Tag: cloud-based technology

Experian’s award-winning platform now brings together market-leading data, generative AI and cutting-edge machine learning solutions for analytics, credit decisioning and fraud into a single interface — simplifying the deployment of analytical models and enabling businesses to optimize their practices. The platform updates represent a notable milestone, fueled by Experian’s significant investments in innovation over the last eight years as part of its modern cloud transformation.  “The evolution of our platform reaffirms our commitment to drive innovation and empower businesses to thrive. Its capabilities are unmatched and represent a significant leap forward in lending technology, democratizing access to data in compliant ways while enabling lenders of all sizes to seamlessly validate their customers’ identities with confidence, help expand fair access to credit and offer awesome user and customer experiences,” said Alex Lintner CEO Experian Software Solutions. The enhanced Experian Ascend Platform dramatically reduces time to install and offers streamlined access to many of Experian's award-winning integrated solutions and tools through a single sign-on and a user-friendly dashboard. Leveraging generative AI, the platform makes it easy for organizations of varying sizes and experience levels to pivot between applications, automate processes, modernize operations and drive efficiency. In addition, existing clients can easily add new capabilities through the platform to enhance business outcomes. Read Press Release Learn More Check out Experian Ascend Platform in the media: Transforming Software for Credit, Fraud and Analytics with Experian Ascend Platform™ (Episode 160) Reshaping the Future of Financial Services with Experian Ascend Platform Introducing Experian’s Cloud-based Ascend Technology Platform with GenAI Integration 7 enhancements of Experian Ascend Platform

Financial institutions have long been on the cutting edge of technology trends, and it continues to be true as we look at artificial intelligence and machine learning. Large analytics teams are using models to solve for lending decisions, account management, investments, and more. However, unlike other industries taking advantage of modeling, financial institutions have the added complexity of regulation and transparency requirements to ensure fairness and explainability. That means institutions need highly sophisticated model operations and a highly skilled workforce to ensure that decisions are accurate and accountability is maintained. According to new research from Experian, we see that while financial institutions plan to use or are using models for a wide range of use cases, there is a range of ModelOps maturity across the industry. Just under half of financial institutions are in the early stages of model building, where projects are more ad-hoc in nature and experimental. Only a quarter of institutions seem to be more mature, where processes are well defined and models can be developed in a reliable timeframe. With more than two-thirds of lenders saying that ModelOps will play a key role in shaping the industry over the next five years, the race to maturity is critical. One of the biggest challenges we see in the space is that it takes too long for models to make it into production. On average, financial institutions estimate that the end-to-end process for creating a new model for credit decisioning takes an average of 15 months. Organizations need to accelerate model velocity, meaning the time that it takes to get a model into production and generating value, to take advantage of this powerful technology. Having the right technology, the right talent, and the right data at the right time continue to drag down operational speed and tracking of models after they are in production. For more information on Experian’s recent study, download the new report ‘Accelerating Model Velocity in Financial Institutions’. We are also hosting an upcoming webinar with tips on how to tackle some of the biggest model development and deployment challenges. You can register for the webinar here.

Digitalization, 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, from https://www.idc.com/getdoc.jsp?containerId=US45599219

Financial services companies have long struggled to make inclusive decisions for small businesses and for low- and moderate-income consumers. One key reason: to make accurate predictions of the financial risks associated with those customers’ accounts requires lenders to rely on a wider variety of data than a credit score alone. To accurately assess risk, expanded Fair Credit Reporting Act regulated data is helpful – including rental data, trended data, enhanced public records, alternative financial services data and more. This expanded FCRA data is one key to financial inclusion. Without that data, lenders risk rejecting potentially profitable customers, including so-called credit invisibles and thin file consumers. In fact, The Federal Reserve, along with four important financial services regulators, highlighted the consumer benefits of alternative data in their December 2019 interagency statement. That statement also highlighted the increased importance of managing compliance when firms use alternative data in credit underwriting. With hundreds of data sources available to help with important tasks such as verifying identity, checking credit, and assessing the value of automotive and real-estate collateral, why have some lenders been slow to use the most appropriate data attributes when making credit decisions? One reason is a matter of IT Architecture; another is priorities. Changing a business process to take advantage of new data requirements can be prohibitively lengthy and costly – ­in terms of both analytical and IT resources. This is especially true for older systems—which were seldom adapted to use Application Programming Interfaces (APIs) supporting modern data structures such as JSON. Furthermore, data access to older systems can require specific types of system connectivity such as VPNs or leased lines. Latency is important in this type of application: some of these tasks have to be done instantly in a digital-first or digital-only lending environment. So is time to market: lenders deploying analytics processes cannot wait for overtaxed IT teams to complete lengthy projects. Lenders’ analytics and IT teams have long known they need to be more agile and efficient, faster to market, and increasingly secure. Their answer, largely, has been a slow but steady migration of their systems to the cloud. A 2019 McKinsey survey revealed that CIOs were modernizing their infrastructures primarily to achieve four goals: agility and time to market, quality and reliability, cost, and security. There are other benefits as well. But if the business case for a cloud strategy was somewhat clear to IT and analytics leaders, it became crystal clear to the rest of the business in 2020. As companies shifted to at-home work using cloud-based collaboration tools, especially videoconferencing services, most companies conquered what was perhaps the final barrier to entry—the fear that the issues of data privacy and security were somehow more insurmountable with virtual machines, containers, and microservices than with on-premise infrastructure. Last quarter, the leading cloud providers ­Amazon Web Services, Google Cloud Platform, and Microsoft Azure ­reported incredible annual revenue growth: 29%, 45%, and 48% respectively. COVID-19 has proven to be the catalyst that greatly sped up the transition to cloud technologies. The jump to the cloud means that lenders are suddenly more capable than ever at making analytically sound – and therefore more financially inclusive ­decisions. The key to analytical decision-making is to use the right data and to make the most appropriate calculations (called attributes) as part of a business strategy or a mathematical model. With Experian programs such as Attribute Toolbox now available in the cloud, calculating those all-important attributes is as simple for the IT department as coding an API call. Lenders will soon be able just as easily to retrieve and process raw data from over 100 data sources, to recognize their native formats and to extract the desired information quickly enough for real-time and batch decisioning. The pandemic has brought economic distress to millions of Americans—it is unlike anything in our lifetimes. The growth of cloud computing promises to enable these consumers to obtain additional products as well as more favorable pricing and terms. It’s ironic that COVID has accelerated the adoption of the very technologies that will expand access to credit for many people who cannot currently access it from mainstream financial firms. To learn more about our Attribute Toolbox, click here. Learn More

The global pandemic has created major shifts in the ways companies operate and innovate. For many organizations, a heavy reliance on cloud applications and cloud services has become the new normal, with cloud applications being praised as “an unsung hero” for accommodating a world in crisis, as stated in an article from the Channel Company. However, cloud computing isn’t just for consumers and employees working from home. In the last few years, cloud computing has changed the way organizations and businesses operate. Cloud-based solutions offer the flexibility, reduced operational costs and fast deployment that can transform the ways traditional companies operate. In fact, migrating services and software to the cloud has become one of the next steps to a successful digital transformation. What is cloud computing? Simply put – it’s the ability to run applications or software from remote servers, hosted by external providers, also known as infrastructure-as-a-service (IaaS). Data collected from cloud computing is stored online and is accessed via the Internet. According to a study by CommVault, more than 93% of business leaders say that they are moving at least some of their processes to the cloud, and a majority are already cloud-only or plan to completely migrate. In a recent Forrester blog titled ‘Troubled Times Test Traditional Tech Titans,’ Glenn O’Donnell, Vice President, Research Director at Forrester highlights that “as we saw in prior economic crises, the developments that carried business through the crisis remained in place. As many companies shift their infrastructure to cloud services through this pandemic, those migrated systems will almost certainly remain in the cloud.” In short, cloud computing is the new wave – now more than ever during a crisis. But what are the benefits of moving to the cloud? Flexibility Cloud computing offers the flexibility that companies need to adjust to fluctuating business environments. During periods of unexpected growth or slow growth, companies can expand to add or remove storage space, applications, or features and scale as needed. Businesses will only have to pay for the resources that they need. In a pandemic, having this flexibility and easy access is the key to adjusting to volatile market conditions. Reduced operational costs Companies (big or small) that want to reduce costs from running a data center will find that moving to the cloud is extremely cost-effective. Cloud computing eliminates the high cost of hardware, IT resources and maintaining internal and on-premise data systems. Cloud-based solutions can also help organizations modernize their IT infrastructures and automate their processes. By migrating to the cloud, companies will be able to save substantial capital costs and see a higher return on investment – while maintaining efficiency. Faster deployment With the cloud, companies get the ability to deploy and launch programs and applications quickly and seamlessly. Programs can be deployed in days as opposed to weeks – so that businesses can operate faster and more efficiently than ever. During a pandemic, faster deployment speeds can help organizations accommodate, make updates to software and pivot quickly to changing market conditions. Flexible, scalable, and cost-effective solutions will be the keys to thriving during and after a pandemic. That’s why we’ve enhanced a variety of our solutions to be cloud-based – to help your organization adapt to today’s changing customer needs. Solutions like our Attribute Toolbox are now officially on the cloud, to help your organizations make better, faster, and more effective decisions. Learn More

This week, Experian released a new version of our CrossCore® digital identity and fraud risk platform, adding new tools and functionality to help businesses quickly respond to today’s emerging fraud threats. The ability to confidently recognize your customers and safeguard their digital transactions is becoming an increasing challenge for businesses. Fraud threats are already rising across the globe as fraudsters take advantage of the global health crisis and rapidly shifting economic conditions. CrossCore combines risk-based authentication, identity proofing and fraud detection into a single cloud platform, which means businesses can more quickly respond to an ever-changing environment. And with flexible decisioning orchestration and advanced analytics, businesses can make real-time risk decisions throughout the customer lifecycle. “Now more than ever, businesses need to lean on capabilities and technology that will allow them to rapidly respond in these challenging times, increase identity confidence in every transaction, and provide a safe and convenient experience for customers,” said E.K. Koh, Experian’s Senior Vice President of Global Identity & Fraud Solutions in a recent press release. “This new CrossCore release enables businesses to easily leverage best-in-class, pre-integrated identity and fraud services through simple self-service.” This new version of CrossCore features a cloud architecture, modern user interface, progressive risk assessments, faster response times, self-service workflow configuration, and a transactional volume reporting dashboard. These enhancements give you a simpler way to manage how backing applications are utilized, allow you to analyze key performance indicators in near real-time, and empower you to catch more fraud faster - without impacting the customer experience. “Recent Aite Group research shows that many banks have seen digital channel usage increase 250% in the wake of the pandemic, so ensuring a seamless and safe customer experience is more important than ever,” said Julie Conroy, Research Director at Aite Group. “Platforms such as CrossCore that can enable businesses to nimbly respond to changing patterns of customer behavior as well as rapidly evolving attack tactics are more important than ever, as financial services firms work to balance fraud mitigation with the customer experience.” CrossCore is the first identity and fraud platform that enables you to connect, access, and orchestrate decisions across multiple solutions. With the newest version, Experian enhances your ability to consolidate numerous fraud risk signals into a single, holistic assessment to improve operational processes, stay ahead of fraudsters, and protect your customers. Read Press Release Learn More About CrossCore

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

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.

Perhaps more than ever before, technology is changing how companies operate, produce and deliver products and services to their customers. Similarly, technology is also driving a shift in customer expectation in how, when and where they consume products and services. But these changes aren’t just relegated to the arenas where tech giants with household names, like Amazon and Google, play. Likewise, financial institutions of every size are also fielding the changes brought on by innovations to the industry in recent years. According to this report by PWC, 77% of firms plan on dedicating time and budgets to increase innovation. But what areas make the most sense for your business? With a seemingly constant shift in consumer and corporate focus, it can be difficult to know which technological advancements are imperative to your company’s success and which are just the latest fizzling buzzword. As you evaluate innovation investments for your organization in 2019 and beyond, here’s a list of four technology innovations that are already changing the financial sector or will change the banking landscape in the near future. The APIs of Open Banking Ok, it’s not a singular innovation, so I’m cheating a bit here, but it’s a great place to begin the conversation because it comprises and sets the stage for many of the innovations and technologies that are in use today or will be implemented in the future. Created in 2015, the Open Banking Standard defined how a bank’s system data or consumer-permissioned financial data should be created, accessed and shared through the use of application programming interfaces or APIs. When financial institutions open their systems up to third-party developer partners, they can respond to the global trends driving change within the industry while greatly improving the customer experience. With the ability to securely share their financial data with other lenders, greater transparency into the banking process, and more opportunities to compare product offerings, consumers get the frictionless experience they’ve come to expect in just about every aspect of life – just not necessarily one that lenders are known for. But the benefits of open banking are not solely consumer-centric. Financial institutions are able to digitize their product offerings and thus expand their market and more easily share data with partners, all while meeting clients’ individualized needs in the most cost-effective way. Biometrically speaking…and smiling Verifying the identity of a customer is perhaps one of the most fundamental elements to a financial transaction. This ‘Know Your Customer’ (KYC) process is integral to preventing fraud, identity theft, money laundering, etc., but it’s also time-consuming and inconvenient to customers. Technology is changing that. From thumbprint and, now, facial recognition through Apple Pay, consumers have been using biometrics to engage with and authorize financial transactions for some time now. As such, the use of biometrics to authenticate identity and remove friction from the financial process is becoming more mainstream, moving from smartphones to more direct interaction. Chase has now implemented voice biometrics to verify a consumer’s identity in customer service situations, allowing the company to more quickly meet a customer’s needs. Meanwhile, in the US and Europe, Visa is testing biometric credit cards that have a fingerprint reader embedded in the card that stores his or her fingerprint in order to authenticate their identity during a financial transaction. In China, companies like Alipay are taking this to the next level by allowing customers to bypass the phone entirely with its ‘pay with a smile’ service. First launched in KFC restaurants in China, the service  is now being offered at hospitals as well. How, when and where a consumer accesses their financial institution data actually creates a digital fingerprint that can be verified. While facial and vocal matching are key components to identity verification and protecting the consumer, behavioral biometrics have also become an important part of the fraud prevention arsenal for many financial institutions. These are key components of Experian’s CrossCore solution, the first open fraud and identity platform partners with a variety of companies, through open APIs discussed above. Not so New Kid on the Block(chain) The first Bitcoin transaction took place on January 12, 2009. And for a number of years, all was quiet. Then in 2017, Bitcoin started to blow up, creating a scene reminiscent of the 1850s California gold rush. Growing at a seemingly exponential rate, the cryptocurrency topped out at a per unit price of more than $20,000. By design cryptocurrencies are decentralized, meaning they are not controlled or regulated by a single entity, reducing the need for central third-party institutions, i.e. banks and other financial institutions to function as central authorities of trust. Volatility and regulation aside, it’s understandable why financial institutions were uneasy, if not skeptical of the innovation. But perhaps the most unique characteristic of cryptocurrencies is the technology on which they are built: blockchain. Essentially, a blockchain is just a special kind of database. The database stores, validates, transfers and keeps a ledger of transfers of encrypted data—records of financial transfers in the case of Bitcoin. But these records aren’t stored on one computer as is the case with traditional databases. Blockchain leverages a distributed ledger or distributed trust approach where a full copy of the database is stored across many distributed processing nodes and the system is constantly checking and validating the contents of the database. But a blockchain can store any type of data, making it useful in a wide variety of applications including tracking the ownership digital or physical assets or the provenance of documents, etc. From clearing and settlements, payments, trade finance, identity and fraud prevention, we’re already seeing financial institutions explore and/or utilize the technology. Santander was the first UK bank to utilize blockchain for their international payments app One Pay FX. Similarly, other banks and industry groups are forming consortiums to test the technology for other various uses. With all this activity, it’s clear that blockchain will become an integral part of financial institutions technology and operations on some level in the coming years. Robot Uprising Rise in Robots While Artificial Intelligence seems to have only recently crept into pop-culture and business vernacular, it was actually coined in 1956 by John McCarthy, a researcher at Dartmouth who thought that any aspect of learning or intelligence could essentially be taught to a machine. AI allows machines to learn from experience, adjust to new inputs and carry out human-like tasks. It’s the result of becoming ‘human-like’ or the potential to become superior to humans that creeps out people like my father, and also worries others like Elon Musk. Doomsday scenarios a la Terminator aside, it’s easy to see how the tech can and is useful to society. In fact, much of the AI development done today uses human-style reasoning as a model, but not necessarily the ultimate aim, to deliver better products and services. It’s this subset of AI, machine learning, that allows companies like Amazon to provide everything from services like automatic encryption in AWS to products like Amazon Echo. While it’s much more complex, a simple way to think about AI is that it functions like billions of conditional if-then-else statements working in a random, varied environment typically towards a set goal. Whereas in the past, programmers would have to code these statements and input reference data themselves, machine learning systems learn, modify and map between inputs and outputs to create new actions based on their learning. It works by combining the large amounts of data created on a daily basis with fast, iterative processing and intelligent algorithms, allowing the program to learn from patterns in the data and make decisions. It’s this type of machine learning that banks are already using to automate routine, rule-based tasks like fraud monitoring and also drive the analytical environments used in their risk modeling and other predictive analytics. Whether or not you’ve implemented AI, machine learning or bot technology into your operations, it’s highly likely your customers are already leveraging AI in their home lives, with smart home devices like Amazon Echo and Google Home. Conversational AI is the next juncture in how people interface with each other, companies and life in general. We’re already seeing previews of what’s possible with technologies like Google Duplex. This has huge implication for the financial services industry, from removing friction at a transaction level to creating a stickier, more engaging customer experience. To that end, according to this report from Accenture, AI may begin to provide in-the-moment, holistic financial advice that is in a customer’s best interest.   It goes without saying that the market will continue to evolve, competition will only grow more fierce, consumer expectation will continue to shift, and regulation will likely become more complex. It’s clear technology can be a mitigating factor, even a competitive differentiator, with these changing industry variables. Financial institutions must evolve corporate mindsets in their approach to prioritize innovations that will have the greatest enterprise-wide impact. By putting together an intelligent mix of people, process, and the right technology, financial institutions can better predict consumer need and expectation while modernizing their business models.

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

Student loan debt is weighing down Americans of all generations, but a college education is still prized as the ticket to opportunity. So will the debt continue to climb? Where will students turn for funding? We interviewed Vince Passione, founder and CEO of LendKey, a lending-as-a-service platform specializing in student lending, to gain his perspective on the state of student lending and how the space is evolving for both consumers and lenders. We’ve all seen the headlines about U.S. student loan debt now accounting for $1.4 trillion. The majority of these loans are government-funded, but do you see this shifting? There are many factors at play here. Tuition is rising rapidly and will soon outpace the current level of governmental support available to students searching for loans. Meanwhile, today’s geopolitical climate signals that the current levels of federal funding will also decrease. With these two confounding trends, the need for competitively priced private financing and refinancing options will increase. The student loan industry will shift toward private lenders such as credit unions and banks in order for students to continue to obtain the funds they need for tuition and other college expenses. The key to helping this transition happen is for banks and credit unions to adopt the user-friendly technology platforms that appeal to these prospective student borrowers. Your end-to-end cloud-based technology platform enables lenders to get into the student loan space. How does this work and what must lenders consider as they underwrite and manage a student loan portfolio? Our turnkey platform is unique, in that it lets lenders control underwriting and pricing, unlike the “disruptive” model utilized by many other technology companies in the industry. Most community banks and credit unions lack the in-house resources to develop, implement and maintain an online lending platform. At the same time, millennials and young borrowers continue to prefer the online interface rather than engaging with a brick-and-mortar establishment. We’re committed to partnering with banks and credit unions to allow them to offer private consumer loans, such as student loans, and support them with our technology (loan application and decisioning) and people (customer service agents and loan processors). A strong grasp on the technology and support aspects of online lending platforms alone is merely the foundation for a successful program. As the student lending asset classes are highly regulated, and the regulations are constantly changing, lenders must look to partner with a firm that has a concrete understanding of the regulation, risk and customer service to translate the information to prospective borrowers. I’ve heard you use the phrase “HENRY.” Can you explain what this is and why these individuals are so lucrative for lenders? HENRY stands for “High Earners, Not Rich Yet” and is a term that can be applied to many millennials and young people in today’s economy. This demographic is typically college graduates with well-paying jobs, but have not yet established themselves financially or accrued enough wealth to subsidize larger purchases like cars, homes, renovations and advanced degrees. This is also why they are so lucrative for lenders. HENRYs have just entered their prime borrowing years and are consumers who will easily be able to pay back loans for cars, homes and renovations. But for most of this demographic, their first experience with a financial service product will be a student loan. It is important to get in on the ground floor with these borrowers through student lending to establish a trusted relationship that will result in repeat loans and referrals. You’ve done a great deal of research on millennials and how they are managing student loans. Can you share some of your key learnings? Do you believe Generation Z will behave and manage student debt similarly? It’s no secret that millennials are more apprehensive of student loans than previous generations. As Gen Z begins to enter college, many are plagued with stigmas set forward by the poor experiences millennials experienced with student loans, making them wary of debt. According to a study, 63 percent of the students said they would “possibly” take on student debt, down from 71 percent in 2016. Gen Z is better prepared by seeing the preceding generation grapple with loan issues. Many are making smarter decisions on schools and programs, and are attentive when it comes to monitoring for updates in regulation. As this generation continues to go through the typical collegiate years, the geopolitical climate, as well as rising tuition costs, will increase the need for competitively priced private financing options for Gen Zers. Finally, what trends or predictions do you see occurring in the student lending space over the next five years? The need for student loans continues to exist and shows no sign of slowing down anytime soon, but lenders are only recently opening their eyes to the opportunity that this massive market presents. With the impending drop in federal funding, more FinTech companies will continue to pop up to address this need. This spike in disruption also poses a threat to banks and credit unions, however. With more FinTechs available to help shoulder the burden of student lending, banking and credit union executives must be more judicious when vetting technology partners to ensure they’re working with a partner that meets their regulatory standards, supports their current and prospective clients, and lets them retain the control they wish to keep in-house.