Experian and Moody’s Analytics have just released the Q1 2019 Main Street Report. The report brings deep insight into the overall financial well-being of the small-business landscape, as well as providing commentary on what certain trends mean for lenders and small businesses. In Q1 U.S. small businesses brushed off a government shutdown as stock markets recovered and income gains remained steady. Delinquency rates remained mostly stable, with pockets of weakness spread out among regions and industries, notably agriculture in the Great Lakes and manufacturing in the Southwest. Small firms seem to have simply shrugged off the headwinds of the first quarter and kept on with business as usual. Despite a fresh escalation in trade tensions,  the year is starting off well with positive news coming from the areas presenting risks to the outlook. A dovish stance on interest rates from the Federal Reserve and room to grow in our housing market — 2019 is off to a strong start. Watch Webinar Recording -  Q1 2019 Quarterly Business Credit Review Listen to the experts from Experian and Moody's Analytics go in-depth on insights revealed in the Q1 2019 Experian/Moody's Analytics Main Street Report.  

Serving commercial Property & Casualty insurers is a major objective of 3rd parties in the analytics and data space. This industry vertical is one in which standard credit tools already apply to the carrier’s challenge in managing claims risk; there is continued investment within and beyond the industry in developing innovative tools for this purpose. However, a smooth roll out of such tools at scale requires a comprehensive understanding of the regulatory process and its constraints. US Insurance industry- overall regulatory structure: Currently, US carriers are regulated primarily by the individual states, a result of the 1945 McCarran Ferguson Act (“MFA”). Less known is that the MFA was presaged by the Paul v Virginia decision (1869, later overturned by SCOTUS) that held that issuing an insurance policy was not a commercial transaction! [1]. Federal regulatory guidance, ultimately from the Office of the Controller of the Currency (OCC) and the Federal Reserve Board (FRB), is implemented via the National Association of Insurance Commissioners (“NAIC”; see below). NAIC organizes the insurance commissioners from all 50 states, Washington DC, and territories. NAIC maintains legislative databases, market conduct standards, industry financial reporting, conducts training, and many other functions. NAIC provides supervisory guidance for the use of models used to predict insurance loss risk. Among other functions, NAIC has created the Own Risk and Solvency Assessment (“ORSA”) framework which implements existing OCC and FRB guidance to the states. Capital reserves needed for solvency as well as business conduct -- including product definition and general business operations, licensing, maintaining a guaranty fund, underwriting, and rate setting-- are determined primarily by the states in which the carrier operates [2]. Today’s system of state-by-state regulation is more challenging than an equivalent centralized regulating body; insurance carriers operate increasingly online, driving the need for multi-state operations which in turn require multistate licensing and complex regulatory compliance. The average property liability firm has 16 state licenses, while the average life insurance carrier has 25. The coordination of state insurance laws, as well as many other quasi-governmental insurance industry functions, falls under the aegis of the NAIC. We will focus our discussion here on the regulation of risk models. How should third parties align the model building with regulatory requirements? Example 1: Basic filing and disclosure protocol: Responsibility to disclose to state regulators typically lies with the developer or the owner of the model. Disclosure responsibility for custom risk models built around the data of a specific client insurer resides with the insurer, while industry standard models used for multiple clients are typically disclosed by the model developer. Reporting and disclosure requirements vary by state. While the most central functions of interest by state regulators are underwriting and rate setting, any other use of models by insurers may be subject to regulatory disclosure. Models used to assess loss risk for rate setting or underwriting purposes are typically examined for discriminatory impact and use of prohibited data in addition to adequate risk performance and numerical stability. “Prohibited data” varies by state but may include certain data elements gleaned from in-state residents, federal crime data, certain credit data elements, traffic violations exceeding a specified age on the books, or other data; the section below deals with credit data. Finally, the requirement to disclose model details such as attributes and weightings also vary between states, and may require the developer to invoke trade secret status for the subject models to avoid disclosure to the public (implicit in many states). The adjudication of such claims is variable between states, as are all communications with regulators on this topic. Example 2: Use of consumer credit information to underwrite personal insurance policies: Using credit information in models to predict loss risk on personal insurance contracts also has a rich and extremely active history in the US. P&C insurers have generally found that credit risk and claims risk are positively correlated. They have used credit data on individual consumers to various degrees. Notably, the Consumer- Based Insurance Score (CBIS) employs consumer credit parameters and has been used across the insurance industry since 1993. Amid vigorous debate, states have seen active legislative attempts to restrict and define allowable use of consumer credit data by insurers. Credit information in some cases can outweigh a consumer’s driving record in setting rates- leading to the bitter but factual observation that excellent consumer credit can literally outweigh a DUI conviction in some states and conditions. In 2016 alone, the state legislative actions below were considered and/or enacted; note once again that the ability of individual states to regulate independently greatly complicates the picture for large carriers operating in multiple states:  California, Hawaii, and Massachusetts do not appear in the table above. In those states, consumer credit information cannot be used to underwrite personal auto policies. Example 3: Reporting channel: State regulators typically require use of the System for Electronic Rate and Form Filing (“SERFF”) database maintained by NAIC for formal submissions: https://login.serff.com/serff/ What’s coming down the road? We have seen examples of the dependence of applicable insurance regulations on individual state laws; the mechanics of model development requires understanding and working with these restrictions. Basic filing and disclosure, permissible model variables, the proprietary status of model detail, and the use of certain consumer information (e.g., credit scores, driving records) are all aspects of risk models whose successful execution depends on understanding the widely variable set of existing state regulations. Several authors have cited the need for a shift in the underlying regulatory structure of the industry from state-based to a national system, citing the inefficiency of the licensing process and the true interstate nature of today’s distribution system. A centralized federal insurance regulatory body would simplify interstate compliance by carriers, but would also introduce other complications. However, it appears prudent in the near-term for 3rd parties developing models to gain awareness of, and streamline, current requirements for regulatory compliance at the state level. Conclusion: There is a considerable additional value that the next generation of models will contribute to the commercial P&C vertical. Insurers and 3rd party developers have demonstrated the applicability of their models and data reports, offering competitive added value with standard risk scores adapted from the credit domain. However, promoting these products more broadly and expanding the product offerings themselves into cyber risk, commercial linkages, and various other tools for insurers, the insurance industry faces efficiency hurdles from our 50-state regulatory framework. With any regulatory centralization unlikely near term, 3rd parties thus need to gain working fluency in NAIC and in the SERFF database, anticipate state-level documentation and disclosure requirements, and attain a level of familiarity with state regulatory machines that enables the management of the interests of their clients with confidence. How Experian can help you Experian provides analytical services for Property & Casualty as well as other insurance product verticals. To enable you to assess claims risk at the time of policy application (or renewal), we either apply standard risk models or develop custom risk models to your underwriting and rate-setting processes. To help you guard against cyber fraud, false identity, and reputation risk, we offer specialty products as well. We also offer special purpose, custom analyses on request, and we sell curated commercial data to your standards as well. References: [1] Brookings Institute. paper on future of regulation- Grace & Klein [2] Insurance Information Institute: Regulation [3] Grant Thornton: ORSA requirements: Model Risk Management for Insurance Companies [4] Blueprint for a Modernized Financial Regulatory Structure, Dept. of Treas., 2008  

Today we are very proud to be taking the wrapper off the next generation of our flagship commercial credit management application, BusinessIQSM 2.0. To meet the ever-changing needs of our clients, we continue to grow and modernize with them.  This innovative and powerful analytical web-based application is designed for commercial enterprise and small-business risk management. From the new interface and side bar navigation to enhanced search and match technology, to judgmental and rules-based scorecards, all the way to custom model scores, Experian’s BusinessIQ 2.0 has something for everyone. Let Experian meet you where you are and take you to where you want to be. BusinessIQ 2.0 Overview In this video we highlight some of the key features of BusinessIQ 2.0. Learn more by going to:  

Experian has released the Experian/Moody's Analytics Main Street Report for Q4 2018. The report brings deep insight into the overall financial well-being of the small-business landscape, as well as providing commentary around what certain trends mean for credit grantors and the small-business community. Bucket 17Q4 18Q3 18Q4 Moderately Delinquent 31-90 1.68% 1.63% 1.68% Severely Delinquent 91+ 4.00% 3.40% 3.49% Bankruptcy BKC 0.16% 0.16% 0.16% The fourth quarter capped a second year of solid performance and growth for small-business credit, but there are signs that the period of moderation experienced during the past two years is over. Since the government shutdown has the potential to throw small-business lending a curve ball in the first half of 2019, the outlook for small-business credit is neutral. Conditions were positive in the fourth quarter, but this may not last long. Delinquency rates remained mostly stable, with pockets of weakness spread out among regions and industries, notably construction in the Plains. In addition to the 35-day shutdown, rising interest rates, destabilizing trade policy and slowing home-price growth are potential trouble sources that are already starting to impact some regions.  

I have been on the road meeting with clients at advisory events, forums, and industry thought leadership conferences, and what I continue to hear is a concern about the upcoming recession. The drivers of the next recession are up for debate but the consensus is that it is inevitable. The U.S. Economy is complex and the signals are mixed as to where the greatest impact will be felt. Protecting your business, whether consumer or commercial focused, is dependent on the stability and strength of your lending criteria and customer engagement practices. You want to protect your customers as well as your business in the case of a market stumble. You are laser-focused on making the best possible decision when reviewing credit applications and setting loan terms, however, financial situations change over time for both individuals and companies. This is especially true when a recession hits and unemployment begins to rise, consumers stop spending, and commercial delinquencies begin to rise. When these macroeconomic changes occur, the credit you have extended to your portfolio might be at under market stresses and at a stronger risk of nonpayment, and this can affect your business’s health and sustainability. By stress testing your portfolio, you can determine what may happen, when stresses are exerted, by a receding economy, on your portfolio. You can use credit information, macroeconomic data, and alternative data to build models that forecast what is likely to happen in the future and how stresses, will affect the ability for people or businesses to pay their bills. While larger regulated companies may be required to perform forecasting and stress testing, lenders of all size can benefit from the process. Gathering the Right Data for Accurate Stress Testing The accuracy of your stress test depends on the type and quality of data used for forecasting. Recessions are cyclical and likely to re-occur every few years, it is recommended that companies use historical data from the 2008 recession for analysis and to make accurate predictions. Young businesses may not have complete historical data going back to the 2008 recessionary time period. A partner like Experian can create look-alike business samples, from the vast holistic data, to simulate the likely impact of macroeconomic scenarios. For example, a financial services firm has been providing small business loans between $50,000 and $100,000 for the past three years and wants to predict future losses. To gather the data for loss forecasting, you need to create a business and product profile identifying loans or businesses with similar characteristics, to stress and forecast performance. These profiles are used to build a look-alike sample of businesses and loan products that look and perform like your current portfolio and will add the sample size and retro time periods needed to create a statistically viable analysis sample. Selecting a Forecasting Strategy Once you have the historic credit, macroeconomic, and alternative data on your portfolio or look-alike retro sample for modeling, you need to stress test the data. Most stress test analyses start with a vintage based analysis. This type of analysis looks at the performance of a portfolio across different time periods (Example: March 2007, March 2008, March 2009, etc..) to evaluate the change in performance and the level of impact environmental stresses have on the portfolio's performance. Once you have this high-level performance, you can extrapolate into the future performance of the portfolio and set capitalization strategies and lending policies. Identifying Loss Forecasting Outcomes Regulators and investors want to know the business is solvent and healthy. Loss forecasting demonstrates that your company is thoughtful in its business processes and planning for future stresses. For regional lenders that are not regulated as closely as large national or global lenders, forecasting shows investors that they are following the same rules as larger regulated lenders, which strengthens investor confidence. It also demonstrates effective management of capital adequacy and puts you on a level playing field with larger lenders. Companies with limited data can start with credit data for look-alike sample development and add historical data and alternative type data as they grow for a holistic portfolio view. Setting up Governance Business policies and macroeconomic stresses change over time, it’s essential to set up a governance schedule to review forecasting processes and documentation. Your stress testing and forecasting will not be accurate if you design it once and do not update it. Most companies use an annual schedule, but others review more frequency because of specific circumstances. Effectively Documenting Loss Forecasting The key element of loss forecasting is effectively documenting both sample and strategy taken in the evaluation of your portfolio. A scenario you might face is when a regulator looks at the analysis performed and you have selected sample data at the business level instead of the loan level, documentation should capture the explanation of why you made the decision and the understood impacts of that decision. While the goal is to have complete data, many companies do not have access to high-quality data. Instead of foregoing loss forecasting, the use of documentation to note the gaps and build a road-map for the data can be of great value. Here are additional key points to include in the documentation: • Data sources • Product names • Credit policies • Analysis strategy • Result summary • Road-map and governance schedule By creating a stress-test analysis strategy for forecasting loss, your company can make sure its portfolio and financial status remain as healthy tomorrow as they are today while maintaining transparency and investor confidence. The next recession is out there, this is a great time to strengthen processes for future successes.  

Today Experian and Moody's Analytics released the Q3 2018 Main Street Report. The report brings deep insight into the overall financial well-being of the small-business landscape, as well as providing commentary around what certain trends mean for credit grantors and the small-business community.  For Q3 2018, the overall outlook for small-business credit is positive, but some industries such as construction have a negative outlook. Delinquency rates stable for now Delinquency rates are stable around their current levels, but this could change quickly if risks mount for certain industries.  Continuing strength in the economy should keep small-business credit performance in check through the fourth quarter and early next year. Rising interest rates, destabilizing trade policy and slowing home-price growth are potential sources of trouble that are already starting to impact some regions. To bring insight to these Q3 business credit findings, Experian and Moody's Analytics will be presenting the Quarterly Business Credit Review for Q3 2018 on Tuesday, December 11th 10:00 a.m. (Pacific) 1:00 p.m. (Eastern).  Read the report and bring your questions, we will be opening up the session for live Q&A as we dig into the numbers and the outlook for Q4 2018. We hope to see you there. Presenters:  Gavin Harding Derrek G. McCrank Cristian deRitis      

It's International Fraud Awareness Week and Experian would like you to know how big the problem is for businesses. Here are some sobering facts, did you know? Every year 3.7 trillion dollars is lost to fraud? it would take the average person to spend 130 million dollars per day in their lifespan to cover that amount.     Fifty four percent of businesses are only "somewhat confident" in their ability to detect fraudulent activity. And that's compared to only 40 percent who are very confident.  52 percent of businesses have chosen to prioritize the user experience over detecting and mitigating fraud. Organizations worldwide lose an estimated 5 percent of their annual revenues to fraud, and an incident of fraud costs a company a median loss of $145,000. .  

Today we are celebrating Veterans Day in the United States. With deep gratitude for their service and the many sacrifices made for our country, Experian salutes Veterans across the country, and around the world, and we extend our warmest wishes for a Happy Veterans Day.   So, where are our Veterans?  Well, according to the U.S. Census many of our Veterans  are located in Montana, Wyoming, Virginia and Alaska. [Source: U.S. Census Bureau] Veterans Day originated as “Armistice Day” on Nov. 11, 1919, the first anniversary marking the end of World War I. Congress passed a resolution in 1926 for an annual observance, and Nov. 11 became a national holiday beginning in 1938. President Dwight D. Eisenhower signed legislation in 1954 to change the name to Veterans Day as a way to honor those who served in all American wars. The day honors military veterans with parades and speeches across the nation and a remembrance ceremony takes place at the Tomb of the Unknowns at Arlington National Cemetery in Arlington, Va. The ceremony honors and thanks all who served in the U.S. armed forces. Did You Know? 18.2 Million The number of military veterans in the United States in 2017. 1.6 Million The number of female veterans in the United States in 2017. 50.0% The percentage of veterans age 65 and older in 2017. At the other end of the age spectrum, 8.9 percent were younger than age 35. Veteran-owned Businesses in the U.S. In the US there are around two and a half million businesses that are majority-owned by veterans and growing. Veteran-owned firms have receipts in excess of a trillion plus dollars, and annual payroll of almost $200 billion. These are not insignificant figures as Veteran-owned small business owners drive to succeed in their business while supporting job growth. To perform well they need continued support and commercial credit can be an invaluable tool to an emerging business and its longevity and stability in the small business development.     Veteran Strength Experian commercial credit data on Veteran-owned businesses highlights remarkable similarity between veteran owned businesses and the industry overall in terms of sales, commercial credit quality, indebtedness, and credit utilization.   Stability Veteran-owned businesses typically employ more people, and show remarkable stability in terms of commercial risk and delinquency. Most Veteran-owned businesses are small and emerging. A positive trend in small business launches in recent years has resulted in a downward trend in the statistic below as more small businesses emerge in the 1-4 employee range and work to grow and survive in market. Avg. Owner Employee Size Infogram   Infogram Veterans who own businesses in the US are an average of 4 years older than their civilian counterparts, and are 10% more likely to own homes. The strength of the housing market in the past few years has given small business owners the ability to source equity from their homes to strengthen their small business and to thrive. Veterans are applying the tools, learning, work ethic and dedication learned from their military service to build stable businesses that support employment and the small business ecosystem.  We expect credit performance in the U.S. to remain strong in the short term but signals of approaching resets could change and impact the market in the short term. Veteran-led businesses will continue to charge forward and remain a vital part of our economy.    

Experian Business Information Services recently introduced a powerful new marketing platform called Business TargetIQ. Product Manager, Kelly DeBoer answered a few questions about the product and described use cases that promote greater collaboration between credit and marketing departments. What does Business TargetIQ do? Business TargetIQ is our new marketing platform so it's a B2B marketing platform where clients can access data for marketing applications. How is it different from other business marketing platforms? It is unique in that it not only includes your standard or core firmagraphic information but also includes Experian's credit attributes. Does it have credit data? What does that mean to marketing or collaboration? Typically marketing data and credit data are housed in separate silos of information. With this tool the information will be combined together which will allow the tool not only to be used in traditional marketing applications for targeting but can also be in that risk factor which applies to different divisions within our client's applications or use cases of the data. Who would most benefit from Business TargetIQ? The thing about Business TargetIQ is it truly applies to all different verticals, as well as all different contacts within the company. So whether it's a financial vertical or a trade vertical, retail, just across the board all clients can utilize this. Anybody that's doing marketing can utilize this platform. What core problems does Business TargetIQ solve? It solves a lot of different problems, so, the most common client issues that are brought to our attention are gaps in data, as well as in the marketing initiatives. So they may have data in-house but they have holes within the data. Our tool will allow them to not only upload their client records and fill in a lot of those gaps that they may have, whether it be contact information, or firmagraphics or address information. It will standardize that data and fill in those gaps. But will also provide the means to again use that data. Our business database which has over 16 million records. They can then utilize that information for prospecting, for data append, for analytics, for research applications, so it solves a lot of problems with regard to marketing and data concerns. How does credit data help with prospecting? So what we find is clients come to us and they may say you know I have an idea of what our clients look like, they're in this SIC or in this industry code, or they have this sales volume or employee size, but what they may not know is on the back end which really helps identify and target those businesses is the credit attributes, so the risk factors around those. So do they have delinquencies in their payments? Have they filed bankruptcies? Do they have UCC filings? So it allows them to take it that next step and not only really define what their clients look like, but identify clients that look like that. Learn More About Business TargetIQ