Data and Analytics

New Experian Report: Beyond the Trends Experian Business Information Services is excited to present our new quarterly report "Beyond the Trends." In this report, we'll be evaluating challenges to particular industries. We'll be looking at account management, pre-treatment, and treatment strategies for small businesses coming out of COVID. The Winter edition has just been released, download your copy below. . Download Report

We get a lot of questions from our customers about blended credit scores vs consumer scores so, for our latest Fast FUNdamentals session, I thought it would be helpful to share what makes blended scores so powerful, and something you should consider when granting credit to small businesses.  Watch our video or read the post, and remember to share it with your friends and colleagues.   Small business growth fuels the U.S. economy through job creation and innovation! Small businesses support regional and local economies throughout the country, with higher proportions in middle America, and for every Walmart, Amazon, and Google, there are thousands of small business manufacturers, distributors, resellers, and app developers supporting their growth as well. In order to grow, small businesses need good ideas, a good business plan, hard work, and access to capital.  Good credit is required to access capital, and many creditors look at the business owner’s consumer credit information from bureaus like Experian, to determine creditworthiness and credit risk.  They tend to see small businesses and small-business owners as one and the same.  However, this strategy is not always successful. The average consumer profile is quite different from the average business owner. Let’s look at how the profiles compare on some general consumer attributes associated with credit risk. The average owner has more trade experiences and has a longer credit history, which indicate lower risk. However, the owner also has higher credit utilization, which commonly indicate higher risk.  Does this mean that the average owner is a higher credit risk than the average consumer?  Not necessarily.  Many small business owners rely to an extent on their personal credit to help finance their business. For public records, such as tax liens, judgments, and bankruptcies, the average is low for both the average consumer and owner. For collections, the average consumer has 5 times the amount to that of the owner.  That’s indicative of higher risk. Another major indicator of credit risk is severe payment delinquency, and again, the average consumer is much more likely to be more severely delinquent than the average owner. Given that the average business owner looks and behaves differently from the average consumer, is there a better way to assess small business credit risk? A better way to assess small business risk Experian conducted a study to look at the relationship between the business and the owner’s credit behaviors over 3.5 years, to determine the strength of that relationship. We tracked the percentage of those businesses and owners that continued to remain credit active and healthy, and those that became high credit risks, becoming 91+ days delinquent on over 35% of all trade obligations. Over 3.5 years, 79% of the time, both the business and owner’s credit remained healthy. We tracked the percentage of those businesses and owners that continued to remain credit active and healthy, and those that became high credit risks, becoming 91+ days delinquent on over 35% of all trade obligations. Over 3.5 years, 79% of the time, both the business and owner’s credit remained healthy, and 5% of the time, both the business and owner became severely delinquent on the business obligations and on the personal credit obligations. That means that 84% of the time, the end result of business and consumer credit is the same! That’s a very strong correlation, so the owner’s consumer credit behavior is very indicative of business credit behavior. But, that also means that 16% of the time, the outcome of the business and consumer are diametrically opposed. 9% of the time, the business goes bad, but the owner stays good.  If a creditor were to approve a business for credit based on the owner’s credit profile, the creditor would have made a bad decision. Furthermore, 7% of the time, the consumer goes bad but the business stays good. Blended risk scores provide better commercial risk assessment Blended risk scores predict business risk by utilizing the owners’ consumer credit attributes with the business credit attributes together – to calculate a more comprehensive risk score for the business With the blended risk score, creditors can more confidently approve those with a great score and know that they will have a profitable customer. And they may have to decline those with a high-risk score to mitigate against future loss. Let’s compare the predictive power of three risk models as the business ages, from infancy to full maturity In the below illustration, the horizontal axis going across measures the business as it ages, from 0 to 2 years on left, to 21+ years old on the right. The risk models compared are the Blended model, Commercial only model and Consumer model. Again, the Blended model uses both the business and consumer credit information to calculate the business risk. The Commercial only model uses just the business credit data to predict business risk and the Consumer model uses consumer credit data to assess the risk of the consumer, which is the business owner. The vertical axis measures KS, which is a metric representing the predictive power of each model in accurately identifying future good vs bad business credit outcomes. The KS is scaled from 0 to 100, with 0 indicating no predictive power and 100 Indicating perfect prediction.  So, higher values indicate stronger model performance. For businesses in infancy up to 2 years, the Consumer risk score is more predictive of business risk than the Commercial risk score. That may seem counter-intuitive, but the underlying reason is that new and young businesses do not have a lot of credit activity on their profile.  Many young businesses are actually funded by the owner’s personal credit, so there is less business credit information to calculate the business score.  And in general, the less information available, the less predictive a model score will be. As the business matures, it establishes and expands its credit profile, opening more tradelines in the name of the business. As the business profile becomes richer in information, the commercial risk becomes more predictive than the consumer score. The business becomes a separate entity, and the consumer score becomes less and less indicative of business risk.  Behaviors can differ dramatically from the business owner, as we have seen in previous examples. As expected, across all the ages of the business, the blended score provides superior performance. The blended score takes the age of the business into consideration as one of the factors for calculating risk, at every stage of the business lifecycle, the blended score provides a more holistic risk of the business by integrating the dynamic relationship between the business and owner credit profiles. Small business owners represent a unique market.  They have an evolved sense of purpose, discipline, and responsibility that allows them to accept the risks and hardships required to build an enterprise from the ground up.  To properly evaluate an entrepreneur’s credit risk, creditors must look at the right score.  Utilizing a blended score is a proven, better way for creditors to evaluate risk and extend worthy businesses the capital they need to grow and prosper. And, as small businesses succeed, we all benefit.    

Credit risk scores predict credit risk in the near future, based on the credit profile of the business as of today. So you have a new applicant. What do you do? You get that credit risk score for those applicants with a great score; you're going to approve them and hope they'll be good customers for life. For those applicants with a high-risk score, you may have to decline them. This is the way it's supposed to work, but how do you know if the risk score works for your portfolio? What is the risk associated with the score specifically for you? To understand the risk of the score for your applicants, you can start scoring all new applicants as of today and wait 3, 6, 12 months. But who has the time to wait a year to see if the score predicts good versus bad outcomes accurately? Watch our 5 Minute FUNdamentals Video   A more immediate way is to score the newly booked accounts from a year ago and compare the score at application with the performance up to today. This process is called model validation. It's possible because Experian archives a snapshot of all business credit profiles and scores monthly going back more than a decade. Model validation results are represented through a performance table or odds chart. Let's go over a simple model validation odds chart. The risk score is scaled from 1 to 5, with 5 indicating the lowest risk and 1 indicating the highest risk. Younger businesses, businesses with minimal credit experience, or companies with severe delinquencies or collections would score low. This table shows the number of accounts that got each risk score. These columns show the number of accounts that say good or went bad within the first 12 months of account opening at the point of application. This is the bad rate for each score by knowing what the risk of the score is for your portfolio. You can understand the risk of new applicants going forward. A common metric used to determine a risk score's predictive power is KS named for its craters, Kolmogorof and Smirnoff. KS measures a score's ability to separate two populations. In this case, future goods and future bads. If more bads get lower scores and more goods get high scores, then the model is doing an excellent job of predicting credit risk. Let's quantify how good the model is performing by calculating KS. We see that 20 accounts got the worst score - a 1, and 12 of these accounts stayed good within one year of opening, and eight of these accounts went bad within one year of opening. Now let's add some columns that calculate values from the worst score on up. These are called the cumulative calculations. At the worst score of 1 there are eight bads captured out of 20 total bads, which is 40% of all bad accounts. At the score of 1 to 2 there are 14 bads captured out of 20 total bads, which is 70% of all bad accounts. At the score of 1 to 3 there are 17 bads captured out of 20 total beds, which is 85% of all bad accounts. Finally, at score of 1 to 5, there are 20 bads captured out of 20 total bads, which is 100% of all bad accounts. We go through the same calculations for the percentage of good captured. Let's calculate that KS now. At each score range, we subtract the percentage of goods captured from the percentage of bads captured. The KS is just the maximum difference between the percentage of bads captured and the percentage of goods captured. The KS is scaled from 0 to 100 with 0 indicating no ability to predict good versus bad outcomes, and 100 indicating perfect prediction. Let's see a model that can not predict credit risk at all. This model captures the same percentage of goods and bads at each score. So the maximum KS is zero. Now let's see how a model can get to a KS of 100. There it is, all bads got the worst score of 1 and no goods. All right, now that we understand how well the risk score predicts risk, let's discuss how we can apply your odds charts, make data-driven decisions. Let's say on average, you make $100 dollars on every excellent account, but you lose $200 for every bad. For each score we calculate the net profit by multiplying the number of goods by profit per good. Net loss by multiplying the number of bads by loss per bed. Now let's calculate the cumulative net profit and loss from best to worst score. We're simply summing the net profit and net loss amount as we go from a score of 5 down to 1. Lastly, we subtract the cumulative net loss from net profit at each score to determine the score cut.  To maximize profit for all the accounts that score a 5, we're making a profit of $1,700. As we add lower-scoring accounts, our maximum profit continues to increase. When we add the accounts at score 1, the maximum profit decreases by $400. This means that we maximize profit by approving everyone that scores 2 and higher. For those that score 1, there are 20 accounts, and we're losing $400 from them. We can choose to decline them or charge them a deposit of $20 or more to be profitable because we're losing $20 on average per account here.  

The concept of machine learning has been around for 50+ years in analytic circles. But machine learning methods have created a stir in the last few years as their popularity and visibility increased in the U.S. consumer and commercial credit industry. The use of these advanced methodologies has been constrained to mainly fraud/identity and collections. Machine Learning techniques are now available for credit decisioning. Our upcoming Sip and Solve session will provide insights to help your regulator feel more comfortable with the methodology you are using. We will share how Experian is making machine learning explainable to regulators and boosting model performance. During this session you will learn three take-aways: Current model governance basics How machine learning methods are boosting performance Best practices in deployment and documentation to help regulators feel comfortable with this more powerful solution

Matt Shubert, Experian's Director of Data Science and Modeling participated in a discussion about trends in AI and Machine Learning. He shared insights on how Experian Business Information Services is leveraging these technologies for clients. Matt and a panel of industry experts discuss how businesses are taking advantage of predictive analytics technology to gain a competitive edge in the marketplace. Webinar Highlights: - Use cases that show how AI and machine learning are helping companies be more proactive than ever - How predictive modeling can lead to more informed business decisions - What steps organizations can take to adopt an AI-enhanced analytics strategy that works for them - And more! Panelists: Puravee Bhattacharya, Senior Data Scientist and Analytics, BI & Performance Reporting at Energia Nirupam Srivastava, Vice President - Strategy and AI at Hero Enterprise Matt Shubert, Director of Data Science and Modeling at Experian

Experian® today announced Ascend Commercial Suite™ for financial institutions specializing in commercial lending as well as insurance carriers to drive growth while reducing risk. The suite includes Experian’s Ascend Analytical Sandbox™ configurations and a new Ascend Commercial Benchmarking Dashboard™ that provides access to industry-leading data on small and midsize businesses. “Experian is committed to creating opportunities for businesses to succeed,” said Hiq Lee, president of Experian’s Business Information Services. “During uncertain times, making fast, accurate decisions is critical for lenders so they can continue to extend credit responsibly to the businesses that need it most. Experian’s Ascend Commercial Suite enables clients to access world-class advanced analytics, AI, machine learning, and benchmarking tools so they can make real-time decisions that can ultimately help businesses on the road to recovery ahead.” Experian’s Ascend Analytical Sandbox is an industry-leading cloud-based data and analytics solution that offers flexibility in addressing lenders’ needs and offers instant access to up to 19 years of data. The secure hybrid-cloud environment allows users to combine their own data sets with Experian’s exclusive data assets, including consumer credit, commercial credit, nontraditional, auto, and more. Small and midsize business lenders, as well as insurance carriers, can seamlessly blend commercial and consumer small business data to get a 360-degree view of their overall small business portfolio to more easily identify risks and opportunities. It’s a one-stop-shop for insights, model development, and results measurement. The Ascend Commercial Benchmarking Dashboard delivers a comprehensive visual dashboard view of credit risk data and Small Business Financial Exchange™ (SBFE) Data exclusively for SBFE members. Clients can compare their portfolios against industry performance and analyze new market segments for potential growth and expansion. The insights available through the Ascend Commercial Suite can be viewed and shared through interactive dashboards and customizable reports. Additional use cases include: Portfolio performance and monitoring: Lenders can harness the power of Experian data to better monitor performance and quickly identify areas of strength or concern on visual dashboards without having to run custom reports every month. Model development and validation: Clients can monitor existing models and develop new models in order to improve risk profiles of new accounts and improve existing accounts. Blended analysis: Small business lenders relying on personal guarantees can use both consumer and business data to determine a customer or potential customer’s overall risk. Marketing analytics and acquisition: Lenders’ campaign information and results combined with Experian’s Credit Risk Database help them understand performance and improve marketing and segmentation. Decisioning for risk assessment and segmentation: Lenders and insurance carriers can optimize risk decisioning and segmentation strategies using analytical tools on one platform, which provides quick and efficient access to multiple integrated data sets. Reject inferencing: Lenders can load application data and use SBFE trade-level data to understand how declines performed if customers obtained credit elsewhere. Custom attributes to better analyze portfolios: With SBFE Data, lenders can create their own custom attributes or use Experian’s highly predictive set of attributes. Experian’s Ascend Commercial Suite is built on the Experian Ascend Technology Platform™. Launched in 2017, the Experian Ascend Technology Platform is recognized as one of the most successful launches in Experian’s history. It’s currently being used by the top financial institutions globally including the United Kingdom, South Africa, Brazil and Asia Pacific. Experian’s Ascend Analytical Sandbox was also selected in 2019 as the winner of the “Best Overall Analytics Platform” award by FinTech Breakthrough, an independent organization that recognizes the top companies, technologies and products in the global fintech market. To learn more about Experian’s Ascend Commercial Suite, please visit: https://www.experian.com/business-information/ascend-commercial-suite.

For the past month, the Commercial Data Sciences team in Business Information Services has been taking precautions in response to the Coronavirus Pandemic, working from home. In the span of the past five weeks, we have seen the spread of the disease ramp up, and the death toll climbs. The impact on businesses of all sizes will be immense. In just a few days we built a robust simulator tool that helps businesses assess the impact of COVID-19 as the disease spreads. With this tool, you can: Identify risk in geographies you do business in Based on geography, review the top 5 riskiest industries for that region Apply an impact scenario so you can plan for the best and worst-case scenarios The U.S. business risk dashboard below was developed by Experian Business Information Services to help businesses better understand the impact COVID-19 may have on their commercial operation based on several key factors. This methodology combines business risk, anticipated impact on business industries and real-time COVID-19 case data to help businesses better simulate various impact scenarios down to the state level to help develop enterprise strategies. A paid version of the dashboard goes down to the county and industry level. The risk index is used as a comparative benchmark across states, counties and industries. Industry classification is used to assess the business’s level of exposure due the nature of the business. For example, businesses in the Arts, Entertainment, and Recreation industries will be more heavily impacted than businesses in Public Administration. The risk index represents the credit risk, industry risk, and COVID-19 risk on businesses across the U.S. The impact layer allows users to easily change the severity of the impact related to the combination with the credit risk, industry risk, and COVID-19 risk across regions and industries. This dashboard is meant to be a directional tool for assessing which industries and geographies are most likely to be impacted and how severe the impact will be. The risk index is not designed to be interchangeable with a traditional credit risk score. The risk index is intended to be used independently to gain insights around the potential impact of the current events on future business credit health at summarized levels including region and industry. The risk index has four different assessment scenarios ranging from low to severe. If the expectation is that various industries are affected differently, but the impact overall is minimal, then the minimal scenario should be applied. Select the other scenarios to amplify the impact.  

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  

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.