Data and Analytics

We sat down with John Krickus, Senior Product Manager for Experian's Scoring solutions to ask about the new Social Media Insight, and how this data and score are being used to help businesses strengthen scoring models, and create opportunities for emerging small businesses with limited credit history but strong social media profiles.  What is Social Media Insight? We're very excited to bring what we consider a breakthrough capability. Social Media Insight is an expansion, a use of new information beyond traditional credit data, that both improves the performance of our scores, provides attributes to our clients, and is directly sourced from the social media providers - no screen scraping. What type of social media data is used? So the social media data comes from sources that, like with our other data sources, we are not allowed to publicly disclose, but we are focused on business social media sites. So we're not going after consumer social media data, but social media data from business-oriented sites, so that we can better evaluate small and midsize businesses. What steps are taken to prevent artificially boosting the ratings of a business? So that's a question that we often receive about artificially boosting ratings. We work with social media companies, they have many techniques to identify where the reviews are coming from and to prevent someone gaming the system. Is it 100 percent full proof? No, but it is very effective. How effective has social media data been in predicting risk? As we've seen with using the data in our scoring solutions, we've seen a tremendous boost in score performance. There have been two real gains from using social media data. First is, we've developed about 70 social media attributes. We can now include those attributes and make these attributes available to our clients to improve the performance of risk scores. The second area really devolved from client feedback as we piloted the data. They indicated to us that there were social media data elements that are very helpful. So, we've been able to attain information such as pricing, parking situation, hours of operation, and those additional data elements have also helped our clients in improving their risk performance scores. What type of data do our clients get with Social Media Insight? We're able to provide our clients one of three types of data. First, we can provide a social media IntelliScore. That's our normal IntelliScore with commercial data, and now social media data. So, you get a higher performance. We've boosted performance by 37 percent with our IntelliScore. Second, we can provide those same social media attributes to our clients. So, they can incorporate those social media attributes into their scoring models. And third, we have the descriptive data. I mentioned hours of operation for example, social media data also provides a better description of the business. So, you just don't get an Exercise Gym, you get whether that gym is kickboxing or whether that's just exercise equipment. Can I target specific kinds of businesses? Can it be used for Marketing? We do have the ability to use social media data for identifying better businesses to do business with. Our initial focus though is in developing the attributes and the score for risk management. So that's really the focus for this first phase of Social Media Insight. How might Social Media Insight help an Insurance company? Insurance companies have been very anxious for this data, and they're getting a different view of the business. We're going beyond traditional trade, public record, background information, and now we're able to provide a view of how long has that business been rated? How are the ratings? What's the trend in the number of ratings? So, it's not just your level rating, but are you getting reviews over time? All that information provides a really unique view of the business that we've never seen before. How can Experian clients access Social Media Insight? Our clients can access the data one of two ways. We can provide a batch file. So, if you have a portfolio and you want to add social media attributes and a social media score to that you can provide us a file in batch. We also have our new API access, and we're very excited about that. So that allows you online real-time access to obtain the social media insight data, and it's very easy to program to. Can Social Media Insight help emerging businesses gain access to capital? Yeah that's an excellent area for Social Media Insight. It's really the newer smaller businesses that don't have a very deep credit history, that Social Media Insight now provides a view that previously it may have lacked information, therefore, credit may not have been extended. Now by having a social media site data available. We're adding depth of information to that business. We've actually found that businesses with social media data, as a group, are less risky than businesses without. How does Social Media Insight help improve risk model performance? So, the view that the social media data provides, we have found has boosted model performance, more than doubling model performance for those new emerging businesses. But even for established businesses we've seen double-digit gains in the measure of performance for models such as KS, and again you're getting a view of a business, number of reviews, and we normalized that. So, if you're in an area that's very active with social media data we take that into account. So, if you have 10 reviews in your history, which are in a very active area, you may not get as much of a positive as if you had 10 reviews and a less active social media area. So that combination really boosts performance and predictiveness of the data. How do Social Media Insight help our clients reduce risk? And really that's been one of the biggest breakthroughs with the social media data. Is by being able to boost the performance as I discussed earlier on KS, those new to the world businesses. They now are able to more confidently make decisions in their portfolio, because there is now a wealth of information. They're able to improve their models with this new additional information and have a very good performance improvement with it before and after. So, there's an across the board performance improvement. How does Social Media Insight help to automate the decisioning process? When you have an automated system, you want to have a higher level of confidence. The higher level of confidence you have, the stronger you can, for example, having automatic approve and automatic reject areas. By adding social media data we're able to get a stronger KS performance, which means you have more confidence in the models, and you can now increase the percentage your portfolios that you're putting through either automated or highly automated decision making. It's a significant boost in performance however you cut it. We're very excited about this unique new information source. Social media data is totally different from any other business credit information that's available, and when it's utilized in a model, in a decisioning system, the gain in performance are dramatic, and we're very excited to bring this capability to our clients. Learn about Social Media Insight  

Are the credit models you are using to make lending decisions more than 2 or 3 years old? If so, you are likely making less than optimal credit decisions. You may be turning down a customer who is a good risk — while taking on customers who are more apt to default on their obligations. Every year a model isn’t updated, its accuracy decreases. The economy changes. The consumer’s or business’s financial situation changes. Updating your models, using the most current data and attributes available, you can have confidence that you are making good credit decisions. To make the most accurate credit decisions possible, many businesses are now turning to data-driven decisioning models that are powered by artificial intelligence (AI) within machine learning engines. While the standard regression model works well in some industries, the lift in predictive value from using AI data models can be very important in other industries, such as retail, fraud and marketing. These models use sophisticated algorithms to predict the customer’s future ability to repay their obligation, which means a much more accurate decision than traditional models. Starting with High Quality Data   While data has always been at the core of credit decisions, models using machine learning are even more dependent on data. These models can be very accurate, but their accuracy depends on having the necessary data to understand what happened in the past and present behavior to make a prediction for what will happen in the future. The more data provided, the higher the accuracy of the decision. Here are three things to consider when building your data-driven decisioning model:   Clean Data – As innovation spurs business and technology to run faster and more efficient, the quality of the data underneath all of that innovation becomes even more important. Machine learning becomes smarter the more data it consumes. This means the accuracy of the credit decisions made by the model is largely dependent on the quality of the data provided. Data from third-party sources often contains mistakes, missing fields, and duplicate information, which results in less accurate credit decisions. Correct Data Points – The accuracy of the results depends on considering the right criteria in the form of data points in the model. When you use machine learning and AI algorithms, they can predict which specific data points will help increase the performance of the model for the specific customer and the specific type of credit decision. Often, data points that you may not consider are the ones that can make a big impact on the accuracy of the decision. Real-Time Data – In the past, there was often significant lag time between collecting and being able to use the data. By using real-time data with machine learning models, you can get a clear picture of the most current view possible and see changes in the different data points as they occur. This lets you make a much more accurate prediction of what will happen, with the consumer or business, than was previously possible with a traditional credit decisioning process. Using Alternative Data to Get the Full Picture Often, additional data — typically referred to as alternative data — that is not readily available from traditional data providers is used to enhance the accuracy and predictive ability of a model. While the model can seem complete without this information, the model may provide suboptimal results without it. Machine learning models can predict the situations and exact type of alternative data a model needs to produce an accurate decision. Experian offers a wide variety of alternative data that clients can use to improve decision models.   For example, a business owner may be taking out short-term loans to increase her cash flow, which makes her a much higher credit risk than she appears to be without this data. Weather information is also a common type of alternative data; a business located in Tornado Alley may need higher cash reserves to be a good credit risk. On the other hand, businesses located in an area impacted by a recent weather event, such as a hurricane, may be a good credit risk even with a lower score because both their business and local economy is recovering. Regularly Evaluating Your Data Model You must build in governance and make sure you are evaluating how the model is working on a regular basis, like having an annual checkup with your healthcare providers. Once you begin using a data model, you can’t simply set it and forget it. Ask the following questions to periodically evaluate your models:   Are there changes in the outcome of the models? You need to verify that your attributes are still predicting the same outcomes as intended, as well as capturing the same data. For example, say you have an attribute in your model that counts the number of credit lines open for a small business. If the attribute changes and those types of credit lines are no longer reported by the data provider, that number can go from three or four to zero, without there being a change in the number of credit lines open by the business. Because the data that goes into your model has changed, your model is not accurate unless you update the attribute. Is your model stable? You need to make sure that degradation hasn’t reached a point where the predictive value is no longer accurate. For example, scores before the 2008 recession have a different meaning than afterward, due to the changes in the financial system. The future of your business depends on making accurate credit decisions. Instead of using outdated models, use the latest technology and methods available by using machine learning data-driven models. It’s simple. It’s quick. And most importantly, data-driven models are accurate. Related articles: How To Modernize Decisioning with Automation and Real Time Respones Integrating Credit Decisions with the Back Office Improving Customer Experience Through Decisioning as a Service (DaaS)  

For utility companies, the customer onboarding process can sometimes be a complex, time-consuming, and unpleasant experience, especially if a manual credit decisioning process is in play. Every time a customer interacts with your utility company — be it via the website, telephone, in-person service call, mobile app, or social media — their experience sets the tone for the overarching relationship between the customer and your company. To improve the onboarding experience, many utilities are turning to machine learning to make faster and simpler credit decisions. However, creating a custom machine-learning decisioning engine is complex and can be costly. By leveraging machine-learning capabilities through Decisioning as a Service (DaaS) offerings, utilities can automate the decisioning process and create a frictionless customer experience. For example, a customer applying for service – even with a large utility — can be approved within seconds by an agent using a tablet. Using DaaS to Automate Your Credit Decisions DaaS can be used for many different types of decisioning — prescreening, prequalification, or instant credit. Utilities also use DaaS for authenticating and resolving identities and assisting with the rationalization of deposits.  Other uses include improving the customer experience, credit line management, retention, cross-selling and collections optimization. The process is like using traditional decisioning methods where the customer or customer service agent enters the customer’s information into the website or system. The utility system then connects with the DaaS engine through an Application Programming Interface (API), The DaaS engine then aggregates real-time data from the credit bureaus and other data providers about the customer, runs them through business rules — and a decision is rendered. The decisioning engine uses rules and algorithms to create predictive models for credit, fraud and, bankruptcy risk, profitability, retention and other key areas. The DaaS engine then automatically determines the results, such as approval, decline, instant pre-screen, cross-sell, or a collection decision. More than just providing a result, the system helps your utility know exactly how to treat the customer based on their risk level. DaaS also helps utilities by customizing offers that deliver both business value and value to the specific customer.  Additionally, the algorithms are continually updated so your decisions are always based on the current business climate, market drivers, and regulatory conditions. Creating a Seamless and Personalized Customer Experience From the customer’s perspective, the process is simple and seamless. Instead of waiting for hours — or possibly days — the customer receives an answer in real-time. If the decision is good news for the customer, they can move on with the process. And if the decision is not what the customer was hoping to hear, they can quickly move on and determine their next steps. There is no waiting, wondering, or anxiety. Using DaaS also creates a more personalized experience for the customer. DaaS recommends the next best step based on the customer’s specific situation, which is an informed way to begin your relationship and sets the right tone for the future. For example, DaaS may recommend no deposit based on risk-level, or a specific product or offer of value to the specific customer. Improving Operational Efficiency and Growing Revenue In addition to more loyal and satisfied customers, your utility company will see operational benefits and lower costs from using DaaS. Your employees no longer need to manually process credit applications and make decisions, thus eliminating paperwork, tasks, and time. Because DaaS provides centralized decision making, you can use the technology across different enterprise frameworks, call center environments, and processing systems. This dramatically increases your efficiency by eliminating manual processes which give your employees more time working to improve the customer experience. Because the process is more accurate than the traditional credit decisioning model, utilities can make better credit decisions. As a result, your utility can significantly reduce bad debt and fraud, which improves your overall financial health. At the same time, you can increase revenue by approving customers who are good credit risks but may have been denied using a manual or out-of-date decisioning model, and thus improve the customer experience. Additionally, your ability to more effectively cross-sell customers will help grow your revenue. Your utility’s success depends on its ability to make quick and accurate credit decisions while also providing a positive customer experience. By using an API to integrate DaaS into your systems, you can have confidence you are making accurate decisions while creating customer loyalty, improving revenue, and reducing costs. It’s simple, it’s easy and your customers will love it.    

The appetite for businesses incorporating big data is growing significantly as the data universe continues to expand at an astronomical rate. In fact, according to a recent Accenture study, 79% of enterprise executives agree that companies that do not embrace big data will lose their competitive position and could face extinction. Especially for financial institutions that capture and consume an incredible amount of data, the challenge becomes how to make sense of it. How can banks, credit unions, and other lenders use data to innovate? To gain a competitive advantage? This is where analytics sandboxes come in. A sandbox is an innovation playground and every data-consuming organizations’ dream come true. More specifically, it’s a platform where you can easily access and manipulate data, and build predictive models for all kinds of micro and macro-level scenarios. This sounds great, right? Unfortunately, even with the amount of data that surrounds financial services organizations, a surprising number of them aren’t playing in the sandbox today, but they need to be. Here’s why: Infinite actionable insights at your fingertips One of the main reasons lenders need a sandbox environment is because it allows you to analyze and model many decisioning scenarios simultaneously. Analysts can build multiple predictive models that address different aspects of business operations and conduct research and development projects to find answers that drive informed decisions for each case. It’s not uncommon to see a financial services organization use the sandbox to simultaneously: Analyze borrowing trends by type of business to develop prospecting strategies  Perform wallet-share and competitive insight analyses to benchmark their position against the market Validate business credit scores to improve risk mitigation strategies   Evaluate the propensity to repay and recover when designing collection strategies  A sandbox eliminates the need to wait on internal prioritization and funding to dictate which projects to focus on and when. It also enables businesses to stay nimble and run ad-hoc analyses on the fly to support immediate decisions. Speed to decision Data and the rapid pace of innovation make it possible for nimble companies to make fast, accurate decisions. For organizations that struggle with slow decision-making and speed to market, an analytics sandbox can be a game-changer. With all your data sources integrated and accessible via a single point, you won’t need to spend hours trying to break down the data silos for every project. In fact, when compared to the traditional archive data pull, a sandbox can help you get from business problem identification to strategy implementation up to 30% faster, as seen with Experian’s Analytical Sandbox: Cost-effective analytics Building your own internal data archive with effective business intelligence tools can be expensive, time-consuming, and resource-intensive. This leaves many smaller financial services at a disadvantage, but sandboxes are not just for big companies with big budgets. An alternative solution that many are starting to explore is remotely hosted sandboxes. Without having to invest in internal infrastructure, this means fast, data-driven decisions with little to no disruption to normal business, fast onboarding, and no overhead to maintain. For financial institutions capturing and consuming large amounts of data, having an analytical sandbox is a necessity. Not only can you build what you want, when you want to address all types of analyses, you’ll have the insights to support business decisions faster and cheaper too. They prove that effective and efficient problem solving IS possible! Ready to learn more about Experian's Analytical Sandbox and how it can help you optimize your business? Contact Commercial Data Science

When you’re launching a new product, business line, or starting up a business, you’ve got to move fast and break things.  This means taking a minimum viable product (MVP) approach, where you’ve got to sacrifice scalability by implementing manual processes to support the early-stage business.  Commonly, a manual process will be in place for credit applications and approvals – pulling the credit report, reviewing the data against a scorecard or policy, and then making the decision. Since this likely takes a day — or often longer — the process decreases your customer’s experience, and can hurt your ability to scale and grow revenue the longer you wait to automate. To grow the business and take it to the next level, you need to migrate away from the paper-pushing approach. The next step is to move toward an automated solution that integrates credit decisions with the back office, such as an ERP, CRM, or another custom system, employing APIs. Using an Application Programming Interface (API) to Connect to Your Decision Engine An API, or Application Programming Interface, is many things. It’s a set of instructions and technical documentation for developers. It’s a collection of services that allow you to interact with a product or service. And it’s a way for businesses to open up and allow for new kinds of innovation – allowing for new business models and application development that wouldn’t be possible without APIs. In the last decade, APIs have become system agnostic, meaning they plug-and-play into nearly any system because they are standardized and popular amongst the development community. Because of this popularity, APIs make it easier for the business to get buy-in from the IT department, which is essential to automating the credit decisioning process. Without an API, the IT department must devote significant resources to the project because more infrastructure to host large database will be required. APIs allow you to pull data in real-time only when you need it, reducing system complexity and decreasing application development costs.  Reduced complexity also means less risk because you are more assured that your IT department will be successful with the integration. Often, when IT departments are presented with information about the API, their response is “No problem, this is standard. We have integrated with a very similar API before. We can do this.” How does your decision engine interact with APIs? You can use APIs to get the raw data elements your credit policy or model needs to render a decision, no matter if the data is internal to your business or provided by third parties. Taking Decisions to the Next Level with Machine Learning According to a recent Harvard Business Review project, the key to successfully utilizing machine learning isn’t to get caught up in new and exotic algorithms but to make the deployment of machine learning easier.  There are many use cases where machine learning can be employed, but use cases where data-driven decisions are being made, as in the credit approval process, are archetypical. During the early stages of the machine learning process, you train the model by feeding it data from past applications. Then, as you use the engine for real-time processing, the engine learns from past decisions. If the engine was originally approving applications with a borderline credit score but found that these applications often ended up being poor risks, the model would then begin turning down these applications. The key ingredient in making machine learning start to work for your credit department is to have domain experts, credit managers, help the IT department focus on the key variables that can help the machine learning model to predict key outcomes – credit losses, bankruptcies, and business failures, and to put the models through many rounds of testing and validation before putting them into real-life practice. Now is the time to move your manual processes online using an API and machine learning. According to Mary Meeker’s Annual Internet Trend Report, 60 percent of customers pay digitally compared to 40 percent in the store.  And it’s likely that the gap will continue to grow. The longer you wait, the further ahead your competitors will be in digitizing the customer experience — and the harder it will be to regain your footing and catch up.

Data analysis surrounding lending practices for commercial lenders falls into 4 distinct buckets that define scope, usability, and purpose. In this post we will discuss how they differ in terms of value and complexity. Descriptive Analytics Descriptive analytics provide the current state of a commercial lender’s acquisitions, portfolio, or other parts of the lending lifecycle. This is “Reporting” in its simplest form. Defining and creating metrics can be as simple as a snapshot of the firmographic and performance elements of a portfolio to complex retro trends that define the effectiveness and success of a lender. Full coverage of the target market and accurate data play a big part in the success of this type of analysis. Selecting the wrong element, when creating a view, can lead the lender to a sub-optimal understanding of the state of their business. Looking at a competitor’s metrics can inform market share and pricing decisions. Experian commercial clients use Portfolio Benchmarking reports as an empirical view into the health of their business compared to their market peers. Adding data visualization on top of the descriptive-analytic reporting quickly closes the gap to a diagnosis. In the map below Texas, California, and Florida have higher rates of account opening and would be attractive target regions for acquisition.   Diagnostic Analytics A Diagnostic view of lending performance will look at the portfolio health of a lender and its peers and determine what are the key success and opportunity drivers within comparable products. Larger financial institutions have been performing this type of analysis for years. Several years ago, fintech lending hit its stride challenging the large commercial lenders by providing targeted products in niche lending spaces with little or no traditional commercial credit data. Large commercial lenders used benchmarking and market analysis to understand where the fintechs were being successful. Large lenders use of alternative data sources and market intelligence helped them to recognize the gaps in identifying and evaluating the risk of those underserved businesses. Fintech use diagnostic analysis, to their advantage, to make fast decisions and pivot to market demand. In the chart above, you can see that ABC bank is able to identify where they are offering higher credit limits than their competitors. This client had similar bad rates to its peers causing the lender to have higher losses due to improperly assigned credit limits. Predictive Analytics Predictive analytics can help to scope the effectiveness of a strategic decision and plan for the long-term impacts of credit decisions. Financial institution use this type of analysis to forecast loan performance and plan for impacts to cash flow as economic and market conditions change. Machine learning is used in predictive analysis to be nimbler in the evaluation of vast amounts of data to provide more accurate prediction of future outcomes. Large financial institutions will use Machine Learning in predicting response to an offer through the lifecycle to the collection of outstanding debt.   Prescriptive Analytics Predicting potential outcomes within a commercial lender’s strategies only gets them half way to a successful outcome. Providing insight on top of the analytic content is what drives the decisions to stay the course or pivot to an alternate course of action. Prescriptive analytics provides that direction. Machine learning can be used as a tool in prescriptive analytic engagements to develop models that can learn and pivot with changes to the market and behaviors of businesses within that market. Having the capability to adjust actions associated with outcomes allows the model to stay relevant and predictive over a longer period. As customer experience drives lending practices, commercial lenders look to use varying levels of analysis as stepping stones to better serve their small business clients. Got a question about analytics? We would be delighted to answer your questions. Commercial Data Science

  Scottsdale, Ariz., May 22, 2018 — Experian®, at its 37th annual Vision Conference, today announced it has become a certified vendor of the Small Business Financial Exchange, Inc. (SBFE), a nonprofit trade association that gathers and aggregates small-business payment data in the United States to help organizations build a complete picture of small business. “We’re excited to work with SBFE, which shares our mission to bring further innovation to the small-business credit landscape,” said Hiq Lee, president, Experian Business Information Services. “By combining the SBFE’s data richness with Experian’s vast consumer and commercial data assets and leading data science capabilities, we will use the power of data to help our clients make the right decisions.” As a SBFE Certified Vendor, Experian can combine its rich data — including traditional and alternative business data and consumer data on business owners — with SBFE’s data to provide the most comprehensive view of a small business in the market today. For example, financial institutions looking for broad and deep insights on small and emerging businesses will be able to find that information in a way no one has offered previously. Also for the first time, Experian clients that are nonfinancial institutions, such as e-commerce, communications, insurers, and software and hardware vendors, can qualify to access this financial data to help them make confident credit decisions by gaining deep visibility into a small business’s capital use and credit history through Experian. “Experian becoming an SBFE Certified Vendor makes perfect sense in support of our ongoing mission to serve our Members and the small-business community,” said Carolyn Hardin-Levine, CEO, SBFE. “Experian demonstrated its ability to meet SBFE’s high data security and governance requirements, controls, and independent oversight requirements. Additionally, Experian’s ability to deliver blended solutions combined with SBFE data will provide our Members with more options and drive innovation as part of SBFE’s single-feed, multicertified vendor model.” New product pipeline Experian fosters a culture of continuous innovation, from the way it works to the solutions it creates. The company plans to deploy its data scientists to apply leading-edge techniques, including machine learning and artificial intelligence, to discover and provide predictive insights and analytical tools to support better decisioning for its clients. It is anticipated that the work of its data scientists on the combined data sets will result in new product launches over the next 24 months. Vision Conference Each year, Vision combines in-depth research, cutting-edge technology and expertise from industry leaders to help Experian’s clients strengthen their balance sheets and plan for sustained growth. The 2018 conference sold out and runs May 20–23 in Scottsdale, Ariz. Contact: Jackie Brenne Experian Public Relations 1 714 830 5126 Jackie.Brenne@experian.com About Experian Experian is the world’s leading global information services company. During life’s big moments – from buying a home or a car, to sending a child to college, to growing a business by connecting with new customers – we empower consumers and our clients to manage their data with confidence. We help individuals to take financial control and access financial services, businesses to make smarter decisions and thrive, lenders to lend more responsibly, and organisations to prevent identity fraud and crime. We have 16,500 people operating across 39 countries and every day we’re investing in new technologies, talented people and innovation to help all our clients maximise every opportunity. We are listed on the London Stock Exchange (EXPN) and are a constituent of the FTSE 100 Index. Learn more at www.experianplc.com or visit our global content hub at our global news blog for the latest news and insights from the Group. About SBFE The Small Business Financial Exchange, Inc., and SBFE, LLC (collectively known as SBFE) is the country’s leading source of small-business credit information. Established in 2001, this nonprofit association’s database houses information on more than 32 million businesses and enables information exchange among members who provide small-business financing. Through its resources and relationships, SBFE makes possible innovative risk management solutions by providing industry insight and analysis of aggregated small-business financial data to its Members. SBFE is the only Member-controlled organization of its type and is serving as the most trusted advocate for the safe and secure growth of small business. For more information, visit www.sbfe.org.

Carl Stronach is the Senior Product Manager for Experian’s API Developer Hub. We sat down with him recently to discuss how API’s are helping business clients solve problems, and to get answers to some frequently asked questions about Experian Business API’s, how they work, and how developers can get started using them. What is the API Developer Hub and Why Did Experian Launch It? The Experian API Hub is a one stop shop for all Experian data. Whether client developer’s come into Experian to access our information on businesses, consumers, automotive data, data quality, they can find all our API’s in one place through one developer account. It makes it much easier for developers to come in find all the data assets that Experian has and start testing that data in just minutes. So clients are coming into the API hub and they’re coding to our API’s to integrate that data into their applications. And so one of the most exciting things that I find is when clients will share a screenshot with me of all our data that’s live in their application. Can you give us some examples of the kinds of apps clients have connected to the hub, do we know what data is being used for? We see clients using the API is for a variety of reasons. The first one would be your traditional credit underwriting.  They need to have a Credit Manager access Experian data within their own application so that they can make a credit decision. The API makes it easy to put all the data that a Credit Manager needs on one single screen. That’s what they are designed to do. And so that’s probably the first use case. The second use case that clients are coming into is when they’re running their own platform. Clients are coming in. They might be a fintech company. They might be a special niche provider of business information on a specific industry. So the API’s enable not only Experian to have a direct relationship with clients and end users, but also enable our own partners to give their end users access to Experian data within their applications as well. Does the Experian API Developer Hub open up opportunities for new business models? We’re enabling new types of clients to come into Experian and start working with our data, clients who would have never done that before. We’re opening the market to these types of developers to create new types of innovations. It spurs innovation. So yes, we think the API Hub enables new business models to be created. One of the ways we’re tackling that is by making the API’s available for startups making the API’s available for startup incubators. There’s all these little networks of startup incubators that allow all their startups to get access to different data assets, or different API’s to solve these business problems. Experian is getting into those incubators to allow our data to be at the fingertips of these startup environments where we can go a step further than just the public developer portal access that we provide to everyone. For some of these businesses that are in these incubators, we can provide production grade data to businesses who are just starting up. Normally they wouldn’t have the capital or funds to access our data in the production environment if they were to come to us directly, but as a part of these programs they can get started in creating a real application at a relatively low cost in those early phases of their business. What are the key benefits of using an API over other processes such as batch delivery? Many clients today might be using a batch process to automatically update their portfolio. There are certainly many benefits to using a batch process. It’s a bulk process. We offer very competitive pricing when we’re talking about batch delivery. The disadvantage is that the data isn’t real-time. Underwriting decisions are best supported by access to fresh data. That’s real-time, and that’s exactly what the APIs do. So when a client makes an API call to us we calculate data based on the data we have available at that time. We’ll pull data to create calculate a score on the fly, a fresh score. Every API that we have delivers data that we have in our database in real-time, so that real-time data that you can get at your fingertips when a client is completing an application, and you can use that data in real-time to make real-time decisions. So there are a few different ways. Where can people learn more about Experian API’s? The best way to find out about our API is to go directly to our API developer portal. The API developer portal is at developer.experian.com. If you visit, you’re able to go to the portal see the API’s that we have, read our API documentation. You can register for an account in just a few minutes and start making test API calls to start seeing how you might interact with Experian data and integrate that into your application. Other ways that we’re supporting clients interacting with our API’s – we are focused on not only say Credit Managers, and those traditional use cases that we’re working with, but we’re really focused on the client developer, and so we’re trying to make it as easy as possible for developers to code to our API’s. We’re doing things like creating libraries. We have some developer libraries that are out on GitHub right now. There’s a Node.JS library and a Java library that is available. We also can provide what’s called a Postman collection to our developers. Basically, it lets them download our API’s onto their computer and start making test API calls to any of our API’s in just a few minutes. It makes it super easy to start coding and interacting with our API’s. We’re trying to provide these open source ways for developers to start working with Experian data. And you know we’re looking for that client feedback. You know, what types of developer libraries can we create to make it easier for you to code to our API’s and integrate. We want to make API integration as easy as possible for some clients, so we’re going the extra mile to create open-source code that makes it really easy to interact with Experian API. Experian API Developer Portal

Andrea Schmalzer is an analytical consultant in the Commercial Data Sciences team here at Experian, and she just completed a study of the mining industry titled "Managing volatility: the unique credit risks of the mining industry", so we did a quick Q&A with Andrea about her research. Gary: Hi Andrea, why did Experian study the mining industry? Have there been any notable changes to this industry in recent years? Andrea: Hi Gary. We decided to study the mining industry in order to evaluate the impact the mining industry has on small businesses that are supporting that industry. Knowing that there will be some volatility as we see shifts in which fossil fuels are predominantly being used. As far as changes go, we have seen a decrease in coal production. Some of this is due to the fact that we had the big boom in natural gas which caused the natural gas prices to decrease substantially. And also just due to the fact that the coal supplies are decreasing overall. Gary: Can you tell us what states driving the bulk of the mining in the United States. Andrea Schmalzer: So Texas is the number one oil and gas producers in the country. As far as natural gas we also had that being mainly produced in Pennsylvania and Oklahoma. For oil, we do see that being produced in North Dakota and California, and as far as coal mining goes that comes from Wyoming, Kentucky and West Virginia. Gary: Andrea what impact has the decrease in natural gas and oil production had on the local small business economy? Andrea: So, North Dakota was one of the most impacted states when the prices of oil dropped because they decided to stop drilling because it wasn't cost effective. So we did see employment drop quite a bit in the oil and gas industry. But overall as a state North Dakota is still under a 3 percent unemployment rate. Some of those folks are now working in retail or grocery stores, gas stations in hopes that new drilling will come up again. For businesses though we do see delinquencies shrinking in the retail segment also in the manufacturing segment. However we do see charge off's increasing for both retail and manufacturing which is one area we need to keep an eye on. Gary: With the falling supply of coal in West Virginia what trends are you seeing in business credit and delinquency? Andrea: Like you said we've seen a large decrease in coal production in West Virginia, most notably in the southern counties of West Virginia. And with that we've seen an increase in unemployment and in some cases an increase in delinquencies. For example the retail and service industries have seen an uptick in delinquencies in southern West Virginia compared to the rest of the state. But we aren't seeing write off's come through yet. So an area to keep an eye on. Also overall in West Virginia we do see higher delinquency rates in the agricultural industry for the coal counties versus non-coal counties. But again, like retail and service we aren't seeing the write the off's come through yet. So definitely an area we need to keep an eye on. Download Whitepaper