s solutions and decisioning strategies, so we are able to make and deploy changes quickly as the needs of the market and business change, and deliver real-time instant decisions while a consumer is at the point of sale. A hosted environment also means reduced implementation timelines, as no software or hardware installation is required, allowing lenders to recognize value faster. A data work horse: Internal and external data can be pulled from multiple sources into a lender’s decisioning model. Lenders may also access an unlimited number of scores and attributes—including real-time access to credit bureau data—and integrate third-party data sources into the decisioning engine. Powerful: A robust decision engine is capable of calculating numerous predictive attributes and custom scoring models, and can also test new strategies against current decision models or perform “what if” simulations on historical data. Data collection, storage and analysis are here to stay. As will be the businesses which are savvy enough to use a solution that can find opportunities and learnings in all of that complex data, quickly curate the best possible actions to take for positive outcomes, and allow lenders and marketers to execute on those recommendations with the click of a button. To learn more about Experian’s decisioning solutions, you can additionally explore our PowerCurve and Attribute Toolbox solutions.
The 1990s brought us a wealth of innovative technology, including the Blackberry, Windows 98, and Nintendo. As much as we loved those inventions, we moved on to enjoy better technology when it became available, and now have smartphones, Windows 10 and Xbox. Similarly, technological and modeling advances have been made in the credit scoring arena, with new software that brings significant benefits to lenders who use them. Later this year, FICO will retire its Score V1, making it mandatory for those lenders still using the old software to find another solution. Now is the time for lenders to take a look at their software and myriad reasons to move to a modern credit score solution. Portfolio Growth As many as 70 million Americans either have no credit score or a thin credit file. One-third of Millennials have never bothered to apply for a credit card, and the percentage of Americans under 35 with credit card debt is at its lowest level in more than 25 years, according to the Federal Reserve. A recent study found that Millennials use cash and debit cards much more than older Americans. Over time, Millennials without credit histories could struggle to get credit. Are there other data sets that provide a window into whether a thin file consumer is creditworthy or not? Modern credit scoring models are now being used in the marketplace without negatively impacting credit quality. For example, VantageScore 3.0 allows for the scoring of 30 million to 35 million more people consumers who are typically unscoreable by other traditional generic credit models. VantageScore 3.0 does this by using a broader, deeper set of credit file data and more advanced modeling techniques. This allows the VantageScore model to more accurately predict unique consumer behaviors—is the consumer paying his utility bill on time?—and better evaluate thin file consumers. Mitigate Risk In today’s ever-changing regulatory landscape, lenders can stay ahead of the curve by relying on innovative credit score models like VantageScore. These models incorporate the best of both worlds by leaning on innovative scoring analytics that are more inclusive, while providing marketplace lenders with assurances the decisioning is both statistically sound and compliant with fair lending laws. Newer solutions also offer enhanced documentation to ease the burden associated with model risk management and regulatory compliance responsibilities. Updated scores Consumer credit scores can vary depending on the type of scoring model a lender uses. If it\'s an old, outdated version, a consumer might be scored lower. If it\'s a newer, more advanced model, the consumer has a better shot at being scored more fairly. Moving to a more advanced scoring model can help broaden the base of potential borrowers. By sticking to old models—and older scores—a sizable number of consumers are left at a disadvantage in the form of a higher interest rate, lower loan amount or even a declined application. Introducing advanced scoring models can provide a more accurate picture of a consumer. As an example, for many of the newest consumer risk models, like FICO Score 9, a consumer’s unpaid medical collection agency accounts will be assessed differently from unpaid non-medical collection agency accounts. This isn\'t true for most pre-2012 consumer risk score versions. Each version contains different nuances for increasing your score, and it’s important to understand what they are. Upgrading your credit score to the latest VantageScore credit score or FICO solution is easier than you think, with a switch to a modern solution taking no longer than eight weeks and your current business processes still in place. Are you ready to reap the rewards of modern credit scoring?
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