Tag: artificial intelligence

Establishing a strong digital strategy remains a top priority for most financial institutions. With more eyes on screens and electronic devices, the pandemic-induced shift to digital has increased the need to meet consumers where they are. New Innovations As a Result of an Accelerated Shift to Digital  In Ernst & Young’s 2019 biannual Global Fintech Adoption Index, 46% of American respondents indicated they were using at least one fintech service. Fast forward, COVID-19 has accelerated the American adoption rate to 59%, according to a September survey conducted by Plaid, a leading digital payments infrastructure company. This shift to digital also resulted in an uptick in the creation of banking and savings processes that leverage advanced technologies. For example, digital-first technologies and artificial intelligence (AI) are changing the prescreen landscape as never before. For financial institutions, smart prescreen marketing solutions, coupled with a traditional approach to personalized service, present vast opportunities to build deeper consumer relationships. However, implementing an effective strategy can be challenging. In a recent webinar, Experian’s Vice President of Product Management Jacob Kong tackled the topic of using new analytics and AI to create a digital-first strategy. Joined by Mark Sievewright, founder of Sievewright & Associates and co-author of Digital Life, and Devon Kinkead, CEO of Micronotes.ai, they explored the evolution of banking and the possibilities offered by pairing data with technology in our new digital world. Watch the full webinar, 'Digital-First Strategies: New Analytics and Artificial Intelligence for Marketing,' and learn more about: The shift to digital life and banking, new analytics and AI How data and information value empowers prescreen marketing Emerging technologies and new tools that can support aggressive growth and marketing initiatives while mitigating risk How Experian is joining forces with Micronotes.ai to launch Micronotes ReFI powered by Experian, to help lower customers’ or members’ borrowing costs by refinancing mispriced debt Learn more about Micronotes ReFI powered by Experian To explore how Experian’s solutions and capabilities can power your prescreen and marketing strategies, please visit our solutions page or contact us for more information. Contact Us

The tax gap—the difference between what taxpayers should pay and what they actually pay on time—can have a substantial impact on states’ budgets. Tax agencies and other state departments are responsible for helping states manage their budgets by minimizing expected revenue shortfalls. Underreported income is a significant budget complication that continues to frustrate even the most effective tax agencies, until the right tools are brought into play.   The Problem Underreporting is a large, complex issue for agencies. The IRS currently estimates the annual tax gap at $441 billion. There are multiple factors that comprise that total, but the most prevalent is underreporting, which represents 80% of the total tax gap. Of that, 54% is due to underreporting of individual income tax. In addition to being the largest contributor to the tax gap, underreporting is also extremely challenging to identify out of the millions of returns being filed. With 85% of taxes owed correctly reported and paid, finding underreporting can be like trying to locate a needle in the proverbial haystack. Making this even more challenging is the limited resources available for auditing returns, which makes efficiency key. The Solution Data, combined with artificial intelligence (AI) equals efficient detection. The problem with trying to detect which returns are most likely to have underreported income is similar to many other challenges Experian has solved with AI. Partnerships between Experian and state agencies combine what we know about consumers with what their agency knows about their population. We can take the data and use AI to separate the signal from the noise, finding opportunities to recoup lost revenue. Read our case study on how Experian was able to help an agency identify instances of underreporting, detecting an estimated $80 million annual lost revenue from underreported income. Download case study Contact us

The COVID-19 pandemic has created shifting economic conditions and rapidly evolving consumer preferences. Lenders must keep up by re-evaluating their strategies to accelerate growth and beat the competition. Here's how AI/ML can help your organization evolve post-COVID-19: With the democratization of AI/ML, lenders of all sizes can now use this technology to grow their lending and optimize for strategic growth. Register for our upcoming webinar to see how lenders like Elevate have incorporated this new technology into their business processes. Register now

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

According to Experian’s latest Global Insights Report, 38% of consumers expect to increase their online activity in the next 12 months. The report also found that consumers continue to have high expectations for their online experience, and businesses are re-imagining the customer journey to reflect that need. This January, Experian surveyed 3,000 consumers and 900 businesses to explore the changes in consumer behavior and business strategy pre- and post-COVID-19. As consumers have embraced life online, they’ve continued to emphasize their feelings regarding the importance of protecting their information. More than half of consumers still consider security to be the most important factor in their digital experience – the same experience they have such high expectations of. Business are acting in turn, with more than half investing in fraud detection methods or software to reduce friction in the customer experience. Digital transformation is also highlighting the need to: Manage regulatory compliance Integrate security measures Ensure access to AI models Attract and manage customers Integrate automation solutions Download the report to get all the latest insights into consumer desires and business behaviors, and keep visiting the Insights blog for a deeper dive into US-specific findings. Download report  

While an overdue economic downturn has been long discussed, arguably no one could have foreseen the economic disruption from COVID-19 to the extent that’s been witnessed thus far. But now that we’re here, is there a line of sight to financial institutions’ next move? With the current situation marked by a history-making rise in unemployment, massive amounts of uncertainty within the market as well as for consumers and small businesses and consumer spending changes, loss forecasting is more important now than ever before. After the longest period of economic growth in history, financial institutions are caught off guard. While large banks are more prepared as they have stress testing capabilities in place and are estimating the potential large impact on their loss allowances, the since-delayed CECL requirements emphasized forecasting for the masses, and yet many are still under-equipped. Loss forecasting has evolved from a need for a small few to now a necessary strategy for all. While some financial institutions will look to loss forecasting to potentially reduce the severity of impact for the path ahead during these times (or even how they might come out stronger than their competition), for many, loss forecasting is the key to survival. Bare necessities. Understanding the possible outcomes of the pandemic’s impact is necessary to make critical business decisions. Lenders are likely receiving numerous questions about their portfolios and possible outcomes. These questions include, but are not limited to: What could the range of outcomes to my portfolio based on expert forecasts of macroeconomic conditions? How will I make lending decisions in the short term? Do my models need to change? How bad could charge offs be for my portfolio? If I have reduced marketing and application flows, at what point do I need to begin opening new accounts or consider portfolio acquisitions? How can lenders get answers? Loss forecasting. As Mohammed Chaudhri, Experian Chief Economist, said, “Loss forecasting is more pivotal than ever…existing models are not going to be up to the task of accurately predicting losses.” Whatever questions you’re receiving, you need certain necessary pieces of information to navigate this new era of loss forecasting. Those pieces are frequently updated client and industry data; ongoing access to expert macroeconomic forecasts; and sophisticated and evolved forecasting models. Client and Industry Data Loan-level data, bankruptcy scores and customer-level attributes are key insights to fueling loss forecasting models. By combining several data sets and scores (and a comprehensive history of both) your organization can see greater benefits. Macroeconomic Forecasts As has been mentioned numerous times, the economic impact resulting from COVID-19 is not at all like the Great Recession. As such, leveraging macroeconomic forecasts, and specifically COVID-19 forecasts, is critical to analyzing the potential impacts to your organization. Sophisticated Models Whether building models on your own or leveraging an expert, the key ingredients include the innerworkings of the model, leveraging historical data and making sure that both the models and the data are updated regularly to ensure you have the most accurate, thorough forecasts available. Also, leveraging machine learning tools is imperative for model specification and evaluation. Fortunately, while model building and loss forecasting used to be synonymous with countless resources and dollar signs, innovation and digital transformation have made these strategies within reach for financial institutions of all sizes. Incorporating the right data (and ensuring that data is regularly updated), with the right tools and macroeconomic scenarios (including COVID-19, upside, baseline, adverse and severely adverse scenarios) enables you to get a line of sight into the actions you need to take now. Empowered with insights to compare and benchmark results, discover the cause of changes in results, explore result scenarios in advance, and access recommended optimizations, loss forecasting enables you to focus on the critical decisions your business depends on. Experian helps you with loss forecasting for now and the future. For more information, including an on-demand webinar Experian presented with Oliver Wyman as well as the opportunity to engage Experian experts into your loss forecasting strategy, please click the button below. Learn More

Last week, artificial intelligence (AI) made waves in the news as the Vatican and tech giants signed a statement with a set of guidelines calling for ethical AI. These ethical concerns arose as the usage of artificial intelligence continues to increase in all industries – with the market for AI technology projected to reach $190.61 billion by 2025, according to a report from MarketsandMarkets™. In the “Rome Call for Ethics,” these new principles require that AI systems must adhere to ethical AI guidelines to protect basic human rights. The doctrine says AI must be developed with a focus on protecting and serving humanity, and that all algorithms should be designed by the principles of transparency, inclusion, responsibility, impartiality, reliability, security and privacy.  In addition, according to the document, organizations must consider the “duty of explanation” and ensure that decisions made as a result of these algorithms are explainable, transparent and fair. As artificial intelligence becomes increasingly used in many applications and ingrained into our everyday lives (facial recognition, lending decisions, virtual assistants, etc.), establishing new guidelines for ethical AI and its usage has become more critical than ever. For lenders and financial institutions, AI is poised to shape the future of banking and credit cards. AI is now being used to generate credit insights, reduce risk and make credit more widely available to more credit-worthy consumers. However, one of the challenges of AI is that these algorithms often can’t explain their reasoning or processes. That’s why AI explainability, or the methods and techniques in AI that make the results of the solution understandable by human experts, remains a large barrier for many institutions when it comes to AI adoption. The concept of ethical AI goes hand-in-hand with Regulation B of the Equal Opportunity Act (ECOA), which protects consumers from discrimination in any aspect of a credit transaction and requires that consumers receive clear explanations when lenders take adverse action. Adverse action letters, which are intended to inform consumers on why their credit applications were denied, must be transparent and incorporate reasons on why the decision was made – in order to promote fair lending. While ethical AI has made recent headlines, it’s not a new concept. Last week’s news highlights the need for explainability best practices for financial institutions as well as other organizations and industries. The time is now to implement these guidelines into algorithms and business processes of the present and future. Join our upcoming webinar as Experian experts dive into fair lending with ethical and explainable AI. Register now

While many companies are interested in implementing technology with advanced analytic capabilities, the concepts behind the technology can often be hard to understand. Demystifying the terminology around artificial intelligence and machine learning is one of the first steps for successful implementation. Discover what they mean for your financial institution in our new infographic: Learn more

Machine learning, once a mysterious and unknown field, has come a long way throughout the years. Now, it's being implemented across a variety of industries - and expertise in all things related to machine learning is in high demand. Take a journey through the history of machine learning in our new infographic: Read the e-book

In today’s ever-changing and hypercompetitive environment, the customer experience has taken center-stage – highlighting new expectations in the ways businesses interact with their customers. But studies show financial institutions are falling short. In fact, a recent study revealed that 94% of banking firms can’t deliver on the “personalization promise.” It’s not difficult to see why. Consumer preferences have changed, with many now preferring digital interactions. This has made it difficult for financial institutions to engage with consumers on a personal level. Nevertheless, customers expect seamless, consistent, and personalized experiences – that’s where the power of advanced analytics comes into play. It’s no secret that using advanced analytics can enable businesses to turn rich data into insights that lead to confident business decisions and strategy development. But these business tools can actually help financial institutions deliver on that promise of personalization. According to an Experian study, 90% of organizations say that embracing advanced analytics is critical to their ability to provide an excellent customer experience. By using data and analytics to anticipate and respond to customer behavior, companies can develop new and creative ways to cater to their audiences – revolutionizing the customer experience as a whole. It All Starts With Data Data is the foundation for a successful digital transformation – the lack of clean and cohesive datasets can hinder the ability to implement advanced analytic capabilities. However,  89% of organizations face challenges on how to effectively manage and consolidate their data, according to Experian’s Global Data Management Research Benchmark Report of 2019. Because consumers prefer digital interactions, companies have been able to gather a vast amount of customer data. Technology that uses advanced analytic capabilities (like machine learning and artificial intelligence) are capable of uncovering patterns in this data that may not otherwise be apparent, therefore opening doors to new avenues for companies to generate revenue. To start, companies need a strategy to access all customer data from all channels in a cohesive ecosystem – including data from their own data warehouses and a variety of different data sources. Depending on their needs, the data elements can come from a third party data provider such as: a credit bureau, alternative data, marketing data, data gathered during each customer contact, survey data and more. Once compiled, companies can achieve a more holistic and single view of their customer. With this single view, companies will be able to deliver more relevant and tailored experiences that are in-line with rising customer expectations. From Personalized Experiences to Predicting the Future The most progressive financial institutions have found that using analytics and machine learning to conquer the wide variety of customer data has made it easier to master the customer experience. With advanced analytics, these companies gain deeper insights into their customers and deliver highly relevant and beneficial offers based on the holistic views of their customers. When data is provided, technology with advanced analytic capabilities can transform this information into intelligent outputs, allowing companies to optimize and automate business processes with the customer in mind. Data, analytics and automation are the keys to delivering better customer experiences. Analytics is the process of converting data into actionable information so firms can understand their customers and take decisive action. By leveraging this business intelligence, companies can quickly adapt to consumer demand. Predictive models and forecasts, increasingly powered by machine learning, help lenders and other businesses understand risks and predict future trends and consumer responses. Prescriptive analytics help offer the right products to the right customer at the right time and price. By mastering all of these, businesses can be wherever their customers are. The Experian Advantage With insights into over 270 million customers and a wealth of traditional credit and alternative data, we’re able to drive prescriptive solutions to solve your most complex market and portfolio problems across the customer lifecycle – while reinventing and maintaining an excellent customer experience. If your company is ready for an advanced analytical transformation, Experian can help get you there. Learn More

AI, machine learning, and Big Data – these are no longer just buzzwords. The advanced analytics techniques and analytics-based tools that are available to financial institutions today are powerful but underutilized. And the 30% of banks, credit unions and fintechs successfully deploying them are driving better data-driven decisions, more positive customer experiences and stronger profitability. As the opportunities surrounding advanced analytics continue to grow, more lenders are eager to adopt these capabilities to make the most of their datasets. And it’s understandable that financial institution are excited at the possibilities and insights that advanced analytics can bring to their business. However, there are some key considerations to keep in mind as you begin this important digital transformation. Here are three things you should do as your financial institution begins its advanced analytics journey. Ensure consistent and clean data quality Companies have a plethora of data and information on their customers. The main hurdles that many organizations face is being able to turn this information into a clean and cohesive dataset and formulating an effective and long-term data management strategy. Trying to implement advanced analytic capabilities while lacking an effective data governance strategy is like building a house on a poor foundation – likely to fail. Data quality issues, such as inconsistent data, data gaps, and incomplete and duplicated data, also haunt many organizations, making it difficult to complete their analytics objectives. Ensuring that issues in data quality are managed is the key to gaining the correct insights for your business.   Establish and maintain a single view of customers The power of advanced analytics can only be as strong as the data provided. Unfortunately, many companies don’t realize that advanced analytics is much more powerful when companies are able to establish a single view of their customers. Companies need to establish and maintain a single view of customers in order to begin implementing advanced analytic capabilities. According to Experian research, a single customer view is a consistent, accurate and holistic view of your organization’s customers, prospects, and their data. Having full visibility and a 360 view into your customers paves the way for companies to make personalized, relevant, timely and precise decisions. But as many companies have begun to realize, getting this single view of customers is easier said than done. Organizations need to make sure that data should always be up-to-date, unique and available in order to begin a complete digital transformation.   Ensure the right resources and commitment for your advanced analytics initiative It’s important to have the top-down commitment within your organization for advanced analytics. From the C-suite down, everyone should be on the same page as to the value analytics will bring and the investment the project might require. Organizations that want to move forward with implementing advanced analytic capabilities need to make sure to set aside the right financial and human resources that will be needed for the journey. This may seem daunting, but it doesn’t have to be. A common myth is that the costs of new hardware, new hires and the costs required to maintain, configure, and set up new technology will make advanced analytics implementation far too expensive and difficult to maintain. However, many organizations don’t realize that it’s not necessary to allocate large capital expenses to implement advanced analytics. All it takes is finding the right-sized solution with configurations to fit the team size and skill level in your organization. Moreover, finding the right partner and team (whether internal or external) can be an efficient way to fill temporary skills gaps on your team. No digital transformation initiative is without its challenges. However, beginning your advanced analytics journey on the right footing can deliver unparalleled growth, profitability and opportunities. Still not sure where to begin? At Experian, we offer a wide range of solutions to help you harness the full power and potential of data and analytics. Our consultants and development teams have been a game-changer for financial institutions, helping them get more value, insight and profitability out of their data and modeling than ever before. Learn More

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

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

The fact that the last recession started right as smartphones were introduced to the world gives some perspective into how technology has changed over the past decade. Organizations need to leverage the same technological advancements, such as artificial intelligence and machine learning, to improve their collections strategies. These advanced analytics platforms and technologies can be used to gauge customer preferences, as well as automate the collections process. When faced with higher volumes of delinquent loans, some organizations rapidly hire inexperienced staff. With new analytical advancements, organizations can reduce overhead and maintain compliance through the collections process. Additionally, advanced analytics and technology can help manage customers throughout the customer life cycle. Let’s explore further: Why use advanced analytics in collections? Collections strategies demand diverse approaches, which is where analytics-based strategies and collections models come into play. As each customer and situation differs, machine learning techniques and constraint-based optimization can open doors for your organization. By rethinking collections outreach beyond static classifications (such as the stage of account delinquency) and instead prioritizing accounts most likely to respond to each collections treatment, you can create an improved collections experience. How does collections analytics empower your customers? Customer engagement, carefully considered, perhaps comprises the most critical aspect of a collections program—especially given historical perceptions of the collections process. Experian recently analyzed the impact of traditional collections methods and found that three percent of card portfolios closed their accounts after paying their balances in full. And 75 percent of those closures occurred shortly after the account became current. Under traditional methods, a bank may collect outstanding debt but will probably miss out on long-term customer loyalty and future revenue opportunities. Only effective technology, modeling and analytics can move us from a linear collections approach towards a more customer-focused treatment while controlling costs and meeting other business objectives. Advanced analytics and machine learning represent the most important advances in collections. Furthermore, powerful digital innovations such as better criteria for customer segmentation and more effective contact strategies can transform collections operations, while improving performance and raising customer service standards at a lower cost. Empowering consumers in a digital, safe and consumer-centric environment affects the complete collections agenda—beginning with prevention and management of bad debt and extending through internal and external account resolution. When should I get started? It’s never too early to assess and modernize technology within collections—as well as customer engagement strategies—to produce an efficient, innovative game plan. Smarter decisions lead to higher recovery rates, automation and self-service tools reduce costs and a more comprehensive customer view enhances relationships. An investment today can minimize the negative impacts of the delinquency challenges posed by a potential recession. Collections transformation has already begun, with organizations assembling data and developing algorithms to improve their existing collections processes. In advance of the next recession, two options present themselves: to scramble in a reactive manner or approach collections proactively. Which do you choose? Get started