Online lenders represent a valuable resource for small businesses in need of working capital. Also known as "alternative" lenders, they are particularly useful to new businesses lacking the long, detailed credit history that banks and traditional lenders usually require to underwrite a commercial loan.

The Responsible Business Lending Coalition — a group of nonbank small-business lenders — announced a self-regulatory program during August that is designed to bring greater clarity and consistency to its industry’s pricing and consumer protections. The Small Business Borrower’s Bill of Rights outlines six primary principles that those signing the pledge will abide by when lending to small businesses. They include:

This week, we invited Charles H. Green to offer his perspectives on the online marketplace lending sector. The following article is his contribution to our series on marketplace lending.

Disruptive technology has radically changed how we shop, socialize, book vacation rentals — and even how we hail a cab. Now we have another Web-based disrupter upending yet one more venerable American institution: how we secure small-business loans.

Originally designed as a cloud-based alternative to expensive software that was not flexible, Salesforce.com has become the platform of choice for many companies. To take full advantage of the many capabilities Salesforce provides and to avoid re-creating department silos that exist with most CRM/ERP platforms, more operational business groups are moving to Salesforce to take advantage of built-in features such as 360-degree prospect and account views, workflow, approval queues and tasks.

Credit departments have long performed the important role of assessing and monitoring the health of new and existing customer accounts. However, in the wake of the Great Recession and the ensuing slow economic recovery, the need to evaluate the health of supply chain partners has become even more important.

In 2014 the Subcommittee on Small Businesses and Entrepreneurism published a report that said only 4% of the total dollar amount of business loans go to Women owned businesses. After hearing of this report, Experian Decision Sciences decided to conduct a study of Women Business Owners to see how they were doing.

Imagine for a moment a young parent who has been laid off from their job. After months of looking for work they still have not found a job. To make ends meet they start doing landscape work for neighbors in the area, eventually jump-starting a landscaping business to provide for their family. With some hard work, they start to build up a clientele in the local neighborhood. While they are starting to get back on their feet slowly, they realize at the current rate, the business will not completely meet the needs of their young family. If they could borrow just $3,000 to buy some more mowers and trimmers, however, they could hire two friends and double the size of the business.

Building financial capability and improving access to credit is essential for economic growth in our country. This is especially true for entrepreneurs, many of whom rely on their personal consumer credit standing when applying for a loan for keeping their small businesses strong or for a capital injection to expand their operations.

Ten years ago movie night at our house would usually include a run to the video store where we would pick out a selection from the New Arrivals section, some candy, perhaps some popcorn and we would have our fingers crossed the selection was a good one. Nowadays it’s not uncommon to find us binge watching streamed episodes of “House of Cards” or “Mad Men on weekends.” What’s even more gratifying is after watching “House of Cards” unprompted, Netflix now recommends “The Newsroom” and other shows we invariably like. How do they know we would like these shows? This is predictive marketing at work, driven by big data. Netflix has developed sophisticated propensity models around each member’s viewing habits, and the net result is a better viewing experience with the service. We make amazing entertainment discoveries every week. In business marketing propensity models will determine which prospects or customers are likely to respond to a particular offer. For example, the marketing department of a large financial institution seeking to expand their commercial small business loan portfolio, might want to segment and target commercial lending offers to a concentration of customers most likely to accept a particular offer. When applied in business, propensity models can unlock opportunities for increased profit, share of wallet and deeper engagement with prospects and customers. At Experian, in a typical propensity modeling engagement we will first meet with our customers to understand their goals and objectives. We talk first about pre-screen criteria that enable us to screen out prospects that would not fit into the criteria. A sporting equipment manufacturer would probably not sell to companies in the mining or agriculture industries, so we weed out the ones least likely to lead to a successful conversion. Our data scientists and statisticians get to work on large data sets and evaluate a number of factors. Experian will then develop a customized response model that will identify significant characteristics of responders vs. non responders and therefore will maximally differentiate responders from non responders. Since (holding other factors constant) a higher response rate is preferred, a response model can help lower the cost per response. The response model will generate a “score” that can be used to rank order the prospects base in terms of response likelihood. The response model can be used in two different ways to achieve maximum effectiveness. It can be used to optimize the number of responders for a given sized solicitation, or it may be used to minimize the number of solicitations in order to achieve a budgeted number of responders. A high response score will indicate someone who is likely to respond, as is shown graphically in Exhibits 1 and 2. This work results in a model of the ideal target to which an offer would most likely resonate with. This is called a lookalike. The marketing department at our large financial institution might start off with a large list of potential candidates to send the offer via direct mail, 1 million for example. But mailing an offer to that many people may be cost prohibitive. A propensity model can identify prospects most likely to accept the offer, so your direct mail campaign is more targeted, thereby increasing ROI. A highly targeted mailing to your ideal targets is a safer bet, and would make for a much more predictable outcome. The marketer can feel more confident mailing an offer to lookalike prospects because the chances of successful conversion are that much higher. That’s the case for Woodland Hills based ForwardLine, who have been providing alternative short-term financing to small businesses since 2003. Working with Experian Decision Analytics, ForwardLine did an analysis of their direct marketing program and determined that 22 percent of direct mail was generating 68 percent of their underwriting approvals, exposing a significant gap in wasted marketing funds. The Experian Decision Analytics team developed a custom model which enabled ForwardLine to algorithmically target lookalike prospects with a higher propensity to convert into a successful loan engagement. Michael Carlson, V.P Marketing, ForwardLine ForwardLine Vice President of Marketing, Michael Carlson is thrilled with the initial results. “Working with Experian we were not only able to improve performance, but we are able to reduce our marketing spend, while achieving the same results. We have taken our direct marketing effort from a small program that was profitable, but not meaningful in terms of generating significant volume, to working with Experian to achieve remarkable results. It’s largely why we enjoyed 20 percent growth this year.”     Best in Industry Credit Attributes Experian clients use our archived Biz AttributesSM along with collection specific data elements as independent variables for propensity model development. Experian’s Biz AttributesSM are a set of commercial bureau attribute definitions (includes several key demographic attributes as well) which are accurately developed off Experian’s Commercial BizSourceSM credit bureau. When used for response model development, Biz AttributesSM provides significant performance lift over other credit attributes. Biz AttributesSM are also effective in segmentation, as overlay to scores and policy rules definition, providing greater decisioning accuracy. Additionally, at Experian we are constantly monitoring our growing data warehouse looking for ways to develop new attributes. We live in an ever changing market place which requires us to develop new credit and demographic attributes as well as making enhancements to existing attributes. This process takes a disciplined, rigorous, and comprehensive approach based on experience guided by data intelligence. Our goal is to provide world-class service and the industry’s best practices for modeling attributes. To keep pace with market changes, new attributes are developed as new data elements become available, while raw data elements and existing attributes are monitored and managed following rigorous and comprehensive attribute governance protocols to ensure continued integrity of attributes. If you would like to learn more about propensity models, contact your Experian representative today.

In many cases, business lenders often rely on the commercial credit of the enterprise coupled with the personal credit of the business’s owner when making lending decisions. This is especially true for sole proprietorships and partnerships. To that end, regulatory action and public policy initiatives aimed at consumer credit often times can have a direct impact on commercial lenders. This blog takes a look at some of the top regulatory priorities for business lenders within the credit ecosystem.

At the recent “Future of Data-Driven Innovation” conference, Emery Simon of the Business Software Alliance noted that each day 2.5 quintillion bytes of data is gathered. How much data is that exactly? To put it into tangible terms, if this data was placed on DVD’s, 2.5 quintillion bytes would create a stack tall enough to go from Earth to the Moon.