Tag: data quality

With great risk comes great reward, as the saying goes. But when it comes to business, there's huge value in reducing and managing that risk as much as possible to maximize benefits — and profits. In today's high-tech strategic landscape, financial institutions and other organizations are increasingly using risk modeling to map out potential scenarios and gain a clearer understanding of where various paths may lead. But what are risk models really, and how can you ensure you're creating and using them correctly in a way that actually helps you optimize decision-making? Here, we explore the details. What is a risk model? A risk model is a representation of a particular situation that's created specifically for the purpose of assessing risk. That risk model is then used to evaluate the potential impacts of different decisions, paths and events. From assigning interest rates and amortization terms to deciding whether to begin operating in a new market, risk models are a safe way to analyze data, test assumptions and visualize potential scenarios. Risk models are particularly valuable in the credit industry. Credit risk models and credit risk analytics allow lenders to evaluate the pluses and minuses of lending to clients in specific ways. They are able to consider the larger economic environment, as well as relevant factors on a micro level. By integrating risk models into their decision-making process, lenders can refine credit offerings to fit the assessed risk of a particular situation. It goes like this: a team of risk management experts builds a model that brings together comprehensive datasets and risk modeling tools that incorporate mathematics, statistics and machine learning. This predictive modeling tool uses advanced algorithmic techniques to analyze data, identify patterns and make forecasts about future outcomes. Think of it as a crystal ball — but with science behind it. Your team can then use this risk model for a wide range of applications: refining marketing targets, reworking product offerings or reshaping business strategies. How can risk models be implemented? Risk models consolidate and utilize a wide variety of data sets, historical benchmarks and qualitative inputs to model risk and allow business leaders to test assumptions and visualize the potential results of various decisions and events. Implementing risk modeling means creating models of systems that allow you to adjust variables to imitate real-world situations and see what the results might be. A mortgage lender, for example, needs to be able to predict the effects of external and internal policies and decisions. By creating a risk model, they can test how scenarios such as falling interest rates, rising unemployment or a shift in loan acceptance rates might affect their business — and make moves to adjust their strategies accordingly. One aspect of risk modeling that can't be underestimated is the importance of good data, both quantitative and qualitative. Efforts to implement or expand risk modeling should begin with refining your data governance strategy. Maximizing the full potential of your data also requires integrating data quality solutions into your operations in order to ensure that the building blocks of your risk model are as accurate and thorough as possible. It's also important to ensure your organization has sufficient model risk governance in place. No model is perfect, and each comes with its own risks. But these risks can be mitigated with the right set of policies and procedures, some of which are part of regulatory compliance. With a comprehensive model risk management strategy, including processes like back testing, benchmarking, sensitivity analysis and stress testing, you can ensure your risk models are working for your organization — not opening you up to more risk. How can risk modeling be used in the credit industry? Risk modeling isn't just for making credit decisions. For instance, you might model the risk of opening or expanding operations in an underserved country or the costs and benefits of existing one that is underperforming. In information technology, a critical branch of virtually every modern organization, risk modeling helps security teams evaluate the risk of malicious attacks. Banking and financial services is one industry for which understanding and planning for risk is key — not only for business reasons but to align with relevant regulations. The mortgage lender mentioned above, for example, might use credit risk models to better predict risk, enhance the customer journey and ensure transparency and compliance. It's important to highlight that risk modeling is a guide, not a prophecy. Datasets can contain flaws or gaps, and human error can happen at any stage.. It's also possible to rely too heavily on historical information — and while they do say that history repeats itself, they don't mean it repeats itself exactly. That's especially true in the presence of novel challenges, like the rise of artificial intelligence. Making the best use of risk modeling tools involves not just optimizing software and data but using expert insight to interpret predictions and recommendations so that decision-making comes from a place of breadth and depth. Why are risk models important for banks and financial institutions? In the world of credit, optimizing risk assessment has clear ramifications when meeting overall business objectives. By using risk modeling to better understand your current and potential clients, you are positioned to offer the right credit products to the right audience and take action to mitigate risk. When it comes to portfolio risk management, having adequate risk models in place is paramount to meet targets. And not only does implementing quality portfolio risk analytics help maximize sales opportunities, but it can also help you identify risk proactively to avoid costly mistakes down the road. Risk mitigation tools are a key component of any risk modeling strategy and can help you maintain compliance, expose potential fraud, maximize the value of your portfolio and create a better overall customer experience. Advanced risk modeling techniques In the realm of risk modeling, the integration of advanced techniques like machine learning (ML) and artificial intelligence (AI) is revolutionizing how financial institutions assess and manage risk. These technologies enhance the predictive power of risk models by allowing for more complex data processing and pattern recognition than traditional statistical methods. Machine learning in risk modeling: ML algorithms can process vast amounts of unstructured data — such as market trends, consumer behavior and economic indicators — to identify patterns that may not be visible to human analysts. For instance, ML can be used to model credit risk by analyzing a borrower’s transaction history, social media activities and other digital footprints to predict their likelihood of default beyond traditional credit scoring methods. Artificial intelligence in decisioning: AI can automate the decisioning process in risk management by providing real-time predictions and risk assessments. AI systems can be trained to make decisions based on historical data and can adjust those decisions as they learn from new data. This capability is particularly useful in credit underwriting where AI algorithms can make rapid decisions based on market conditions. Financial institutions looking to leverage these advanced techniques must invest in robust data infrastructure, skilled personnel who can bridge the gap between data science and financial expertise, and continuous monitoring systems to ensure the models perform as expected while adhering to regulatory standards. Challenges in risk model validation Validating risk models is crucial for ensuring they function appropriately and comply with regulatory standards. Validation involves verifying both the theoretical foundations of a model and its practical implementation. Key challenges in model validation: Model complexity: As risk models become more complex, incorporating elements like ML and AI, they become harder to validate. Complex models can behave in unpredictable ways, making it difficult to understand why they are making certain decisions (the so-called "black box" issue). Data quality and availability: Effective validation requires high-quality, relevant data. Issues with data completeness, accuracy or relevance can lead to incorrect model validations. Regulatory compliance: With regulations continually evolving, keeping risk models compliant can be challenging. Different jurisdictions may have varying requirements, adding to the complexity of validation processes. Best practices: Regular reviews: Continuous monitoring and periodic reviews help ensure that models remain accurate over time and adapt to changing market conditions. Third-party audits: Independent reviews by external experts can provide an unbiased assessment of the risk model’s performance and compliance. These practices help institutions maintain the reliability and integrity of their risk models, ensuring that they continue to function as intended and comply with regulatory requirements. Read more: Blog post: What is model governance? How Experian can help Risk is inherent to business, and there's no avoiding it entirely. But integrating credit risk modeling into your operations can ensure stability and profitability in a rapidly evolving business landscape. Start with Experian's credit modeling services, which use expansive data, analytical expertise and the latest credit risk modeling methodologies to better predict risk and accelerate growth. Learn more *This article includes content created by an AI language model and is intended to provide general information.

As quarantine restrictions lift and businesses reopen, there is still uncertainty in the mortgage market. Research shows that more than two million households face foreclosure as moratoriums expire. And with regulators, like the Consumer Financial Protection Bureau (CFPB), urging mortgage servicers to prepare for an expected surge in homeowners needing assistance, lenders need the right resources as well. One of the resources mortgage lenders rely on to help gain greater insight into their borrower’s financial picture is income and employment verification. The challenge, however, is striking the right balance between gaining the insights needed to support lending decisions and creating a streamlined, frictionless mortgage process. There are three main barriers on the path to a seamless and digital verification process. Legacy infrastructure Traditional verification solutions tend to rely on old technology or processes. Whether a lender’s verification strategy is centered around a solution built on older technology or a manual process, the time to complete a borrower verification can vary from taking a day to weeks. Borrowers have grown accustomed to digital experiences that are simple and frictionless and experiencing a drawn out, manual verification process is likely to impact loyalty to the lender’s brand. Stale employment and income data The alternative to a manual process is an instant hit verification solution, with the aim to create a more seamless borrower experience. However, lenders may receive stale borrower income and employment data back as a match. Consumer circumstances can change frequently in today’s economic environment and, depending on the data source the lender is accessing, data may be out of date or simply incorrect. Decisioning based on old information is problematic since it can increase origination risk. Cost and complexity Lenders that use manual processes to verify information are adding to their time to close and ultimately, their bottom line by way of time and resources. Coupled with pricing increases, lenders are paying more to put their borrowers through a cumbersome and sometimes lengthy process to verify employment and income information. How can mortgage lenders avoid these common pitfalls in their verification strategy? By seeking verification solutions focused on innovation, quality of data, and that are customer-centric. The right tool, such as Experian VerifyTM, can help provide a seamless customer experience, reduce risk, and streamline the verification process. Learn more

The ongoing COVID-19 pandemic has facilitated an increase in information collection among consumers and organizations, creating a prosperous climate for cybercriminals. As businesses and customers adjust to the “new normal,” hackers are honing in on their targets and finding new, more sophisticated ways to access their sensitive data. As part of our recently launched Q&A perspective series, Michael Bruemmer, Experian’s Vice President of Data Breach Resolution and Consumer Protection, provided insight on emerging fraud schemes related to the COVID-19 vaccines and how increased use of digital home technologies could lead to an upsurge in identity theft and ransomware attacks. Check out what he had to say: Q: How did Experian determine the top data breach trends for 2021? MB: As part of our initiative to help organizations prevent data breaches and protect their information, we release an annual Data Breach Forecast. Prior to the launch of the report, we analyze market and consumer trends. We then come up with a list of potential predictions based off the current climate and opportunities for data breaches that may arise in the coming year. Closer to publication, we pick the top five ‘trends’ and craft our supporting rationale. Q: When it comes to data, what is the most immediate threat to organizations today? MB: Most data breaches that we service have a root cause in employee errors – and working remotely intensifies this issue. Often, it’s through negligence; clicking on a phishing link, reusing a common password for multiple accounts, not using two-factor authentication, etc. Organizations must continue to educate their employees to be more aware of the dangers of an internal breach and the steps they can take to prevent it. Q: How should an organization begin to put together a comprehensive threat and response review? MB: Organizations that excel in cybersecurity often are backed by executives that make comprehensive threats and response reviews a top corporate priority. When the rest of the organization sees higher-ups emphasizing the importance of fraud prevention, it’s easier to invest time and money in threat assessments and data breach preparedness. Q: What fraud schemes should consumers be looking out for? MB: The two top fraud schemes that consumers should be wary of are scams related to the COVID-19 vaccine rollout and home devices being held for ransom. Fraudsters have been leveraging social media to spread harmful false rumors and misinformation about the vaccines, their effectiveness and the distribution process. These mistruths can bring harm to supply chains and delay government response efforts. And while ransomware attacks aren’t new, they are getting smarter and easier with people working, going to school and hosting gatherings entirely on their connected devices. With control over home devices, doors, windows, and security systems, cybercriminals have the potential to hold an entire house hostage in exchange for money or information. For more insight on how to safeguard your organization and consumers from emerging fraud threats, watch our Experian Symposium Series event on-demand and download our 2021 Data Breach Industry Forecast. Watch now Access forecast About Our Expert: Michael Bruemmer, Experian VP of Data Breach Resolution and Consumer Protection, North America Michael manages Experian’s dedicated Data Breach Resolution and Consumer Protection group, which aims to help businesses better prepare for a data breach and mitigate associated consumer risks following breach incidents. With over 25 years in the industry, he has guided organizations of all sizes and sectors through pre-breach response planning and delivery.

The ongoing COVID-19 crisis and the associated rise in online transactions have made it more important than ever to keep customer information accurate and company databases up to date. By ensuring your organization’s data quality, you can allocate resources more effectively, minimize costs and safely serve your customers. As part of our recently launched Q&A perspective series, Suzanne Pomposello, Experian’s Strategic Account Director for CEM vertical markets, and William Palmer, Senior Sales Engineer, provided insight on how utility providers can manage and maintain accurate client data during system migrations and modernizations, achieve a single customer view and implement an operational data quality program. Check out what they had to say: Q: What are the best practices for effective data quality management that utility providers should follow? SP: To ensure data quality, we advise starting with a detailed understanding of the data your organization is currently maintaining and how new data entering your systems is being utilized. Conducting a baseline assessment and being able to properly validate the accuracy of your data is key to identifying areas that require cleansing and enrichment. Once you know what improvements and corrections need to be made, you can establish a strategy that will empower your organization to unlock the full potential of your data. Q: How does Experian help clients improve their data hygiene? SP: Experian has over 30 years of expertise in data cleansing, which is tapped to help clients deploy tactics and strategies to ensure an acceptable level of data integrity. First, we obtain a complete picture of each organizations objectives and challenges. We then assess the quality of their data and identify sources that require remediation. Armed with insight, we work alongside organizations to develop a phased action plan to standardize and enhance their data. Our data management solutions satisfy a wide range of needs and can be consumed in real-time, bulk and batch form. Q: Are there any protection regulations to be aware of when obtaining updated data? WP: Unlike Experian’s regulated divisions, most Experian Data Quality data elements are not burdened by complex regulations and restrictions. Our focus is on organizations’ main customer data points (e.g., address, email address and phone). We reference this data against unregulated source systems to validate, append and complete customer profiles. Experian’s data quality management tools can serve as a foundation for many regulatory, compliance and governance requirements, including, Metro 2 reporting, TCPA and CCPA. Q: Are demos of Experian’s data management solutions available? If so, where can they be accessed? WP: Yes, you can visit our website to view product functionality clips and recorded demonstrations. Additionally, we welcome the opportunity to explore our comprehensive data quality management tools via tests and live demonstrations using actual client data to gain a better understanding of how our solutions can be used to improve operational efficiency and the customer experience. For more insight on how to cleanse, standardize, and enhance your data to make sure you get the most out of your information, watch our Experian Symposium Series event on-demand. Watch now Learn more About Our Experts: Suzanne Pomposello, Strategic Account Director, Experian Data Quality, North America Suzanne manages the energy vertical for Experian’s Data Quality division, supporting North America. She brings innovative solutions to her clients by leveraging technology to deliver accurate and validated contact data that is fit for purpose. William Palmer, Senior Sales Engineer, Experian Data Quality, North America William is a Senior Sales Engineer for Experian’s Data Quality division, supporting North America. As an expert in the data quality space, he advises utility clients on strategies for immediate and long-term data hygiene practices, migrations and reporting accuracy.

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

Every morning, I wake up and walk bleary eyed to the bathroom, pop in my contacts and start my usual routine. Did I always have contacts? No. But putting on my contacts and seeing clearly has become part of my routine. After getting used to contacts, wearing glasses pales in comparison. This is how I view alternative credit data in lending. Are you having qualms about using this new data set? I get it, it’s like sticking a contact into your eye for the first time: painful and frustrating because you’re not sure what to do. To relieve you of the guesswork, we’ve compiled the top four myths related to this new data set to provide an in-depth view as to why this data is an essential supplement to your traditional credit file. Myth 1: Alternative credit data is not relevant. As consumers are shifting to new ways of gaining credit, it’s important for the industry to keep up. These data types are being captured by specialty credit bureaus. Gone are the days when alternative financing only included the payday store on the street corner. Alternative financing now expands to loans such as online installment, rent-to-own, point-of-sale financing, and auto-title loans. Consumers automatically default to the financing source familiar to them – which doesn’t necessarily mean traditional financial institutions. For example, some consumers may not walk into a bank branch anymore to get a loan, instead they may search online for the best rates, find a completely digital experience and get approved without ever leaving their couches. Alternative credit data gives you a lens into this activity. Myth 2: Borrowers with little to no traditional credit history are high risk. A common misconception of a thin-file borrower is that they may be high risk. According to the CFPB, roughly 45 million Americans have little to no credit history and this group may contain minority consumers or those from low income neighborhoods. However, they also may contain recent immigrants or young consumers who haven’t had exposure to traditional credit products. According to recent findings, one in five U.S. consumers has an alternative financial services data hit– some of these are even in the exceptional or very good credit segments. Myth 3: Alternative credit data is inaccurate and has poor data quality. On the contrary, this data set is collected, aggregated and verified in the same way as traditional credit data. Some sources of data, such as rental payments, are monthly and create a consistent look at a consumer’s financial behaviors. Experian’s Clarity Services, the leading source of alternative finance data, reports their consumer information, which includes application information and bank account data, as 99.9% accurate. Myth 4: Using alternative credit data might be harmful to the consumer. This data enables a more complete view of a consumer’s credit behavior for lenders, and provides consumers the opportunity to establish and maintain a credit profile. As with all information, consumers will be assessed appropriately based on what the data shows about their credit worthiness. Alternative credit data provides a better risk lens to the lender and consumers may get more access and approval for products that they want and deserve. In fact, a recent Experian survey found 71% of lenders believe alternative credit data will help consumers who would have previously been declined. Like putting in a new pair of contact lenses the first time, it may be uncomfortable to figure out the best use for alternative credit data in your daily rhythm. But once it’s added, it’s undeniable the difference it makes in your day-to-day decisions and suddenly you wonder how you’ve survived without it so long. See your consumers clearly today with alternative credit data. Learn More About Alternative Credit Data

If your company is like many financial institutions, it’s likely the discussion around big data and financial analytics has been an ongoing conversation. For many financial institutions, data isn’t the problem, but rather what could or should be done with it. Research has shown that only about 30% of financial institutions are successfully leveraging their data to generate actionable insights, and customers are noticing. According to a recent study from Capgemini,  30% of US customers and 26% of UK customers feel like their financial institutions understand their needs. No matter how much data you have, it’s essentially just ones and zeroes if you’re not using it. So how do banks, credit unions, and other financial institutions who capture and consume vast amounts of data use that data to innovate, improve the customer experience and stay competitive? The answer, you could say, is written in the sand. The most forward-thinking financial institutions are turning to analytical environments, also known as a sandbox, to solve the business problem of big data. Like the name suggests, a sandbox is an environment that contains all the materials and tools one might need to create, build, and collaborate around their data. A sandbox gives data-savvy banks, credit unions and FinTechs access to depersonalized credit data from across the country. Using custom dashboards and data visualization tools, they can manipulate the data with predictive models for different micro and macro-level scenarios. The added value of a sandbox is that it becomes a one-stop shop data tool for the entire enterprise. This saves the time normally required in the back and forth of acquiring data for a specific to a project or particular data sets. The best systems utilize the latest open source technology in artificial intelligence and machine learning to deliver intelligence that can inform regional trends, consumer insights and highlight market opportunities. From industry benchmarking to market entry and expansion research and campaign performance to vintage analysis, reject inferencing and much more. An analytical sandbox gives you the data to create actionable analytics and insights across the enterprise right when you need it, not months later. The result is the ability to empower your customers to make financial decisions when, where and how they want. Keeping them happy keeps your financial institution relevant and competitive. Isn’t it time to put your data to work for you? Learn more about how Experian can solve your big data problems. >> Interested to see a live demo of the Ascend Sandbox? Register today for our webinar “Big Data Can Lead to Even Bigger ROI with the Ascend Sandbox.”

Big Data is no longer a new concept. Once thought to be an overhyped buzzword, it now underpins and drives billions in dollars of revenue across nearly every industry. But there are still companies who are not fully leveraging the value of their big data and that’s a big problem. In a recent study, Experian and Forrester surveyed nearly 600 business executives in charge of enterprise risk, analytics, customer data and fraud management. The results were surprising: while 78% of organizations said they have made recent investments in advanced analytics, like the proverbial strategic plan sitting in a binder on a shelf, only 29% felt they were successfully using these investments to combine data sources to gather more insights. Moreover, 40% of respondents said they still rely on instinct and subjectivity when making decisions. While gut feeling and industry experience should be a part of your decision-making process, without data and models to verify or challenge your assumptions, you’re taking a big risk with bigger operations budgets and revenue targets. Meanwhile, customer habits and demands are quickly evolving beyond a fundamental level. The proliferation of mobile and online environments are driving a paradigm shift to omnichannel banking in the financial sector and with it, an expectation for a customized but also digitized customer experience. Financial institutions have to be ready to respond to and anticipate these changes to not only gain new customers but also retain current customers. Moreover, you can bet that your competition is already thinking about how they can respond to this shift and better leverage their data and analytics for increased customer acquisition and engagement, share of wallet and overall reach. According to a recent Accenture study, 79% of enterprise executives agree that companies that fail to embrace big data will lose their competitive position and could face extinction. What are you doing to help solve the business problem around big data and stay competitive in your company?

 Organizations that can mobilize their data assets to power critical business initiatives will see a distinct advantage in the coming years. In fact, most C-level executives (87%) believe data has greatly disrupted their organization’s operations over the past 12 months. Here are more insights from the newly released 2018 global data management benchmark report: As digital transformation efforts proliferate and become commonplace, organizations will need to implement processes and technology that scale with the demands of data-driven business. Read the full report

Did you know that 80% of all data migrations fail? Like any large project, data migration relies heavily on many variables. Successful data migration depends on attention to detail, no matter how small. Here are 3 items essential to a successful data migration: Conduct a Pre-Migration Impact Assessment to identify the necessary people, processes and technology needed. Ensure accurate, high-quality data to better streamline the migration process and optimize system functionality. Assemble the right team, including an experienced leader and business users, to ensure timely and on-budget completion. 35% of organizations plan to migrate data this year.   If you’re among them, use this checklist to create the right plan, timeline, budget, and team for success.

At the end of July, the Consumer Financial Protection Bureau (CFPB) took a significant step toward reforming the regulatory framework for the debt collection and debt buying industry by announcing an outline of proposals under consideration.  The proposals will now be considered by a small business review panel before the CFPB announces a proposed rule for wider industry comment. The CFPB said its proposals will affect only third-party debt collectors pursuant to the Fair Debt Collection Practices Act (FDCPA).  However, the CFPB signaled it may consider a separate set of proposals for first-party collectors. The collections industry has long been a focus of the CFPB.  In 2012, the bureau designated larger market participants in the debt collections marketplace and placed some of these entities under supervision. In 2013, the CFPB released an Advanced Notice of Proposed Rulemaking covering collections. The focus on debt collection is fueled in part by the large number of consumer complaints it receives about the debt collection market (roughly 35% of total complaints).  Moreover, the CFPB’s proposals build upon some of the regulatory and enforcement priorities that the CFPB and Federal Trade Commission have pursued for several years around data quality, consumer communication and disclosures. Here are some of the key takeaways for third party debt collectors from the CFPB’s proposals: Address data quality: Collectors would be required to substantiate claims that a consumer owes a debt in order to begin a collection. Collectors would also be required to pass on information provided by consumers in the course of collections activity. New Validation Notice and Statement of Rights: The CFPB’s draft outline would update the information provided to consumers through the FDCPA validation notice, as well as require disclosure of a consumer statement of rights. Changes to frequency of communications:  Debt collectors would be limited to six emails, phone calls or mailings per week, including unanswered calls and voicemails. After reaching the consumer, the debt collector would be allowed either one contact or three attempted contacts per week. There would also be a waiting period of 30 days before contacting the family of a debtor who has died. New disclosures on “out of statute” debt and litigation: In the outline, CFPB proposes having debt collectors provide new disclosures to consumers regarding the possibility of litigation and whether the debt is beyond the statute of limitations. Waiting period before sending collection accounts to  a consumer reporting agency: Reporting a person’s debt would be prohibited under the draft outline unless the collector has first communicated directly with the consumer about the debt. The CFPB will next hear comments from a panel of small businesses in the industry, complete an analysis of how its proposals would impact small businesses, and take written comments from the public. Following those steps, the agency will issue a proposed rule for comment.  

The financial services industry continues to face mounting pressures to meet the highest standards of data reporting and accuracy. New regulations and mandates are introduced regularly, impacting the way companies do business. And a more credit-educated consumer base is seeking insights into their own credit data, providing a separate second of eyes that demand accuracy. Not only has the Fair Credit Reporting Act (FCRA) set requirements on dispute investigation and response, but the Consumer Financial Protection Bureau (CFPB) is also paying close attention. Recent announcements indicate the CFPB wants more information about the credit eco-system to gain more data about consumer disputes. According to the CFPB, it’s a joint problem – “the NCRAA’s, data furnishers, public record providers, and consumers all play roles which affect the accuracy of the information with credit reports.” And it’s not just the big banks that are being targeted with fines. The CFPB has made it clear it will also direct attention to certain nonbanks and financial products. In today’s data-driven environment, there are roughly 12,000-plus data furnishers, resulting in more than one billion pieces of information being updated on a monthly basis. Over 220 million consumers have some form of credit information attached to them, and transactional data is flowing all the time. Fail to update and a furnisher will quickly see flaws in their reporting. In fact, a recent study revealed an estimated 2.1% of contact info goes bad if unattended for more than one month. Clearly, achieving data quality is an ongoing investment for any organization, but companies often lack a clean plan. Some data furnishers fail to report, or elect to report to just one bureau, even though providing better data will result in a more complete and accurate credit profile. So how do you tackle the challenge of data quality? Organizations should consider implementing these six steps: Review data governance. Correct errors in data submissions. Complete an audit of data submissions. Evaluate disputes and resolutions. Compare data to peers and the industry. Review existing policies and processes. Follow these steps and your organization will earn a reputation among both regulators and consumers for clean, credible data. Plus, the investment in better data will reduce the need to resolve future disputes and fines. To learn more about meeting your FCRA responsibilities and best practices around data quality, check out our on-demand webinar or data integrity services site.

Data quality continues to be a challenge for many organizations.

Data quality continues to be a challenge for many organizations as they look to improve efficiency and customer interaction.