SMBs aren’t always able to access the capital they need — even if they’re perfectly qualified for a loan. As a lender, you understand that underwriting can be a lengthy process, especially when comprehensive credit data isn’t always readily available on an SMB applicant. This leads to inefficiencies — namely, wasted time and money — in the traditional underwriting process, which can fail to accurately distinguish qualified SMBs from fraudulent ones.
By enhancing underwriting efficiency, reducing manual review, and improving approval rates, the latest credit decisioning engines are rewriting the possibilities for lenders. Lenders that serve SMBs, in particular, are increasingly turning to automated credit decisioning engines like Lendflow’s — where holistic data profiles seamlessly flow into automated workflows and customizable scorecards — to upgrade underwriting operations and drive bottom-line results.
Advanced credit decisioning engines catalyze growth for cutting-edge lenders — let’s explore why.
What’s Wrong with Traditional Credit Data Analysis?
Across most, if not all industries, SMBs possess a need to finance new ventures and invest in sustained growth. But obtaining capital from traditional lenders often involves a cumbersome credit approval process — one which requires a level of time and resources SMBs often lack.
SMBs usually operate on a tight budget, under short timelines. SMBs seek quick approval because they want fast access to point-of-need capital. Seasonal projects and new ventures become jeopardized, for example, when a loan takes months to get approved… if it gets approved at all. This bottleneck needlessly prevents SMBs from accessing vital capital in a timely manner and comes at a lost opportunity cost to lenders.
A growing business might not have a long credit history or maintain a pristine credit history “on paper” within their limited transactions. Whether an SMB provides credit info deemed insufficient or simply can’t access the wide range of data requested by underwriters — data which can include credit scores, credit history, pay stubs, mortgage history, and more — the bottom line remains the same. Lenders leave revenue on the table when high-intent, qualified SMBs’ capital needs are left unfulfilled.
For too long this market inefficiency was accepted by lenders as a given cost of doing business. Thankfully, that’s changing — with recent fintech innovations in credit decisioning, helping lenders break into untapped markets and accelerate growth strategies.
Superior Data Aggregation, Superior Underwriting Outcomes
With an unprecedented amount of credit data at your fingertips, Lendflow’s high-impact credit decisioning engine streamlines your underwriting process, helps to minimize credit risk, and improves precision — all at the same time.
Lendflow’s data aggregation begins by developing a complete inventory of data on each of your SMB customers and applicant borrowers. By pulling from the full breadth of specialized data sources, lenders’ underwriting models and customer verification processes can effectively account for non-traditional SMB credit data.
Without adding risk or cost, lenders tap into these additional sources of critical financial data — including borrowers’ comprehensive earnings reports, verified income statements, payroll-linked repayments, and much more — to better evaluate borrowers.
By providing strong signals of a business’ risk profile and its ability to repay a loan, non-traditional data points enhance risk assessments. And by leveraging Lendflow’s volume discounts with partnered vendors in conjunction with the intelligent orchestrated aggregation, lenders save money on data costs. Better yet lenders can programmatically aggregate the data in an intelligent and sequential manner — without wasting dollars on data for SMBs that fail to meet desired thresholds at each stage of the decision tree.
Advanced Credit Decisioning in Action
To further witness the transformative impact of credit decisioning innovations, look no further than how a platform like Lendflow contextualizes non-financial data across bank-level transaction detail, e-commerce, social, and payroll platforms to build holistic, more precise SMB applicant profiles.
Lendflow’s data aggregation platform pulls SMB’s real-time payroll data directly from the source — such as borrowers’ employment or payroll platforms. Banks and other lenders, meanwhile, traditionally rely on weeks-old direct deposit records when making looser inferences regarding an SMB’s employment status and payroll. This data lag can drift on a matter of weeks — more than enough time for the data to be rendered moot and, even worse, inaccurate, for underwriting purposes.
For seasonal businesses, lenders can easily examine relevant e-commerce data with Lendflow to gain a full picture of a business’ historical sales. E-commerce data that accounts for seasonality, inventory turnover, payouts, marketing data, and more allows lenders to more effectively project growth and evaluate an SMB’s creditworthiness. Underwriters can also access other financial, legal, customer, and social metrics to firmly drill down their understanding of each SMB applicant.
By layering in properly aggregated data across a range of proprietary and public vendors, you can sidestep the underwriting inefficiencies and challenges banks face when using incomplete or out-of-touch SMB data.
Analyzing Cash Flow in the Context of Creditworthiness
Cash flow analysis also demonstrates the impact of optimized data aggregation. Lendflow goes beyond traditional credit history information to build a complete view of each SMB’s cash flow. The more visibility you have into an SMB’s cash flow — accounting for gross and net pay, taxes, and deductions — the more in touch you can be with an SMB’s business model, earnings history, and potential.
By examining up-to-the-minute banking data, lenders use Lendflow to better evaluate an SMB’s downstream income. Instead of making unreliable forecasts about an SMB’s gross and net income, lenders leveraging Lendflow’s powerful data aggregation platform can account for taxes, payroll deductions, and other balance sheet items to run more precise valuations.
Remember that lenders traditionally compile bank statements, cash-flow analyses, and other financial reports manually when assessing an SMB’s financial health. From a time-value standpoint, this analog collection process alone can be a resource drain.
Lendflow’s automated data aggregation and orchestration engine, on the other hand, helps lenders scale efficiently — without any time lag. By scanning the full breadth of an applicant’s electronic records in real-time, underwriters process each prospective SMB faster, with more consistency.
Getting on Board with the Data Revolution
If you’re a bank or traditional lending institution, there’s no need to wait and watch while fintech platforms continue to reap the benefits of automated credit decisioning. By offering a fast and precise picture of an SMB’s credit history and financial outlook, innovative credit decisioning engines eliminate the inefficiencies that have long plagued the lending industry. Lenders don’t need to add risk or cut corners to reach more qualified customers, achieve faster underwriting turnarounds, and run more precise credit analyses.
In other words, optimized automated credit decisioning doesn’t compromise your underwriting requirements or saturate the quality of your clientele. Far from it. Lenders save valuable time and improve risk assessments because Lendflow’s automated data aggregation leaves no stone unturned. With improved underwriting accuracy, efficiency, and output, both you and your SMB customers benefit.
Automated decisioning with non-traditional data could be just what you need to take your lending business to the next level. To get started with data-driven decisioning, talk to one of our representatives at Lendflow!
