Tag: technology

It may be a new decade of disruption, but one thing remains constant – the consumer is king. As such, customer experience (and continually evolving digital transformations necessary to keep up), digital expansion and all things identity will also reign supreme as we enter this new set of Roaring 20s. Here are seven of the top trends to keep tabs of through 2020 and beyond. 1. Data that does more – 100 million borrowers and counting Traditional, alternative, public record, consumer-permissioned, small business, big business, big, bigger, best – data has a lot of adjectives preceding it. But no matter how we define, categorize and collate data, the truth is there’s a lot of it that’s untapped, which is keeping financial institutions from operating at their max efficiency levels. Looking for ways to be bigger and bolder? Start with data to engage your credit-worthy consumer universe and beyond. Across the entire lending lifecycle, data offers endless opportunities – from prospecting and acquisitions to fraud and risk management. It fuels any technology solution you have or may want to implement over the coming year. Additionally, Experian is doing their part to create a more holistic picture of consumer creditworthiness with the launch of Experian LiftTM in November. The new suite of credit score products combines exclusive traditional credit, alternative credit and trended data assets, intended to help credit invisible and thin-file consumers gain access to fair and affordable credit. "We're committed to improving financial access while helping lenders make more informed decisions. Experian Lift is our latest example of this commitment brought to life,” said Greg Wright, Executive Vice President and Chief Product Officer for Experian Consumer Information Services. “Through Experian Boost, we're empowering consumers to play an active role in building their credit histories. And, with Experian Lift, we're empowering lenders to identify consumers who may otherwise be excluded from the traditional credit ecosystem,” he said. 2. Identity boom for the next generation Increasingly digital lifestyles have put personalization and frictionless transactions on hyperdrive. They are the expectation, not a nice-to-have. Having customer intelligence will become a necessary survival strategy for those in the market wanting to compete. Identity is not just for marketing purposes; it must be leveraged across the lending lifecycle and every customer interaction. Fragmented customer identities are more than flawed for decisioning purposes, which could potentially lead to losses. And, of course, the conversation around identity would be incomplete without a nod to privacy and security considerations. With the roll-out of the California Consumer Privacy Act (CCPA) earlier this month, we will wait to see if the other states follow suit. Regardless, consumers will continue to demand security and trust. 3. All about artificial intelligence and machine learning We get it – we all want the fastest, smartest, most efficient processes on limited – and/or shrinking – budgets. But implementing advanced analytics for your financial institution doesn’t have to break the bank. And, when it comes to delivering services and messaging to customers the way they want it, how to do that means digital transformation – specifically, leveraging big data and actionable analytics to evaluate risk, uncover industry intel and improve decisioning. One thing’s for certain, financial institutions looking to compete, gain traction and pull away from the competition in this next decade will need to do so by leveraging a future-facing partner’s expertise, platforms and data. AI and machine learning model development will go into hyperdrive to add accuracy, efficiency, and all-out speed. Real-time transactional processing is where it’s at. 4. Customer experience drives decisioning and everything Faster, better, more frictionless. 2020 and the decade will be all about making better decisions faster, catering to the continually quickening pace of consumer attention and need. Platforms and computing language aside, how do you increase processing speed at the same time as increasing risk mitigation? Implementing decisioning environments that cater to consumer preferences, coupled with best-in-class data are the first two steps to making this happen. This can facilitate instant decisioning within financial institutions. Looking beyond digital transformation, the next frontier is digital expansion. Open platforms enable financial institutions to readily add solutions from numerous providers so that they can connect, access and orchestrate decisions across multiple systems. Flexible APIs, single integrations and better strategy and design build the foundation of the framework to be implemented to enhance and elevate customer experience as it’s known today. 5. Credit marketing that keeps up with the digital, instant-gratification age Know your customer may be a common acronym for the financial services industry, but it should also be a baseline for determining whether to send a specific message to clients and prospects. From the basics, like prescreen, to omni-channel marketing campaigns, financial institutions need to leverage the communication channels that consumers prefer. From point of sale to mobile – there are endless possibilities to fit into your consumers’ credit journey. Marketing is clearly not a one-and-done tactic, and therefore multi-channel prequalification offers and other strategies will light the path for acquisitions and cross-sell/up-sell opportunities to come. By developing insights from customer data, financial institutions have a clear line of sight into determining optimal strategies for customer acquisition and increasing customer lifetime value. And, at the pinnacle, the modern customer acquisition engine will continue to help financial institutions best build, test and optimize their customer channel targeting strategies faster than ever before. From segmentation to deployment, and the right data across it all, today and tomorrow’s technology can solve many of financial organizations’ age-old customer acquisition challenges. 6. Three Rs: Recession, regulatory and residents of the White House Last March, the yield curve inverted for the first time since 2007. Though the timing of the next economic correction is debated, messaging is consistent around making a plan of action now. Whether it’s arming your collections department, building new systems, updating existing systems, or adjusting rules and strategy, there are gaps every organization needs to fill. By leveraging the stability of the economy now, financial institutions can put strategies in place to maximize profitability, manage risk, reduce bad debt/charge-offs, and ensure regulatory compliance among their list of to-do’s, ultimately resulting in a more efficient, better-performing program. Also, as we near the election later this year, the regulatory landscape will likely change more than the usual amount. Additionally, we will witness the first accounts of what CECL looks like for SEC-filing financial institutions (and if that will suggest anything for how non-SEC-filing institutions may fare as their deadline inches closer), as well as see the initial implications of the CCPA roll out and whether it will pave a path for other states to follow. As system sophistication continues to evolve, so do the risks (like security breaches) and new regulatory standards (like GDPR and CCPA) which provide reasons for organizations to transform. 7. Focus on fraud (in all forms) With evolving technology, comes evolved fraudsters. Whether it’s loyalty and rewards programs, account openings, breaches, there are so many angles and entry points. Synthetic identity fraud is the fastest-growing type of financial crime in the United States. The cost to businesses is estimated to grow to $1.2 billion by 2020, according to the Aite Group. To ensure the best protection for your business and your customers, a layered, risk-based approach to fraud management provides the highest levels of confidence in the industry. Balance is key – while being compliant with regulatory requirements and conscious of user experience, ensuring consumers’ peace of mind is priority one. Not a new trend, but recognizing fraud and recognizing good consumers will save continue to save financial institutions money and reputational harm, driving significant improvement in key performance indicators. Using the right data (and aggregating multiple data sets) and digital device intelligence tools is the one-two punch to protect your bottom line. For all your needs in 2020 and throughout the next decade, Experian has you covered. Learn more

It seems like artificial intelligence (AI) has been scaring the general public for years – think Terminator and SkyNet. It’s been a topic that’s all the more confounding and downright worrisome to financial institutions. But for the 30% of financial institutions that have successfully deployed AI into their operations, according to Deloitte, the results have been anything but intimidating. Not only are they seeing improved performance but also a more enhanced, positive customer experience and ultimately strong financial returns. For the 70% of financial institutions who haven’t started, are just beginning their journey or are in the middle of implementing AI into their operations, the task can be daunting. AI, machine learning, deep learning, neural networks—what do they all mean? How do they apply to you and how can they be useful to your business? It’s important to demystify the technology and explain how it can present opportunities to the financial industry as a whole. While AI seems to have only crept into mainstream culture and business vernacular in the last decade, it was first coined by John McCarthy in 1956. A researcher at Dartmouth, McCarthy thought that any aspect of learning or intelligence could be taught to a machine. Broadly, AI can be defined as a machine’s ability to perform cognitive functions we associate with humans, i.e. interacting with an environment, perceiving, learning and solving problems. Machine learning vs. AI Machine learning is not the same thing as AI. Machine learning is the application of systems or algorithms to AI to complete various tasks or solve problems. Machine learning algorithms can process data inputs and new experiences to detect patterns and learn how to make the best predictions and recommendations based on that learning, without explicit programming or directives. Moreover, the algorithms can take that learning and adapt and evolve responses and recommendations based on new inputs to improve performance over time. These algorithms provide organizations with a more efficient path to leveraging advanced analytics. Descriptive, predictive, and prescriptive analytics vary in complexity, sophistication, and their resulting capability. In simplistic terms, descriptive algorithms describe what happened, predictive algorithms anticipate what will happen, and prescriptive algorithms can provide recommendations on what to do based on set goals. The last two are the focus of machine learning initiatives used today. Machine learning components - supervised, unsupervised and reinforcement learning Machine learning can be broken down further into three main categories, in order of complexity: supervised, unsupervised and reinforcement learning. As the name might suggest, supervised learning involves human interaction, where data is loaded and defined and the relationship to inputs and outputs is defined. The algorithm is trained to find the relationship of the input data to the output variable. Once it delivers accurately, training is complete, and the algorithm is then applied to new data. In financial services, supervised learning algorithms have a litany of uses, from predicting likelihood of loan repayment to detecting customer churn. With unsupervised learning, there is no human engagement or defined output variable. The algorithm takes the input data and structures it by grouping it based on similar characteristics or behaviors, without a defined output variable. Unsupervised learning models (like K-means and hierarchical clustering) can be used to better segment or group customers by common characteristics, i.e. age, annual income or card loyalty program. Reinforcement learning allows the algorithm more autonomy in the environment. The algorithm learns to perform a task, i.e. optimizing a credit portfolio strategy, by trying to maximize available rewards. It makes decisions and receives a reward if those actions bring the machine closer to achieving the total available rewards, i.e. the highest acquisition rate in a customer category. Over time, the algorithm optimizes itself by correcting actions for the best outcomes. Even more sophisticated, deep learning is a category of machine learning that involves much more complex architecture where software-based calculators (called neurons) are layered together in a network, called a neural network. This framework allows for much broader, complex data ingestion where each layer of the neural network can learn progressively more complex elements of the data. Object classification is a classic example, where the machine ‘learns’ what a duck looks like and then is able to automatically identify and group images of ducks. As you might imagine, deep learning models have proved to be much more efficient and accurate at facial and voice recognition than traditional machine learning methods. Whether your financial institution is already seeing the returns for its AI transformation or is one of the 61% of companies investing in this data initiative in 2019, having a clear picture of what is available and how it can impact your business is imperative. How do you see AI and machine learning impacting your customer acquisition, underwriting and overall customer experience?

Over the years, businesses have gathered a plethora of datasets on their customers. However, there is no value in data alone. The true value comes from the insights gained and actions that can be derived from these datasets. Advanced analytics is the key to understanding the data and extracting the critical information needed to unlock these insights. AI and machine learning in particular, are two emerging technologies with advanced analytics capabilities that can help companies achieve their business goals. According to an IBM survey, 61% of company executives indicated that machine learning and AI are their company’s most significant data initiatives in 2019. These leaders recognize that advanced analytics is transforming the way companies traditionally operate. It is no longer just a want, but a must. With a proper strategy, advanced analytics can be a competitive differentiator for your financial institution. Here are some ways that advanced analytics can empower your organization: Provide Personalized Customer Experiences Business leaders know that their customers want personalized, frictionless and enhanced experiences. That’s why improving the customer experience is the number one priority for 80 percent of executives globally, according to an Experian study. The data is already there – companies have insights into what products their customers like, the channels they use to communicate, and other preferences. By utilizing the capabilities of advanced analytics, companies can extract more value from this data and gain better insights to help create more meaningful, personalized and profitable lending decisions. Reduce Costs Advanced analytics allows companies to deploy new models and strategies more efficiently – reducing expenses associated with managing models for multiple lending products and bureaus. For example, OneMain Financial, was able to successfully drive down risk modeling expenses after implementing a solution with advanced analytics capabilities. Improve Accuracy and Speed to Market To stay ahead of the competition, companies need to maintain fast-moving environments. The speed, accuracy and power of a company’s predictive models and forecasts are crucial for success. Being able to respond to changing market conditions with insights derived from advanced analytics is a key differentiator for future-forward companies. Advanced analytic capabilities empower companies to anticipate new trends and drive rapid development and deployment, creating an agile environment of continual improvement. Drive Growth and Expand Your Customer Base With the rise of AI, machine learning and big data, the opportunities to expand the credit universe is greater than ever. Advanced analytic capabilities allow companies to scale datasets and get a bird’s eye view into a consumer’s true financial position – regardless of whether they have a credit history. The insights derived from advanced analytics opens doors for thin file or credit invisible customers to be seen – effectively allowing lenders to expand their customer base. Meet Compliance Requirements Staying on top of model risk and governance should always remain top of mind for any institution. Analytical processing aggregates and pulls new information from a wide range of data sources, allowing your institution to make more accurate and faster decisions. This enables lenders to lend more fairly, manage models that stand up to regulatory scrutiny, and keep up with changes in reporting practices and regulations. Better, faster and smarter decisions. It all starts with advanced analytics. Businesses must take advantage of the opportunities that come with implementing advanced analytics, or risk losing their customers to more future-forward organizations. At Experian, we believe that using big data can help power opportunities for your company. Learn how we can help you leverage your data faster and more effectively. Learn More

To provide consumers with clear-cut protections against disturbance by debt collectors, the Consumer Financial Protection Bureau (CFPB) issued a Notice of Proposed Rulemaking (NPRM) to implement the Fair Debt Collection Practices Act (FDCPA) earlier this year. Among many other things, the proposal would set strict limits on the number of calls debt collectors may place to reach consumers weekly and clarify requirements for consumer-facing debt collection disclosures. A bigger discussion Deliberation of the debt collection proposal was originally scheduled to begin on August 18, 2019. However, to allow commenters to further consider the issues raised in the NPRM and gather data, the comment period was extended by 20 days to September 18, 2019. It is currently still being debated, as many argue that the proposed rule does not account for modern consumer preferences and hinders the free flow of information used to help consumers access credit and services. The Association of Credit and Collection Professionals (ACA International) and US House lawmakers continue to challenge the proposal, stating that it doesn’t ensure that debt collectors’ calls to consumers are warranted, nor does it do enough to protect consumers’ privacy. Many consumer advocates have expressed doubts about how effective the proposed measures will be in protecting debtors from debt collector harassment and see the seven-calls-a-week limit on phone contact as being too high. In fact, it’s difficult to find a group of people in full support of the proposal, despite the CFPB stating that it will help clarify the FDCPA, protect lenders from litigation and bring consumer protection regulation into the 21st century. What does this mean? Although we don’t know when, or if, the proposed rule will go into effect, it’s important to prepare. According to the Federal Register, there are key ways that the new regulation would affect debt collection through the use of newer technologies, required disclosures and limited consumer contact. Not only will the proposed rules apply to debt collectors, but its provisions will also impact creditors and servicers, making it imperative for everyone in the financial services space to keep watch on the regulation’s status and carefully analyze its proposed rules. At Experian, our debt collection solutions automate and moderate dialogues and negotiations between consumers and collectors, making it easier for collection agencies to connect with consumers while staying compliant. Our best-in-class data and analytics will play a key role in helping you reach the right consumer, in the right place, at the right time. Learn more

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

Today is National Fintech Day – a day that recognizes the ever-important role that fintech companies play in revolutionizing the customer experience and altering the financial services landscape. Fintech. The word itself has become synonymous with constant innovation, agile technology structures and being on the cusp of the future of finance. Fintech challengers are disrupting existing financial models by leveraging data, advanced analytics and technology – both inspiring traditional financial institutions in their digital transformation strategies and giving consumers access to a variety of innovative financial products and services. But to us at Experian, National Fintech Day means more than just financial disruption. National Fintech Day represents the partnerships we have carefully fostered with our fintech clients to drive financial inclusion for millions of people around the globe and provide consumers with greater control and more opportunities to access the quality credit they deserve. “We are actively seeking out unresolved problems and creating products and technologies that will help transform the way businesses operate and consumers thrive in our society. But we know we can’t do it alone,” said Experian North American CEO, Craig Boundy in a recent blog article on Experian’s fintech partnerships. “That’s why over the last year, we have built out an entire team of account executives and other support staff that are fully dedicated to developing and supporting partnerships with leading fintech companies. We’ve made significant strides that will help us pave the way for the next generation of lending while improving the financial health of people around the world.” At Experian, we understand the challenges fintechs face – and our real-world solutions help fintech clients stay ahead of constantly changing market conditions and demands. “Experian’s pace of innovation is very impressive – we are helping both lenders and consumers by delivering technological solutions that make the lending ecosystem more efficient,” said Experian Senior Account Executive Warren Linde. “Financial technology is arguably the most important type of tech out there, it is an honor to be a part of Experian’s fintech team and help to create a better tomorrow.” If you’d like to learn more about Experian’s fintech solutions, visit us at Experian.com/Fintech.

Financial institutions preparing for the launch of the Financial Accounting Standard Board’s (FASB) new current expected credit loss model, or CECL, may have concerns when it comes to preparedness, implications and overall impact. Gavin Harding, Experian’s Senior Business Consultant and Jose Tagunicar, Director of Product Management, tackled some of the tough questions posed by the new accounting standard. Check out what they had to say: Q: How can financial institutions begin the CECL transition process? JT: To prepare for the CECL transition process, companies should conduct an operational readiness review, which includes: Analyzing your data for existing gaps. Determining important milestones and preparing for implementation with a detailed roadmap. Running different loss methods to compare results. Once losses are calculated, you’ll want to select the best methodology based on your portfolio. Q: What is required to comply with CECL? GH: Complying with CECL may require financial institutions to gather, store and calculate more data than before. To satisfy CECL requirements, financial institutions will need to focus on end-to-end management, determine estimation approaches that will produce reasonable and supportable forecasts and automate their technology and platforms. Additionally, well-documented CECL estimations will require integrated workflows and incremental governance. Q: What should organizations look for in a partner that assists in measuring expected credit losses under CECL? GH: It’s expected that many financial institutions will use third-party vendors to help them implement CECL. Third-party solutions can help institutions prepare for the organization and operation implications by developing an effective data strategy plan and quantifying the impact of various forecasted conditions. The right third-party partner will deliver an integrated framework that empowers clients to optimize their data, enhance their modeling expertise and ensure policies and procedures supporting model governance are regulatory compliant. Q: What is CECL’s impact on financial institutions? How does the impact for credit unions/smaller lenders differ (if at all)? GH: CECL will have a significant effect on financial institutions’ accounting, modeling and forecasting. It also heavily impacts their allowance for credit losses and financial statements. Financial institutions must educate their investors and shareholders about how CECL-driven disclosure and reporting changes could potentially alter their bottom line. CECL’s requirements entail data that most credit unions and smaller lenders haven’t been actively storing and saving, leaving them with historical data that may not have been recorded or will be inaccessible when it’s needed for a CECL calculation. Q: How can Experian help with CECL compliance? JT: At Experian, we have one simple goal in mind when it comes to CECL compliance: how can we make it easier for our clients? Our Ascend CECL ForecasterTM, in partnership with Oliver Wyman, allows our clients to create CECL forecasts in a fraction of the time it normally takes, using a simple, configurable application that accurately predicts expected losses. The Ascend CECL Forecaster enables you to: Fulfill data requirements: We don’t ask you to gather, prepare or submit any data. The application is comprised of Experian’s extensive historical data, delivered via the Ascend Technology PlatformTM, economic data from Oxford Economics, as well as the auto and home valuation data needed to generate CECL forecasts for each unsecured and secured lending product in your portfolio. Leverage innovative technology: The application uses advanced machine learning models built on 15 years of industry-leading credit data using high-quality Oliver Wyman loan level models. Simplify processes: One of the biggest challenges our clients face is the amount of time and analytical effort it takes to create one CECL forecast, much less several that can be compared for optimal results. With the Ascend CECL Forecaster, creating a forecast is a simple process that can be delivered quickly and accurately. Q: What are immediate next steps? JT: As mentioned, complying with CECL may require you to gather, store and calculate more data than before. Therefore, it’s important that companies act now to better prepare. Immediate next steps include: Establishing your loss forecast methodology: CECL will require a new methodology, making it essential to take advantage of advanced statistical techniques and third-party solutions. Making additional reserves available: It’s imperative to understand how CECL impacts both revenue and profit. According to some estimates, banks will need to increase their reserves by up to 50% to comply with CECL requirements. Preparing your board and investors: Make sure key stakeholders are aware of the potential costs and profit impacts that these changes will have on your bottom line. Speak with an expert

Although half of businesses globally report an increase in fraud management over the past 12 months, many still experience fraud losses and attacks. To help address these challenges, Experian held its first-ever Fintech Fraud & Identity Meetup on February 5 in San Francisco, Calif. The half-day event was aimed at offering insights on the main business drivers of fraud, market trends, challenges and technology advancements that impact identity management and fraud risk strategy operations. “We understand the digital landscape is changing – inevitably, with technology enhancements come increased fraud risk for businesses operating in the online space,” said Jon Bailey, Experian’s Vice President of Fintech. “Our focus today is on fraud and identity, and providing our fintech customers with the tools and insights needed to grow and thrive.” The meetup was attended by number of large fintech companies with services spanning across a broad spectrum of fintech offerings. To kick off the event, Tony Hadley, Experian’s Senior Vice President of Government & Regulatory Affairs, provided an update on the latest regulatory news and trends impacting data and the fintech space. Next followed a fraud and identity expert panel, which engaged seasoned professionals in an in-depth discussion around two main themes 1) fraud trends and risk mitigation; and 2) customer experience, convenience, and trust. Expert panelists included: David Britton, Experian’s Vice President of Industry Solutions; Travis Jarae, One World Identity’s Founder & CEO; George Kurtyka, Joust’s Co-Founder & COO; and Filip Verley, Airbnb’s Product Manager. “The pace of fraud is so fast, by the time companies implement solutions, the shelf-life may already be old,” Britton said. “That is the crux – how to stay ahead. The goal is to future-proof your fraud strategy and capabilities.” At the close of the expert panel, Kathleen Peters, Experian’s Senior Vice President Head of Fraud and Identity, demoed Experian’s CrossCore™ solution – the first smart, open, plug-and-play platform for fraud and identity services. Peters began by stating, “Fraud is constant. Over 60% of businesses report an increase in fraud-related losses over the past year, with the US leading the greatest level of concern. The best way to mitigate risk is to create a layered approach; that’s why Experian invented CrossCore.” With the sophistication of fraudsters, it’s no surprise that many businesses are not confident with the effectiveness of their fraud strategy. Learn more about how you can stay one step ahead of fraudsters and position yourself for success in the ever-changing fraud landscape; download Experian’s 2019 Global Identity and Fraud Report here. For an inside look at Experian’s Fintech Fraud & Identity Meetup, watch our video below.

2018 was a whirlwind of a year – though it was not surprising when Google’s 2018 “most-searched” list showed Fornite GIFs ruled the internet, Black Panther was the most-Googled movie, and the Keto diet was trending (particularly in late December and early January, go figure). But, while Google’s most-searched terms of 2018 present pure pop-culture entertainment, they miss the mark on the trends we find most meaningful being principals of the financial services industry. What about the latest news in fintech? According to Business Insider, fintech companies secured $57.9 billion in funding in the first half of 2018 alone, nearing the previous annual record of $62.5 billion set in 2015. Taking it a step further, CBInsights reports that 24 of 39 fintech unicorns are based in North America. We won’t blame Google for this oversight. Faced with the harsh reality that the “most-searched” results are based on raw-data, perhaps it’s possible that people really do find Fortnite more exciting than financial services trends – but not us at Experian. We have been closely following disruption in the financial services space all while leading the charge in data innovation. When competing in environments where financial institutions vie for customer acquisition and brand loyalty, digital experience is not enough. Today’s world demands finance redefined – and fintechs have answered the call. Fintechs are, by far, among the most innovative technology and data-driven companies in the financial services industry. That’s why we built a team of seasoned consultants, veteran account executives and other support staff that are 100% dedicated to supporting our fintech partners. With our expert team and a data accuracy rate of 99.9%, there isn’t a more reliable fintech source. Perhaps this is one financial services trend that Google can’t ignore (we see you Google)! For more information regarding Experian’s fintech solutions, check out our video below and visit Experian.com/fintech.

From a capricious economic environment to increased competition from new market entrants and a customer base that expects a seamless, customized experience, there are a host of evolving factors that are changing the way financial institutions operate. Now more than ever, financial institutions are turning to their data for insights into their customers and market opportunities. But to be effective, this data must be accurate and fresh; otherwise, the resulting strategies and decisions become stale and less effective. This was the challenge facing OneMain Financial, a large provider of personal installment loans serving 10 million total customers across more than 1,700 branches—creating accurate, timely and robust insights, models and strategies to manage their credit portfolios. Traditionally, the archive process had been an expensive, time-consuming, and labor-intensive process; it can take months from start to finish. OneMain Financial needed a solution to reduce expenses and the time involved in order to improve their core risk modeling.   In this recent IDC Customer Spotlight, sponsored by Experian, "Improving Core Risk Modeling with Better Data Analysis," Steven D’Alfonso, Research Director spoke with the Senior Managing Director and head of model development at OneMain Financial who turned to Experian’s Ascend Analytical Sandbox to improve its core risk modeling through reject inferencing. But OneMain Financial also realized additional benefits and opportunities with the solution including compliance and economic stress testing. Read the customer spotlight to learn more about the explore how OneMain Financial: Reduced expense and effort associated with its archive process Improved risk model development timing from several months to 1-2 weeks Used Sandbox to gain additional market insight including: market share, benchmarking and trends, etc. Read the Case Study

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.

“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.

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?

We use our laptops and mobile phones every day to communicate with our friends, family, and co-workers. But how do software programs communicate with each other? APIs--Application Programming Interfaces--are the hidden backbone of our modern world, allowing software programs to communicate with one another. Behind the scenes of every app and website we use, is a mesh of systems “talking” to each other through a series of APIs. Today, the API economy is quickly changing how the world interacts. Everything from photo sharing, to online shopping, to hailing a cab is happening through APIs. Because of APIs, technical innovation is happening at a faster pace than ever. We caught up with Edgar Uaje, senior product manager at Experian, to find out more about APIs in the financial services space. What exactly are APIs and why are they so important? And how do they apply to B2B? APIs are the building blocks of many of our applications that exist today. They are an intermediary that allows application programs to communicate, interact, and share data with various operating systems or other control programs. In B2B, APIs allow our clients to consume our data, products, and services in a standard format. They can utilize the APIs for internal systems to feed their risk models or external applications for their customers. As Experian has new data and services available, our clients can quickly access the data and services. Are APIs secure? APIs are secure as long as the right security measures are put in place. There are many security measures that can be utilized such as authentication, authorization, channel encryption and payload encryption. Experian takes security seriously and ensures that the right security measures are put in place to protect our data. For example, one of the recent APIs that was built this year utilizes OAuth, channel encryption, and payload encryption. The central role of APIs is promoting innovation and rapid but stable evolution, but they seem to only have taken hold selectively in much of the business world. Is the world of financial services truly ready for APIs? APIs have been around for a long time, but they are getting much more traction recently. Financial tech and online market place lending companies are leading the charge of consuming data, products, and services through APIs because they are nimble and fast. With standard API interfaces, these companies can move as fast as their development teams can. The world of financial services is evolving, and the time is now for them to embrace APIs in day-to-day business. How can APIs benefit a bank or credit union, for example? APIs can benefit a bank or credit union by allowing them to consume Experian data, products, and services in a standard format. The value to them is faster speed to market for applications (internal/external), ease of integration, and control over the user’s experience. APIs allow a bank or credit union to quickly develop new and innovative applications quickly, with the support of their development teams. Can you tell us more about the API Developer Portal? Experian will publish the documentation of our available APIs on our Developer Portal over time as they become available. Our clients will have a one-stop shop to view available APIs, review API documentation, obtain credentials, and test APIs. This is simplifying data access by utilizing REST API, making it easier for our clients.