Tag: sandbox

Big Data, once thought to be overhyped consultant-speak, is now a term and business model so ubiquitous it underpins billions of dollars in revenue across nearly every industry. Similarly, the advanced analytics derived from big data are key to staying relevant in an everchanging global economy and to consumers with expanding expectations. But for many financial institutions, using big data and advanced analytics seemed to only be in reach for big banks with large, advanced data teams. With the expansion of the Experian Ascend Technology PlatformTM, the conversation is changing. Financial institutions of all sizes can now leverage advanced analytics, artificial intelligence and machine learning with new configurations in the award-winning platform. In a release earlier this week, Experian announced new tools and configurations in the Ascend Analytical SandboxTM to fit teams of every size and skill level. Now fintechs, banks and credit unions of every size can have access to Experian’s one-stop source for advanced analytics, business intelligence and ultimately, better decisions. The secure hybrid-cloud environment allows users to combine their own data sets with Experian’s exclusive data assets, including credit, alternative, commercial, auto and more. From there, users can build and test models across different stages of the lending cycle, including originations, prescreen, account management and collections, and seamlessly put their models into production. Experian’s Ascend Analytical Sandbox also allows users to benchmark their portfolios against the industry, identify credit trends and explore new product opportunities. All the insights gathered through the Ascend Analytical Sandbox can be viewed and shared through interactive dashboards and customizable reports that can be pulled in near real time. Additional use cases include: Reject inferencing – refine models, scorecards and strategies by analyzing trades opened by previous applicants who were rejected or approved but did not move forward Prescreen campaigns – design prescreen campaigns, evaluate results and improve strategies Cross-sell – identify cross-sell opportunities for existing customers and identify how they may be working with other lenders Collections strategies, stress testing and loss forecasting – build stronger models to identify customers that have ability and willingness to pay debts, stress test and forecast loss Peer benchmarking and industry trends – compare current portfolio against peers and the industry Recession planning – identify areas to adjust your portfolio to prepare for an economic downturn OneMain Financial, a large provider of personal installment loans serving 10 million total customers across more than 1,700 branches, turned to Experian to improve its risk modeling and credit portfolio management capabilities with the Ascend Analytical Sandbox. Since using the solution, the company has seen significant improvements in reject inferencing – a process that is traditionally expensive, manually-intensive and time consuming. According to OneMain Financial, the Ascend Analytical Sandbox has shortened the process to less than two weeks from up to 180 days. "Experian's Ascend Technology Platform and Analytical Sandbox is an industry gamechanger," said Michael Kortering, OneMain Financial's Senior Managing Director and Head of Model Development. "We're completing analyses that just weren't possible before and we're getting decisions to our clients faster, without compromising risk.” For more information on Ascend Analytical Sandbox SX – the latest solution for financial institutions of all sizes – or other enterprise-wide capabilities of the Experian Ascend Technology Platform, click here.

“We don’t know what we don’t know.” It’s a truth that seems to be on the minds of just about every financial institution these days. The market, not-to-mention the customer base, seems to be evolving more quickly now than ever before. Mergers, acquisitions and partnerships, along with new competitors entering the space, are a daily headline. Customers expect the same seamless user experience and instant gratification they’ve come to expect from companies like Amazon in just about every interaction they have, including with their financial institutions. Broadly, financial institutions have been slow to respond both in the products they offer their customers and prospects, and in how they present those products. Not surprisingly, only 26% of customers feel like their financial institutions understand and appreciate their needs. So, it’s not hard to see why there might be uncertainty as to how a financial institution should respond or what they should do next. But what if you could know what you don’t know about your customer and industry data? Sound too good to be true? It’s not—it’s exactly what Experian’s Ascend Analytical Sandbox was built to do. “At OneMain we’ve used Sandbox for a lot of exploratory analysis and feature development,” said Ryland Ely, a modeler at Experian partner client, OneMain Financial and a Sandbox user. For example, “we’ve used a loan amount model built on Sandbox data to try and flag applications where we might be comfortable with the assigned risk grade but we’re concerned we might be extending too much or too little credit,” he said. The first product built on Experian’s big data platform, Ascend, the Analytical Sandbox is an analytics environment that can have enterprise-wide impact. It provides users instant access to near real-time customer data, actionable analytics and intelligence tools, along with a network of industry and support experts to drive the most value out of their data and analytics. Developed with scalability, flexibility, efficiency and security at top-of-mind, the Sandbox is a hybrid-cloud system that leverages the high availability and security of Amazon Web Services. This eliminates the need, time and infrastructure costs associated with creating an internally hosted environment. Additionally, our web-based interface speeds access to data and tools in your dedicated Sandbox all behind the protection of Experian’s firewall. In addition to being supported by a revolutionized tech stack backed by an $825 million annual investment, Sandbox enables use of industry-leading business intelligence tools like SAS, RStudio, H2O, Python, Hue and Tableau. Where the Ascend Sandbox really shines is in the amount and quality of the data that’s put into it. As the largest, global information services provider, the Sandbox brings the full power of Experian’s 17+ years of full-file historical tradeline data, boasting a data accuracy rate of 99.9%. The Sandbox also allows users the option to incorporate additional data sets including commercial small business data and soon real estate data, among others. Alternative data assets add to the 50 million consumers who use some sort of financial service, in addition to rental and utility payments. In addition to including Experian’s data on the 220+ million credit-active consumers, small business and other data sets, the Sandbox also allows companies to integrate their own customer data into the system. All data is depersonalized and pinned to allow companies to fully leverage the value of Experian’s patented attributes and scores and models. Ascend Sandbox allows companies to mine the data for business intelligence to define strategy and translate those findings into data visualizations to communicate and win buy-in throughout their organization. But here is where customers are really identifying the value in this big data solution, taking those business intelligence insights and being able to take the resulting models and strategies from the Sandbox directly into a production environment. After all, amassing data is worthless unless you’re able to use it. That’s why 15 of the top financial institutions globally are using the Experian Ascend Sandbox for more than just benchmarking and data visualization but also risk modeling, score migration, share of wallet, market entry, cross-sell and much more. Moreover, clients are seeing time-savings, deeper insights and reduced compliance concerns as a result of consolidating their production data and development platform inside Sandbox. “Sandbox is often presented as a tool for visualization or reporting, sort of creating summary statistics of what’s going on in the market. But as a modeler, my perspective is that it has application beyond just those things,” said Ely. To learn more about the Experian Ascend Analytical Sandbox and hear more about how OneMain Financial is getting value out of the Sandbox, watch this on-demand webinar.

I believe it was George Bernard Shaw that once said something along the lines of, “If economists were laid end-to-end, they’d never come to a conclusion, at least not the same conclusion.” It often feels the same way when it comes to big data analytics around customer behavior. As you look at new tools to put your customer insights to work for your enterprise, you likely have questions coming from across your organization. Models always seem to take forever to develop, how sure are we that the results are still accurate? What data did we use in this analysis; do we need to worry about compliance or security? To answer these questions and in an effort to best utilize customer data, the most forward-thinking financial institutions are turning to analytical environments, or sandboxes, to solve their big data problems. But what functionality is right for your financial institution? In your search for a sandbox solution to solve the business problem of big data, make sure you keep these top four features in mind. Efficiency: Building an internal data archive with effective business intelligence tools is expensive, time-consuming and resource-intensive. That’s why investing in a sandbox makes the most sense when it comes to drawing the value out of your customer data.By providing immediate access to the data environment at all times, the best systems can reduce the time from data input to decision by at least 30%. Another way the right sandbox can help you achieve operational efficiencies is by direct integration with your production environment. Pretty charts and graphs are great and can be very insightful, but the best sandbox goes beyond just business intelligence and should allow you to immediately put models into action. Scalability and Flexibility: In implementing any new software system, scalability and flexibility are key when it comes to integration into your native systems and the system’s capabilities. This is even more imperative when implementing an enterprise-wide tool like an analytical sandbox. Look for systems that offer a hosted, cloud-based environment, like Amazon Web Services, that ensures operational redundancy, as well as browser-based access and system availability.The right sandbox will leverage a scalable software framework for efficient processing. It should also be programming language agnostic, allowing for use of all industry-standard programming languages and analytics tools like SAS, R Studio, H2O, Python, Hue and Tableau. Moreover, you shouldn’t have to pay for software suites that your analytics teams aren’t going to use. Support: Whether you have an entire analytics department at your disposal or a lean, start-up style team, you’re going to want the highest level of support when it comes to onboarding, implementation and operational success. The best sandbox solution for your company will have a robust support model in place to ensure client success. Look for solutions that offer hands-on instruction, flexible online or in-person training and analytical support. Look for solutions and data partners that also offer the consultative help of industry experts when your company needs it. Data, Data and More Data: Any analytical environment is only as good as the data you put into it. It should, of course, include your own client data. However, relying exclusively on your own data can lead to incomplete analysis, missed opportunities and reduced impact. When choosing a sandbox solution, pick a system that will include the most local, regional and national credit data, in addition to alternative data and commercial data assets, on top of your own data.The optimum solutions will have years of full-file, archived tradeline data, along with attributes and models for the most robust results. Be sure your data partner has accounted for opt-outs, excludes data precluded by legal or regulatory restrictions and also anonymizes data files when linking your customer data. Data accuracy is also imperative here. Choose a big data partner who is constantly monitoring and correcting discrepancies in customer files across all bureaus. The best partners will have data accuracy rates at or above 99.9%. Solving the business problem around your big data can be a daunting task. However, investing in analytical environments or sandboxes can offer a solution. Finding the right solution and data partner are critical to your success. As you begin your search for the best sandbox for you, be sure to look for solutions that are the right combination of operational efficiency, flexibility and support all combined with the most robust national data, along with your own customer data. Are you interested in learning how companies are using sandboxes to make it easier, faster and more cost-effective to drive actionable insights from their data? Join us for this upcoming webinar. Register for the Webinar

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?