Data Quality

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.

How can fintech companies ensure they’re one step ahead of fraudsters? Kathleen Peters discusses how fintechs can prepare for success in fraud prevention.

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

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

Many data furnishers are experiencing increases in dispute rates. It’s a tough spot to be in. Data furnishers are not only obligated under the FCRA to investigate and respond to all consumer disputes – reviewing every Automated Consumer Dispute Verification – but they must also do so within less than 30 days. As the number of disputes rise, resources become taxed and the risk of not meeting Fair Credit Reporting Act (FCRA) obligations increases. Let’s face it, consumer disputes aren’t going away, but understanding the reported data and metrics behind disputes can help data furnishers minimize them and defend reporting strategies and processes. 5 Way to Uncover Data Inaccuracy 1. Gain perspective against the industry and peers. Depending on the industry you service, the general benchmarks for dispute rates can vary. It’s important to understand where you fall in regards to dispute rates. Are you trending high or low? As an annualized average, we’ve recently experienced the following industry dispute rates through the end of the year: However, industry averages are just the tip of the iceberg. Measurement against peers can provide a clearer picture of where you fall. Are you an outlier or on par? How do you respond in comparison to peers? Are you deleting the trade as the result of the dispute at a higher rate? This could be an indicator of a systemic problem that needs addressing. 2. Implement pre-submission quality checks. Once you know where you stand, make sure your data is accurate before it heads out the door and hits the consumer’s credit report. Implement manual checks against Metro 2 rules. Build SQL queries to perform your checks. Better yet, use data validation software to automatically identify, track and remediate errors before sending the file to the bureaus. These steps can catch disputes before they happen. 3. Review any data being rejected after submission. Even if your new reporting motto is ‘know before it goes’; once the data has been transmitted, you’ll still want to monitor data being rejected due to Metro 2® errors. When data is rejected that means the update you provided did not make it to file. This leaves room for disputes. Incorporating a robust review of all rejected data in a timely and detailed manner, with updates made before the next reporting period, can improve the accuracy of your data. 4. Audit to identify and correct any stale data on file. An audit for any stale data  - which includes open accounts with a balance greater than zero that have not been updated recently - should be performed at least annually. Review, research and remediate any outdated data that could affect your customer, making it susceptible to a dispute. 5. Educate your customers. Why are your customers disputing? Are there common themes within your customer base? Often, a dispute can be eliminated before it happens, with some explanation on the way an account is reported. By providing proactive access to materials and resources that help demystify the credit reporting process, a potentially negative interaction can be turned into a positive learning opportunity, helping the overall customer experience. Learn more about data accuracy solutions.

 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

The data to create synthetic identities is available. And the marketplace to exchange and monetize that data is expanding rapidly. The fact that hundreds of millions of names, addresses, dates of birth, and Social Security numbers (SSNs) have been breached in the last year alone, provides an easy path for criminals to surgically target new combinations of data. Armed with an understanding of the actual associations of these personally identifiable information (PII) elements, fraudsters can better navigate the path to perpetrate identity theft, identity manipulation, or synthetic identity fraud schemes on a grand scale. Using information such as birth dates and addresses in combination with Social Security numbers, criminals can target new combinations of data to yield better results with lower risk of detection. Some examples of this would be: identity theft, existing account takeovers, or the deconstruction and reconstruction of those PII elements to better create effective synthetic identities. Experian has continued to evolve and innovate against fraud risks and attacks with an understanding of attack rates, vectors, and the shifting landscape in data availability and security. In doing so, we’ve historically operated under the assumption that all PII is already compromised in some way or is easily done so. Because of this, we employ a layered approach, providing a more holistic view of an identity and the devices that are used over time by that identity. Relying solely on PII to validate and verify an identity is simply unwise and ineffective in this era of data compromise. We strive to continuously cultivate the broadest and most in-depth set of traditional, innovative and alternative data assets available. To do this, we must enable the integration of diverse identity attributes and intelligence to balance risk, while maintaining a positive customer experience. It’s been quite some time since the use of basic PII verification alone has been predictive of identity risk or confidence.  Instead, validation and verification is founded in the ongoing definition and association of identities, the devices commonly used by those individuals, and the historical trends in their behavior. Download our newest White Paper, Synthetic Identities: Getting real with customers, for an in-depth Experian perspective on this increasingly significant fraud risk.

In 2017, 81 percent of U.S. Americans have a social media profile, representing a five percent growth compared to the previous year. Pick your poison. Facebook. Instagram. Twitter. Snapchat. LinkedIn. The list goes on, and it is clear social media is used by all. Grandma and grandpa are hooked, and tweens are begging for accounts. Factor in the amount of data being generated by our social media obsession – one report claims Americans are using 2,675,700 GB of Internet data per minute – and it makes some lenders wonder if social media insights can be used to assess credit risk. Can banks, credit unions and online lenders look at social media profiles when making a loan decision and garner intel to help them make a credit decision? After all, in some circles, people believe a person’s character is just as important as their income and assets when making a lending decision. Certainly, some businesses are seeing value in collecting social media insights for marketing purposes. An individual’s interests, likes and click-throughs reveal a lot about their lifestyle and potential brand linkages. But credit decisions are different. In fact, there are two key concerns when considering social media data as it pertains to financial decisions. There is that little rule called the Equal Credit Opportunity Act, which states credit must be extended to all creditworthy applicants regardless of race, religion, gender, marital status, age and other personal characteristics. A quick scan of any Facebook profile can reveal these things, and more. Credit applications do not ask for these specific details for this very reason. Social media data can also be manipulated. One can “like” financial articles, participate in educational quizzes and represent themselves as if they are financially responsible. Social media can be gamed. On the flip side, a consumer can’t manipulate their payment history. There is no question that data is essential for all aspects of the financial services industry, but when it comes to making credit decisions on a consumer, FCRA data trumps everything. In the consumer’s best interest, it is essential that credit data be both displayable and disputable. The right data must be used. For lenders, their primary goal is to assess a consumer’s stability, ability and willingness to pay. Today, social media can’t address those needs. It’s not to say that social media data can’t be used in the future, but financial institutions are still grappling with how it can be predictive of credit behavior over time. In the meantime, other sources of data are being evaluated. Everything from including on-time utility and rental payments, insights on smaller dollar loans and various credit attributes can help to provide a more holistic view of today’s credit consumer. There is no question social media data will continue to grow exponentially. But in the world of credit decisioning, the “like” button cannot be given quite yet.

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.

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.

Historical data that illustrates lower credit card use and increases in payments is key to finding consumers whose credit trajectory is improving. But positive changes in consumer behavior—especially if it happens slowly over time—don’t necessarily impact a consumer’s credit score. And many lenders are missing out on capturing new business by failing to take a closer look. It’s easy to categorize consumers by their credit score alone, but you owe it to your bottom line to investigate further: Are the consumer’s overall payments increasing? Is his credit card utilization decreasing? Are the overall card balances remaining consistent or declining? Could the consumer be within your credit score guidelines within a month or two? And most importantly, could a competitor acquire the consumer a month or two after you declined him? Identifying new customers who previously used credit responsibly is relatively easy since they typically have rich credit profiles that may include a mortgage and numerous types of credit accounts. But how do you evaluate consumers who may look identical? Trended data and attributes provide insight into whether a consumer is headed in the right direction:   With more than 613 trended attributes available for real-time decisioning and for batch campaigns, Experian trends key factors that provide the insight needed for lenders to lend deeper without sacrificing credit quality. Looking at trended data and attributes is critical for portfolio growth, and credit line increases based on good credit behavior is a must for lenders for two reasons. First, you’ve already spent the money acquiring the consumer and you should not waste the opportunity to maximize returns. Second, competition is fierce; another lender could reward the consumer for great credit behavior they’ve displayed with your company. Be there first, be consistent, or be left behind. Use Experian’s Payment Stress Attributes and Short-term Utilization Attributes in custom scores or swap-set strategies in order to find quality customers who may be worthy of line increases or other attribute credit terms.  Look to trended data to swap in consumers who may fall within a few points under your credit score guidelines, and reward your existing customers before another lender does. Near-prime consumers of today are the prime consumers of tomorrow.

Data is the cornerstone of retail success today. Yet only 39% of retailers trust their data when making important business decisions.  Your organization — whether retail or not — can start depending on your data and gain actionable insights with these data management tips: Put the right people in place. Get the tools you need. Enrich your data. Collect accurate customer information Arranging for the right people, tools and processes to maintain accurate information helps you stay on top of your data now and lets you leverage that data to stay ahead of the curve. Learn more tips>

The consumer economy has evolved dramatically over the past few years — in large part due to technology and access to large amounts of data. Credit data, especially, can be a powerful asset for financial institutions in this new environment. More than 88 million U.S. consumers use their smartphone to do some form of banking. 67% of consumers made purchases across multiple channels in the last six months. With the help of data scientists, financial institutions can build models that crunch huge volumes of data and append their own customer data to drive portfolio management, customer acquisition and collections decisions across digital and mobile channels. Learn more>

As we kick off the new year, let’s take a look at some interesting things we learned about data quality in 2016. Our latest data quality report found some concerning statistics about companies and their data quality: 56% of organizations report losing sales opportunities due to bad data. 79% say data clearly ties directly to business objectives, but only 2% trust their data completely. 83% report that poor data quality impacts their business initiatives. Data is at the heart of your organization, and the quality of that data underpins the success of many of your business initiatives. Implementing a successful data quality program, therefore, is imperative to your organization’s future. Building a business case for data quality

Businesses believe that 23% of their customer or prospect data is inaccurate. Since 84% of companies have a loyalty or customer engagement program in place, poor data is a costly issue.  The unfortunate reality is that 74% of companies have encountered problems with these programs — and 12% of revenue is believed to be wasted as a result. Is your loyalty program suffering from poor data? There is a cure. Think of data quality as preventative medicine for a costly and entirely avoidable illness. >>Learn more