In its continued efforts to tame inflation, the Federal Reserve increased interest rates ¼ point last week, the tenth consecutive increase in just over a year. The cumulative increase is 500bps since March 2022, bringing the Fed Funds rate to 5.00%-5.25%, which is the highest since 2007. While inflation is still above the Fed’s target rate of 2%, they indicated a pause in rate increases. The labor market continues to be strong with April unemployment down to 3.4%, matching the low of January which is the lowest unemployment since 1969. Despite all the efforts by the Fed to have a soft landing, the economy could be upended if Congress does not increase the debt ceiling soon. With inflation slowing, and the labor market strong, a soft landing is possible. Treasury Secretary Yellen said the U.S. could default on debt as early as June 1st. If the U.S. defaults on outstanding debt, many forecast disastrous impacts to the world economy. Despite the recent decline in residential construction spending, construction spend remains strong in both residential and non-residential sectors. The construction industry is one of the few industries that saw a boom throughout the pandemic. Even though over the past few months both residential and non-residential experienced a decline in construction starts and construction spend, the volumes remain above pre-pandemic levels. High construction demand is being met with the formation of many new construction companies. New construction companies are seeking credit at a higher rate, but delinquencies in the construction industry are increasing. Higher risk and higher interest rates are causing commercial lending to tighten, and construction companies are seeing fewer loan originations and smaller loans/lines of credit. What I am watching: The non-residential construction industry is expected to see steady growth in 2023 due to project backlogs but could slow in 2024. Due to higher mortgage rates, the residential construction industry is expected to see a significant decline in housing starts through 2023 with the sector stabilizing in 2024. Aside from the immediate key drivers of interest rates and cost of capital, other areas of focus will be on the labor force and the demand for skilled vs. non-skilled labor. The number of skilled workers is decreasing yet the demand for skilled labor is increasing. The construction industry will have to attract the necessary talent to support the growth. Operational changes in the construction industry will be a driving factor. The construction industry is seeing a shift toward technology in all aspects of construction. Utilization of robotics is increasing which could replace portions of the workforce. Smart Cities, Smart Homes, Green Building are all trending which will materially change construction projects. The Construction Industry is experiencing a noticeable shift and companies will continue to adapt to keep up with demand.

Improve customer experience while boosting profitability through automation As a credit manager or risk manager, you need real-time data to determine the right credit terms for each customer. Does the company pay its bills on time? Is it growing, or is it in danger of going out of business? What debts are outstanding? It’s easy to see how granting credit to the wrong customer can harm your business but limiting credit to financially solvent customers also limits the growth of your own company. We're going to talk about automating credit decisions in this post and some action steps you can take toward greater efficiency and profitability. The importance of credit decisions is especially urgent now as we enter a bear market following some significant turbulence in the regional banking space. The decisions companies make when times are good determine their ability to stay in business through the next recession. Your business simply can’t succeed unless you consistently make smart credit decisions. And the secret to making the right credit decisions efficiently starts with modernizing your credit approval process. While it’s easy to think about credit decisions with new customers, the process actually occurs throughout the customer buying life cycle — prospecting, acquisition origination, account management and collections. When you have an inefficient manual process for credit decisions, you lose a significant amount of productivity and time. If you take up to 30 days to make a credit decision, there’s a good chance your potential customer will turn into your competitor’s customer. Additionally, manual credit decisions, by nature, exist in a silo and can’t be used by other departments and processes, which adds to the inefficiency. Companies can leverage the latest in data science and technology by moving to highly automated data-driven decisions. In addition to making more accurate decisions, you can improve efficiency significantly by making faster decisions that allow you to turn prospects into customers more quickly. With a modern credit decision engine, you can also share data with other vendors, departments and systems through application programming interface (API) integrations. But true efficiency is about more than making a single accurate decision. It’s also about creating a credit policy using data models and then automating that policy to increase both accuracy and efficiency. You can then consistently apply the data science to each decision and each application in a way that’s repeatable. Because each company has its own risk tolerance, credit policy and credit approach, your model must be specifically customized to be most effective. This article provides the road map you need to modernize your credit decision process. By understanding the steps involved in creating an automated process, you can begin to evolve your credit decisioning into an efficient process that increases revenue and productivity. How to automate credit decisions and real-time responses When a new customer wants to establish credit terms with you, the first thing they’re asked to do is to fill out your credit application. When you hand over a paper application, did you know you could be negatively impacting your revenue or providing a poor customer experience? Some companies don’t. More than likely, your customer has filled out at least one digital application in the past.  When you use paper, your customers may perceive your company as out of step with technology, which can lead your customers to wonder where else you’re lagging behind. Digital applications provide a simplicity factor, and when you don’t offer one, your credit approval process is more difficult for customers, leaving them with more work to do — wasting time handwriting their information and then returning the application by email, by fax or in person. Because many companies have already moved to a digital application, your pen-and-paper process sticks out to customers — and not in a good way. And manually processing a paper application takes longer — often much longer — than a digital application. Customers leave without a credit approval, giving them time to change their mind about their purchase or find a better deal, which means you just lost a new sale. Even if they still choose to work with you, their relationship with your company starts out with a mediocre customer experience. After the paper application is completed, the workflow process is often time-consuming, error-prone and cumbersome, and the time involved means that your company waits longer to get the revenue from the sale. By using a manual process, your team spends hours on processing and decisions that could be better spent servicing customers directly or working on other initiatives to grow your business. Putting the credit application online for laptop, tablet, or smartphone use As customer experience becomes more critical to how businesses compete and differentiate themselves in the marketplace, companies inevitably will continue to move toward digital applications. Some companies include a link to their online credit application on their website so customers can fill it out from anywhere, anytime they want. Other companies operating physical storefronts will give customers a tablet so they can fill out the digital application right then and there. Companies with sales representatives who go into the field can also use the system for credit approvals by simply handing their tablet or smartphone over to the customer. When sales reps have the power to easily convert sales on the spot, you can increase revenue and improve the customer experience at the same time. And your sales reps’ satisfaction improves, because you’re giving them tools to help drive their success. Automating the decisioning process Some companies improve the application process further by automating credit approvals. Real-time decisions give customers something they often expect — instant gratification. With a digital application, you can extend credit and issue payment terms that work for your customer, which prevents them from shopping around. If a potential customer leaves your site or store without a credit decision, you could easily lose their interest, and they might never come back. When your customer completes the application and clicks Submit, the automated decisioning process can initiate instantly. Because the application is digital, the system can render a decision immediately, display it onscreen and send the applicant a confirmation email. Your employees no longer have to look at each application by hand — just those flagged by the system as needing a human touch. The decision is based on rules and parameters you set up in the system’s decisioning engine to determine credit approval. Because different companies have different levels of risk — some are more risk-averse, while others are more aggressive — you can set up a decision policy based on the specific criteria that work for your company. This also means less friction between the credit department and sales, because the credit policy is already preconfigured with clearly defined criteria. This can help sales representatives better manage expectations with their customers. Improving the workflow Your company likely already has an established workflow process for credit applications. Many automated application processing and decisioning technologies are designed so you can leverage certain components and integrate them into your process as you see fit. This enables added flexibility and customization when moving your existing process to an online format. Experian® provides several standard workflows for customers to use as is. You can also quickly modify an existing workflow or, potentially, use API integrations to build automation into your existing processes and systems. Modernizing the end-to-end credit approval process can feel daunting in the beginning. Start by identifying specific credit department processes that can be easily automated right away and that can drive quick, impactful returns. By starting small, you can implement and manage change more easily over time. As your business and stakeholders become more comfortable, you can continue to automate more pieces of the process to drive incremental returns. Eventually, your company could automate the entire credit approval process. No more paper applications, and no more frustrated customers. Integrating credit decisions with the back office When you’re launching a new product or business line or starting a new business, you need to move fast and break things. This means taking a minimum viable product (MVP) approach, where you 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 can take a day or even longer, the process damages your customer’s experience, and it 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 move away from the paper-pushing approach. The next step is moving toward an automated solution that integrates credit decisions with the back office, such as an Enterprise Resource Planning (ERP), a Customer Relationship Management (CRM) or another custom system that employs APIs. An application programming interface, or API, is many things. It’s a set of instructions and technical documentation for developers. It’s a collection of services that let you 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’re standardized and popular among the development community. Every API that we have is delivering data that we have in real time. You can use that data to make decisions in real time.Carl Stronach, Experian 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, more infrastructure will be required to host a large database, which means the IT department will need to devote significant resources to the project. 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’re 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.” 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, whether 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 using machine learning successfully isn’t getting caught up in new and exotic algorithms, but making 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 predict key outcomes like credit losses, bankruptcies and business failures and put the models through many rounds of testing and validation before putting them into practice in real life. Now is the time to move your manual processes online using an API and machine learning. According to Mary Meeker’s 2018 InternetTrends report, 60 percent of customers pay online, while only 40 percent pay in-store. And that gap likely will continue to grow. The longeryou wait, the further ahead your competitors will be in digitizing the customer experience — and the harder it will be to regain yourfooting and catch up. Now is the time to move your manual processes online using an API and machine learning. Why data models matter more than ever Are the credit models you’re using to make lending decisions more than 2 or 3 years old? If so, you’re likely making less-then-optimal credit decisions. You may be turning down customers who are 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. When you update your models using the most current data and attributes available, you can be confident that you’re making good credit decisions. To make the most accurate credit decisions possible, many businesses are turning to data-driven decisioning models powered by artificial intelligence (AI) within machine learning engines. Commercial Data Science Consulting 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 customers’ future ability to repay their obligation, which means a much more accurate decision than traditional models. Watch our 15-minute Sip and Solve talk Modernizing your credit approval process can feel daunting in the beginning stages, but it doesn't have to be. By starting small with some basic automation principles and tips, you can begin to implement change more easily over time in order to drive incremental returns for your business. As you and your stakeholders become more comfortable with these changes and see the tangible benefits, you can continue automating more pieces of the process to drive even more value. Watch Recording Starting with high-quality data Data has always been at the core of credit decisions, but 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 both what happened in the past and present behavior to make a prediction for what will happen in the future. The more data provided, the more accurate the decision will be. Three things to consider when building your data-driven decisioning model: Clean data— As innovation spurs business and technology to run faster and be more efficient, the quality of the underlying data becomes even more important. Machine learning becomes smarter as it consumes more data, which means the accuracy of the model’s credit decisions is largely dependent on the quality of the data provided. Data from third-party sources often contains mistakes, missing fields and duplicate information, which can result 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. Machine learning and AI algorithms can predict which specific data points will help increase the model’s performance for the specific customer and the specific type of credit decision. Often, you may fail to consider data points 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 data and being able to use it. 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 you could with a traditional credit decisioning process. Using alternative data to get the full picture Often, additional data — typically referred to as alternative data — that isn’t readily available from traditional data providers is used to enhance a model’s accuracy and predictive ability. The model may seem complete, but without this information, it can provide suboptimal results. 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 clients can use to improve decision models. For example, a business owner taking out short-term loans to increase her cash flow is 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 the local economy are recovering. Regularly evaluating your data model You must build in governance and make sure you’re evaluating how the model is working on a regular basis, like having an annual checkup with your healthcare providers. Once you start using a data model, you can’t just set it and forget it. Ask the following questions to periodically evaluate your models: Are there changes in the model’s outcome? You need to verify that your attributes are still predicting the intended outcomes, as well as capturing the same data. Say your model has an attribute 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 any change in the number of the business’s open credit lines. Because the data that goes into your model has changed, it won’t be accurate until you update the attribute. Is your model stable? You need to make sure degradation hasn’t reached a point where the predictive value is no longer accurate. For example, scores before and after the 2008 recession have a different meaning due to the changes in the financial system. The future of your business depends on your ability to make accurate credit decisions. Instead of using outdated models, take advantage of the latest technology and methods available by using machine learning data-driven models. The process is simple and quick and most importantly, data-driven models are accurate. Tying it all together Modernizing the end-to-end credit approval process can feel daunting in the beginning stages. But finding a service provider that offers flexible solutions and guidance to help kick-start the process can better position you for success. Experian’s decisioning services are positioned to provide maximum flexibility to our clients. We will summarize how each Experian decisioning solution can help you, depending on how far along in automating your credit approval process. DecisionIQSM — Get started with speed and simplicity For companies that are new to modernizing the credit approval process or have a limited technology budget, starting with a simple, web-hosted decisioning tool like DecisionIQ can be a good option. Basic access lets clients build policies leveraging scorecards, exception rules and credit limits prebuilt by Experian. The DecisionIQ simple policy wizard can get you up and running in minutes, and it’s ideal for companies looking for an off-the-shelf starting point for making faster, more reliable credit decisions. DecisionIQSM Plus — Add customization and precision for improved risk management Companies that have more experience in automated decisioning or operate in a complex business model likely are looking for specific customizations to help drive incremental returns within their risk management process. DecisionIQ Plus is designed to help clients facilitate blended scorecards so they can assess risk on small businesses in the United States quickly and easily. This becomes particularly important when these small-business customers lack a commercial credit footprint or support their business operation using the owner’s personal credit. DecisionIQ Plus can also be leveraged to build commercial scorecards on international businesses. Given the continued growth of international commerce, these data assets prove essential for many clients expanding globally. DecisionIQSM Premier — Expedite customer onboarding and improve the customer experience For companies with the most advanced automated decisioning that are moving from improving the risk management function to deploying a streamlined onboarding experience, DecisionIQ Premier is designed to help companies build and deploy online credit applications in real-time. Once activated, these online credit applications can capture, route, and store an applicant’s submitted information within the DecisionIQ-hosted workflow. Companies can also configure the online credit application to sync with their automated decision policy, making it easy to render instant onscreen approvals and email communications at the point of sale. By linking an online credit application with a live credit policy, DecisionIQ Premier can help improve a company’s speed to revenue and its ability to compete in the marketplace. Learn more about Experian Decisioning Solutions

The U.S. economy continues to be stronger than expected, even as a looming downturn is still expected. Inflation remains persistent with March prices 5% higher than a year ago, but slowing from 6% inflation in February. Rent inflation continued to increase in March to 8.3%, the highest in over 40 years. Food inflation declined for the seventh consecutive month to 8.5% in March, down from 9.5% in February. The cost of energy in the U.S. in March was 6.4% lower than a year ago, the first decline since January 2021. One of the biggest drivers of inflation over the past year was energy, but in March energy was 6.4% lower than a year ago, the first decline since January 2021. The labor market continues to be tight with low unemployment still driving wages higher, but inflation makes real wages stagnant. With these mixed signals, it will be interesting to see if the Federal Reserve continues to increase interest rates, or if they pause rate hikes at their May 3rd meeting. New businesses are opening at a high pace, with the 2022 monthly average 44% higher than the pre-pandemic 2019 monthly average. Those newer and smaller businesses are seeking a greater portion of commercial credit and have accounted for a larger portion of new commercial accounts opened. Both consumer and commercial delinquencies are trending upward since 2021 lows. Delinquencies are rising in the newer and smaller business segments and may be the first to feel the brunt of tightening credit criteria. What I am watching As delinquency begins to rise, lenders are tightening underwriting policies. Businesses will find it harder to obtain capital and may turn to alternative funding sources besides traditional banks. Alternative lenders generally charge higher interest rates, and in a rising interest rate environment, they are getting even higher, so businesses will be hard-pressed to find affordable funding sources. Commercial bankruptcies, which were at historical lows the past year, started to increase in Q4 2022 and are likely to continue to increase, especially if businesses in need of capital struggle to obtain it. Download your copy of Experian's Commercial Pulse Report today. Better yet, subscribe so you'll always know when the latest Pulse Report comes out. Subscribe Today

In this episode of Experian Business Chat, I sit down with Sr. Product Manager Thy Phan to discuss the portfolio risk challenges faced by risk managers during uncertain economic times and how Experian's Ascend Commercial Suite can help them. Ascend offers clients a comprehensive view of data, historical trends, and industry insights, enabling them to make informed decisions about credit policies and strategies. With the platform's data-agnostic approach, clients can better target their marketing efforts, identify growth opportunities, and adapt their product offerings to suit the changing economic landscape. What follows is a lightly edited transcript of our interview. Gary Stockton: Well, hello, and welcome to Experian Business Chat, where we talk about commercial risk and how Experian data and analytics solutions solve a variety of problems. I'm Gary Stockton, and today I'm here with Thy Phan, Product Manager in Business Information Services, for our Ascend solutions. Can you talk about the challenges you are seeing for risk managers in the economy? Thy Phan: Yeah, absolutely, Gary. So with the clients that we've mainly been working with, three common challenges come to mind when we talk about challenges that credit managers face. The first is data, the ability to obtain data, and the ability to link data. There are first-party data sets in-house, which can be dispersed across different platforms and different environments. So it's difficult for them to extract the data and pull it out in an environment where they can then link it all together. Secondly, our clients rely on many different data partners, such as Experian, for credit and other alternative data. So the challenge that the client's face is doing all the prep work to process the data in a way that they can start analyzing and looking at the data. And it's difficult for them to really link the data sets together to get a more comprehensive view of their customer. The second challenge that we often see is the lack of analytical environments or software and tools that their teams can use to develop models, for example, run analytics quickly and then deploy into production. For those clients that actually do have those tools and platforms available, the resources are very limited. So often, what we see is they're fighting for resources across different projects, which makes it difficult or slow to roll out new things. And then, thirdly, the reporting aspect of it. It's difficult for clients to produce reports on a regular cadence that can inform business decisions on the fly, on demand. So those are the three main challenges that we often see with our clients today. What does Experian have to help manage these challenges, and how can clients use data to understand what their next step is during a recession? Thy Phan: Here at Experian, we have a product suite called the Ascend Commercial Suite. It is an analytical platform that is cloud-based and Experian hosted. The value for the client is that Experian provides all of the data upfront. So all of our historical credit data, alternative data, we prep and load all the data into the platform ahead of time for the client. The platform is also data-agnostic, which means that we can load any data set onto the platform for the client, including first and other third-party data. Experian would do the heavy lifting to match and link all of the data for the client, so they can leverage Experian's premier linking logic, and matching logic to get a comprehensive view, a consistent view across the board, and everything prepped and ready to go. All they have to do is log into the environment, and they can start their work. Also, there's a reporting capability as part of the platform. So they can pull reports anytime anybody can go in there, pull a report, and be able to get those insights in terms of the trends and shifts, and it has a co-authoring capability. So more than one resource can go in and work on the same project at the same time, so it can be dispersed across the business and shared so that things can be done on a more regular cadence and at a quicker pace. How can Ascend help risk managers be more confident in their credit policies when times are uncertain? Thy Phan: There are three ways that Ascend really helps. Because all of the data is in there and clients are getting a very consistent and comprehensive view of the different data sets of their customers, several insights can be observed. The first one is different risk factors. Are there different risk factors that really impact their portfolio? Gary Stockton: Wow, that's incredibly powerful. And particularly for risk managers who are probably right now trying to figure out if they're in a good position if they're strong enough, and if they've got the right level of risk in their portfolio. Thy Phan: Absolutely. And one other thing that I will mention that's very powerful is the fact that they can start predicting where the economy is going to be based on some of the industry trends; for example, if we're looking at late-stage delinquencies, maybe those delinquencies haven't shown up in their portfolio yet, but because they have the industry view, they can see that maybe late-stage delinquencies are starting to pick up in the industry. And so they can get a view of the changes in the macroeconomic situations potentially that they may not be able to see just by looking at their portfolio. If we're looking at late-stage delinquencies, maybe those delinquencies haven't shown up in their portfolio yet, but because they have the industry view, they can see that maybe late-stage delinquencies are starting to pick up in the industry. Thy Phan, Experian Can you tell us what you are seeing as far as new capabilities and strategies that are leading to success for our clients? Thy Phan: Yes. The first example is at the top of the funnel. You have to be able to do more with less, especially when the economy turns and budgets are going to be tightened. So some of the strategies that our clients are deploying are having the data available, they can load in their performance data, and identifying profiles of their ideal customers, not only from a marketing perspective but also from a credit perspective with a limited budget. They don't want to market to just everybody; they want to market to customers that are more likely to buy and more likely to qualify. Secondly, the ability to see growth opportunities, like white space analysis or market penetration. Are their customers mainly located in a specific geography or particular industry? Are there opportunities to grow outside of those core areas? With the economy shifting, it's critical for clients to find more revenue streams elsewhere. And thirdly, potential shifts in product offerings. For example, maybe previously, when the economy was doing well, one product may have made sense and been more valuable to customers, and now with the potential shift, the client may need to shift to a different product or a different go-to-market strategy. The data can help inform some of that decision-making. Gary Stockton: Well, Thy, thank you for sharing your perspectives and insights with us. If you would like to learn more about Ascend, we invite you to watch our benchmarking demo, where analytics consultant Emily Garrett shares use cases found in Experian's Commercial Ascend Benchmarking Dashboard. Learn more about Ascend Commercial Suite

Lenders are experiencing an increase in delinquencies and are therefore tightening credit criteria. According to a survey of loan officers, underwriting standards are becoming particularly more stringent on commercial loans. Moreover, the recent news of the SVB collapse has also highlighted the vulnerability of small banks and served us as a reminder of their crucial role in serving local communities. During the days immediately after the SVB failure, we saw a sizable shift in deposits from small banks to larger institutions. A liquidity crunch affecting small banks puts their lending capacity at risk and could develop into a credit crunch in the communities served by them. Furthermore, as mobile banking becomes more prevalent and friction on fund transfers is minimized, financial institutions must work harder to retain their deposit customers. In all, the financial sector has shown remarkable resilience weathering the recent challenges, and small and medium-sized banks seem to have successfully covered their liquidity gap through borrowing from the Fed. Moving forward, it is essential for small and mid-sized financial institutions to reassure their customer of their stability and to prepare for additional interest rate hikes. Large institutions should focus on streamlining their acquisition processes to capitalize on a potential influx of new customers, and to implement fraud detection systems to identify bad actors while minimizing disruption to legitimate applicants. What I'm Watching Interest Rates: The Fed has been sending mixed signals lately, stating that it remains committed to fighting inflation (which implies additional rate hikes) yet signaling a willingness to slow down or even pause the rate increases in light of recent events and the pressure that rate increases put on banks’ deposits. Small and Medium-Sized Banks’ Financial Health: Smaller institutions seem to have successfully managed recent challenges. Keep an eye out for further signs of stabilization, and the impact of additional rate increase on unrealized losses and liquidity. Regulatory Environment: In reaction to the SVB and Signature Bank failures, it will be interesting to see if regulators issue new requirements for banks.

If the current economic cycle has you craving more insight into the small business sector you have come to the right place. We are delighted to release the Spring 2023 Beyond The Trends report. This release finds credit markets remaining largely open across risk tiers, but small business lenders will be more sensitive to market factors such as labor, wages, inflation, global supply chain disruptions, sanction activity, and rising delinquency trends as small businesses look for growth as consumer spending although strong, begins to moderate. Here are a few highlights contained in the latest report: Inflation-adjusted income has risen for seven-consecutive months Consumers spend as income acceleration continues. This spending increase does not mean that consumers are getting more value or products for the purchase volume. It also does not mean that spending is hitting all sectors of the market. Retail spending slowed in February for department stores, restaurants, and bars. Consumers continue to spend on vacations, up 17% (Bureau of Economic Analysis), and luxury goods through the first part of the new year. This behavioral volatility in spending may have retailers reconsidering their strategy for the remainder of the race. Costs are still elevated, and that cuts into the buying power of the average consumer. Inflation appears to have peaked, but moderation has been slower than expected. Consumers may see some relief as price acceleration eases, fuel costs decline, and food inflation cools. However, service industry inflation and shelter costs are expected to remain elevated. Factory and manufacturing velocity follow consumer demand As retail-focused supply chains return to pre-pandemic efficiency and shipping container costs decline, retail spending slowed in February. Retailers are cautious in pre-order inventory volume for 2023 due to concerns about economic instability. Download Spring 2023 Beyond The Trends Report

Account takeover fraud is a lucrative type of identity theft in which online account information or login credentials are stolen and used for nefarious means. When fraudsters gain access to an account, they manipulate things like passwords and usernames to prevent the rightful account owner from receiving notifications so they can make withdrawals, submit fraudulent payments, or open new accounts using the compromised credentials. A 2021 Javelin study1 reported huge increases in account takeover fraud, with losses increasing 90% from the prior year. With limited resources to devote to cybersecurity, small and midsize businesses are at a higher risk for account takeover. Small business account takeover, also known as “corporate account takeover”, represents a significant and ongoing threat to both businesses themselves and the lending institutions who service with them. In this article, we are going to review how corporate account takeover manifests in various businesses who are applying for credit and explain how risk professionals can leverage automated fraud detection software to improve review processes, streamline their lending services, and perhaps most importantly protect their reputations. What is Corporate Account Takeover Fraud and how does it happen? Account takeover is an insidious type of cybercrime in which fraudsters or hackers gain access to online accounts and use them to withdraw money, make purchases, or extract information. Their goal is either to use that information to gain access to associated accounts or sell it on the dark web to increase damage and their potential profit. Account takeover schemes can happen right under the business or business owner’s nose, and the results vary. Some fraudsters are looking for instant gratification and a big payout while some play a longer game, accessing accounts via weak passwords, malware, or email phishing schemes and selling sensitive information on the dark web to other cybercriminals. 62% of businesses experienced an increase in fraud losses due to account opening and account takeover. Source2 Small and midsize businesses are particularly vulnerable to account takeover schemes as they often have limited resources to devote to cybersecurity, or weaker security measures in place compared to larger corporations. The 2021 Identity Fraud Study by Javelin Strategy & Research found that the number of identity fraud victims in the US increased by 113% between 2019 and 2020, with small businesses experiencing a higher rate of fraud than larger businesses3. Here are a few examples of account takeover schemes in small business lending: When an account takeover attempt has been successful, there is an increase in suspicious activity like changes in usernames, passwords, and addresses, or unauthorized bank account activity or transfers. It is also common for fraudsters to use the newly stolen information to try and open new lines of credit, all before the business or business owner is aware there has been a breach. According to the Better Business Bureau (BBB), business email compromise affects organizations big and small, and has resulted in more losses than any other type of fraud in the U.S. with 80% of organizations receiving at least one email in a scam attempt4. How does Account Takeover impact lending services? Despite uncertain economic circumstances, small and midsize businesses continue to press on and evolve. According to Experian’s 2023 Beyond the Trends Report, SMBs make up 99.9% of all businesses in the U.S. and new business applications continue to rise5. But weaker security measures and limited resources mean SMBs are at a higher risk for account takeover fraud. This ultimately impacts lending institutions, who may unknowingly release funds to a compromised business account. Aite research shows that 64% of financial institutions are seeing higher rates of Account Takeover Fraud attacks now than prior to the pandemic. Source6 So how does this work in an SMB environment? A fraudster who has successfully obtained the account information of a small business, small business owner, or personal guarantor can bypass legacy security protocols to appear legitimate. That fraudster can then commit various harmful acts, like apply for lines of credit, open new accounts, and make transfers. For more insight into how these account takeover attacks play out, consider the realistic scenarios below: It’s important to note that most, if not all, lending services providers experience some degree of fraud loss, and competing business priorities no doubt play a role in the adoption of fraud prevention technology. Budgetary restrictions, high turnover rates, and technological expertise and limitations all play a role, but without modern fraud solutions in place, lenders run the risk of experiencing more than just financial losses. They risk losing confidential or proprietary information, encountering legal liabilities, and perhaps most importantly damage to their reputation. So, the question is, what kind of risk are you willing to take? Account takeover schemes, though pervasive, are just one type of fraud attack. The reality is fraudsters continue to evolve and become more sophisticated all the time, and to stay competitive lenders should consider implementing a comprehensive fraud strategy that will arm them against unnecessary losses. If you are a financial institution coming to terms with growing fraud rates, below are some questions you should consider asking. Questions to ask when formulating your fraud mitigation strategy: What kind of fraud losses are you currently experiencing and what impact are they having on your business? Are you able to accurately assign your fraud losses? Do you have a fraud prevention strategy? If so, what types of fraud does it solve for? If not, what are your barriers to implementing one? What do your current approval processes look like? How much time are you spending manually reviewing applications and what would the cost-to-benefit be if you had something automated in place that could streamline those efforts? What solutions, do you have in place? Do they solve for one or more types of fraud? For example, can they detect the specific information anomalies that indicate an account takeover? Proactive, automated solutions are the key to preventing Account Takeover Fraud With increasing business applications and high fraud rates, now is the perfect time for risk professionals and lending institutions to take a close look at their current fraud prevention strategy and consider what improvements could be made. Many legacy fraud solutions are limited in scope compared to their modern counterparts, and often leave large referral volumes on the shoulders of analysts who simply can’t keep up with demand. This, coupled with outdated screening protocols which offer limited scope into the full picture of the application, makes it that much harder for analysts to detect account takeover fraud even when it’s right in front of them. Some institutions use tools that only seek to meet for Know Your Customer (KYC) or Know Your Business (KYB) requirements, while others may only look to verify the identity of the personal guarantor. But the key to preventing account takeover fraud is to implement an automated fraud solution that uses different data sources to confirm both the identity of the applicant and their association to the SMB. 75% of organizations rate developing better fraud detection processes as an important focus area with 71% currently planning to implement new digital fraud prevention solutions. Source7 The most effective fraud solutions provide more than simple KYC and KYB checks, they also look for various inconsistencies and connections between the business owner, personal guarantor, and the business itself. For example, a fraudster who has committed account takeover might appear legitimate on an application, passing KYC identification checks without issue, but perhaps they aren’t associated with the business, or the business itself is illegitimate. A comprehensive fraud solution looks beyond KYC and KYB at multiple and varied data sources, like professional and social networks, SBA status, website linkage, and more to detect hidden anomalies indicative of account takeover fraud. The best part about these fraud screening tools is that they work during the account opening or onboarding stage of the customer lifecycle to proactively prevent account takeover fraud losses before they impact lenders. Implementing a comprehensive fraud strategy may be in competition with other business priorities, but lenders who prioritize upgrading their outdated or limited risk processes to a seamless, automated fraud strategy will set themselves apart. They will effectively and efficiently reduce their risk of approving fraudulent applications, including those which have experienced account takeover, save time and resources spent manually reviewing large volumes of applications, and fortify their reputations as institutions that put integrity first. Sources: https://javelinstrategy.com/press-release/identity-fraud-losses-total-52-billion-2021-impacting-42-million-us-adults?cmpid=na-im-23-blog-what-is-account-takeover-fraud-how-can-you-mitigate-risk https://www.experian.com/decision-analytics/global-fraud-report https://javelinstrategy.com/research/2021-identity-fraud-study-shifting-angles https://www.bbb.org/content/dam/0734-st-louis/bec-study/bbb-explosion-of-bec-scams.pdf https://www.experian.com/business-information/landing/beyond-the-trends-report https://aite-novarica.com/report/key-trends-driving-fraud-transformation-2021-and-beyond?cmpid=Insightsblog-021121-solving-fraud-problem-account-takeover-fraud https://www.pymnts.com/study/reframing-anti-fraud-strategy-modernization-risk-management-b2b-ap-ar/

Bankruptcies and collections are on the rise since mid 2022. Pandemic-related relief and forgiveness suppressed collections for most of 2021 and the first half of 2022. Since the height of the pandemic, new business openings are at a highly elevated level. Businesses under two years in businesses accounted for 40% of new commercial credit account openings in 2022, up from 27% in 2020. While new businesses seek credit, they tend to be risky – – as it is broadly known that about a third of all businesses fail within the first couple of years in business. That is evident in the collections numbers, which show that newer businesses are driving the overall higher collection levels. As collections become a larger factor, it is critical for lenders to look for ways to mitigate losses through portfolio management efforts. Further interest rate increases likely this year Recently, Federal Reserve Chair Powell indicated that further interest rate increases are likely this year. However, the magnitude of increases is unknown since there are still mixed signals in the economy. Inflation has been slowing but is persistent. After February’s labor market reported strong numbers with continued low unemployment and high job creation, eyes will turn to the inflation report coming out on March 14th. With mixed economic indicators, it will be interesting to see if the Fed increases rates a more modest quarter of a point or takes a more aggressive position with a half-point increase at their March meeting. Download your copy of Experian's Commercial Pulse Report today. Better yet, subscribe so you'll always know when the latest Pulse Report comes out. Subscribe Today

Experian study of utility data reveals opportunity for unscored small businesses through early bureau data contribution. In today's business landscape, creditworthiness is critical to accessing the capital necessary for growth and success. However, many businesses, particularly small businesses, struggle to establish and maintain a strong credit profile. These businesses may be profitable, but to the credit system, until enough of their trading partners report payment experiences to the credit bureaus, they fall into a "Credit Invisible" segment. Experian Commercial Decision Sciences recently conducted a study of a regional utility company's portfolio, revealing a significant number of credit-invisible small businesses that had been doing business with the utility for years, yet lacked a business credit profile with Experian. This underscores the importance of data contribution to credit bureaus for establishing credit profiles, and the study provides valuable insights into the relationship between business age and credit risk. By measuring the benefits of being credit established longer, businesses can improve their credit risk profile and gain access to commercial credit. The pandemic reveals a deep disparity between large and small businesses As the U.S. continues to pull itself out of the pandemic, key economic measures provide divergent signals. From the height of 15 percent unemployment in April 2020, the rate has edged down to 6 percent by March 2021. In 2020, the stock market saw double-digit growth, supported by drastic government spending and monetary policy levels. Despite or because of the pandemic, there were clear winners from Wall Street. But on Main Street, utter devastation. According to Yelp, 55% of businesses marked as closed on Yelp could not re-open and have gone out of business. Commercial data contribution lags behind consumer Businesses, just like consumers, need capital to survive and prosper, and establishing good credit is a vital component. Most consumer credit profiles are rich in payment history because lenders and financial institutions must contribute their consumer financial trade payment experiences, such as credit card, loan, lease, and line of credit, to a credit bureau. However, there is no such requirement for commercial trade experiences. Therefore, unlike consumers, it’s not enough for a business to pay its credit obligations on time to develop a healthy credit profile. For their excellent payment history to be reflected in their credit profile, creditors must contribute the payment information to a commercial credit bureau. While the permanent business closures noted by Yelp are troubling, the pandemic saw record numbers of new business filings. According to census data, nearly 5.4 million applications to form new businesses were filed in 2021, a 53 percent increase over 2019 and 23 percent higher than 2020. A high percentage of these new businesses are minority-owned. These businesses and millions of other “Credit Invisible” businesses will need to establish a commercial credit profile with a history of payment experiences in order to qualify for better credit terms. Data contribution is essential to help these unscored small businesses succeed. Digital transformation comes at a cost for some For the past several years, there has been a concerted shift toward automated credit decisions, driven by efficiency, competitive pressures, and improving the customer experience. However, the push toward automation adversely impacts businesses with limited or no credit experience. In the animation below we outline an example of the typical credit approval process and steps where small businesses may fall off the automation path. The point of auto decisioning is to ensure a smooth and seamless process from application to approval for all applications, but it falls short in practice. When the credit inquiry is submitted to a commercial credit bureau, the business has to be found or matched. If matched, the business has to have existed for a period of time. Then there are negative event exclusions, and finally, the approval is based on the risk score. For businesses that do not have a credit profile, or even for those that do, if the credit age is not mature enough, getting credit is a slow and bumpy process. Commercial credit experience expands with age As the businesses survive past infancy and continue to mature, their credit experiences also grow. The diagram below shows the relationship between the age of a business, the average number of commercial tradelines, and the average total balance across those tradelines. For the first five years, the average spend increases at a rate of over 125% per year, while the average number of trade relations doubles during this time. The higher rate of spend per tradeline indicates the business is growing, and those businesses can establish more tradelines as they mature. The second chart below focuses on the Financial Stability Risk Score (FSR) of the businesses in the utility company portfolio. The Financial Stability Risk Score predicts the likelihood of business bankruptcy or significant delinquency, defined as 75% or more of outstanding balances 91 days plus past due. The average risk score for all businesses in our dataset is 50. The Financial Stability Risk Score average will increase as the businesses age, but as the chart shows, businesses do not get to a score of 50 until they are 5 to 8 years old. The chart shows that the vast majority of businesses become financially stable after 12 years. As businesses mature, their credit trade relationships grow. As credit trade experience grows over time, the risk of business failure decreases. This animation illustrates that transformation over time. Utility data analysis Let’s examine the negative consequences for businesses that do not have a commercial credit presence. Experian assisted a utility company with the development of a commercial deposit strategy by calculating the risk associated with the Financial Stability Risk score with their customers’ likelihood of severe delinquency in paying their utility bills over the next 12 months. The chart below shows the relationship between the score range and the bad rate (br), which is the likelihood of severe payment delinquency. As the score gets higher, the bad rate gets lower, from 24% in the worst score range to 6%. Understanding the risk associated with the score, the utility company can use the score to appropriately decision new applicants, assessing deposits for higher-risk businesses to mitigate against future loss. The blue vertical bar represents the percentage of their portfolio that falls into each score range, so the utility company understands the severe late payment risk for each of these scored accounts. Credit invisible small businesses are unduly penalized Unfortunately, 30% of the utility company’s population were unscored, and for this unscored segment, the bad rate is 21%. If these unscored accounts had been new applicants, they would have been assigned a 3-month deposit. However, because they are existing customers, we can look at the relationship between the age of the account and the bad rate. The chart below shows that 66% of the unscored segment had been active for 0-4 years with them, and the bad rate is 24%. For accounts 5-11 years old, the bad rate decreases to 19% but is still high enough to warrant a deposit. For accounts 12+ years old, they are at lower risk of becoming severely delinquent and should not be assessed a deposit. That’s 10% of the unscored segment that would be unduly penalized because they are credit invisible. In Summary Data contribution to a credit bureau can have significant benefits for establishing a commercial credit presence. As Experian highlights, many businesses, even those with a long history, may not have an established credit profile. By contributing data to a credit bureau, businesses can accelerate the process of building a stable and mature credit profile, which in turn can increase access to capital and better terms. This is especially important for emerging businesses that need continued access to credit to survive and thrive. In short, early data contribution can dramatically improve a business's creditworthiness and chances of success. In the case study, we see that younger businesses without a credit score are at high risk. How would the risk of these businesses have changed if this utility company had been contributing their portfolio accounts receivable data to a bureau? We know that businesses reach an average score of 50 around 5 to 8 years of being established on a credit bureau. By contributing a commercial portfolio to a bureau, the data provider will increase the likelihood of success for every business in their portfolio. And, in turn, every business in their portfolio will likely become lower risk and better customers. Learn more about the Trade Data Contribution Program

Consumers are borrowing to maintain spending levels even though higher interest rates make borrowing more expensive Consumer spending is by far, the largest component of the U.S. economy. At the height of the COVID-19 pandemic lockdowns, consumers were not spending and instead saved huge amounts of money. Since re-openings occurred, consumers went on spending sprees to make up for the time in lockdown. The higher demand along with supply chain issues are partly driving the high inflation. Consumers dug into their savings to continue to spend and cover higher prices. With savings dwindling, the FDIC reporting that Q2 & Q3 2022 were the two largest recorded declines in bank deposits in the U.S., consumers have increased borrowing so that they can continue to spend. As the Federal Reserve increases interest rates, a large portion of the increase in debt burden is becoming much more expensive than a year ago. These highly leveraged consumers are likely to begin driving up delinquencies, causing banks to react and tighten lending policies. With bank account deposits dwindling and borrowing becoming less available and more expensive, consumers will have no option but to cut back on spending. When consumers reduce spending, the first sectors to be impacted are discretionary areas such as travel, accommodation, restaurants, arts and entertainment, and certain retail. Businesses in those sectors have been seeking higher amounts of commercial funding over the past year compared to pre-pandemic levels. In addition, their delinquencies are increasing, indicating that these sectors are tight on cash. If sales decline at a high pace going forward, these sectors may feel the brunt of the impact of an economic slowdown. What I am watching: The Federal Reserve has taken an aggressive approach to slow the economy and cool inflation to return to the target 2% inflation rate. However, since the Fed’s most recent rate hike on Feb. 1, the January jobs and retail sales reports both came in stronger than expected, including a 3.4% unemployment rate which was the lowest in 53 years. While CPI is showing inflation slowly reducing, the latest PCE numbers show consumer prices continuing to increase. These factors are a good reason to believe that the Federal Reserve is likely to not only continue to increase interest rates at their March meeting and beyond but will also revert to larger increases of more than a quarter of a point.

This week Experian and Oxford Economics released the Q4 2022 Main Street Report. The report provides insight into the financial well-being of the small business landscape. Critical factors in the Main Street Report include business credit data (credit balances, delinquency rates, utilization rates, etc.) and macroeconomic information (employment rates, income, retail sales, industrial production, etc.). Report Highlights Consumer sentiment improved in Q4 2022, despite a softening of spending behavior. This positive behavior has contributed to the positive health and growth perspective of small businesses heading into 2023, leading to stable cash flow performance. In addition, commercial lending markets remained open and commercial delinquencies returned to pre-pandemic levels. However, higher goods and services costs may pressure spending as affordability tightens and personal cash flows thin. The US economy grew strongly in Q4 2022, but the core of the economy was soft, indicating that a repeat performance in early 2023 is unlikely. The trend in job growth has decelerated, and the Fed needs to engineer a soft landing. The Fed is pushing back against market expectations of rate cuts and is likely to hike more than expected. Download Q4 2022 Report  

Get the latest quarterly small business trends Mark your calendars! Experian and Oxford Economics will present key findings in the latest Main Street Report for Q4 2022 during the Quarterly Business Credit Review. Ryan Sweet, Oxford’s U.S. Chief Economist will share his take on Experian’s most recent small business credit data and a macroeconomic outlook for the coming quarter. Brodie Oldham, Experian’s V.P. of Commercial Data Science, will cover commercial credit trends. Presenters Brodie Oldham, V.P. Commercial Data Science Experian Ryan Sweet, U.S. Chief Economist Oxford Economics Q4 2022 Main Street Report The Q4 2022 Experian/Oxford Economics Main Street report will release at the end of February. If you are not already subscribed to thought leadership updates, be sure to sign up for updates on our Commercial Insights Hub. Event Details Date: Thursday, March 9th, 2023Time: 10:00 a.m. (Pacific), 1:00 p.m. (Eastern) Why you should attend: Leading Experts on Commercial and Macro-Economic Trends Credit insights and trends on 30+ Million active businesses Ask our panel questions in real-time Industry Hot Topics Covered (Inclusive of Business Owner and Small Business Data) Commercial Insights you cannot get anywhere else Peer Insights with Interactive Polls (Participate) Discover and understand small business trends to make informed decisions Actionable takeaways based on recent credit performance Save My Seat