Over the last 12 years, we have encountered many funders, which usually face several challenges to grow.
One such area for improvement is the scalability of processes and systems used for credit risk and underwriting.
The reason for this limitation is deeply rooted in the traditional underwriting process, which is:
- Subjective in nature: Poor data points and human factors play a role.
- It is a labor-intensive process. In other words, it’s expensive.
- Internal contention results from it, which can increase inefficiencies.
In the usual decision-making process, Leads are filtered through a series of zero-one or pass/fail type of filters.
Among other data points: Available cash flow, credit score, time in business, average deposits/ net cash flow, and so forth.
Even though several Systems allow for gathering the usual data points, they are – mostly – solely for presentation purposes. Sometimes, they can automatically follow a series of – conceptually the same – pass/ fail filters.
Systems of the first type (presentation only) rely on human decisions and timing; thus, they have volume and speed limitations.
The second type of System tackle and improve the speed and decision-making process; their logic can allow for a decision tree-like definition. But they are only an enhancement on the rate of these pass/fail filters.
The problem with the pass/fail approach is compounded by an intrinsic limitation of us humans: we can only relate a couple of variables simultaneously. So while we are narrowing the universe of possible deals and trying to hone in on the “good ones”, under the perspective of one or perhaps two variables, we are losing sight of other possible combinations in the data points that might be better to explain results; thus, we are losing volume and not necessarily choosing the best prospectuses.
At NISO, we build best-in-class models, trained to improve the ability to select better deals, forecast default levels, and accurately estimate the amount of RTR that contracts and vintages will ultimately collect.
These proprietary models can improve RTR collections by selecting better deals and that will ultimately collect at least Eight to Ten percentage points better than conventional methods (8% to 10%); and are fast to detect, flag, and react to environmental changes, allowing decision-making to adjust or avoid directing resources into less-than-ideal areas.
Moreover, specific models can be developed to hone in on desirable sectors requiring extra detailed analysis.
We facilitate your growth by providing a suite of services that cover everything from bookkeeping to Controller activities and from a full suite of analytics reports and dashboards to monitor to the portfolio but also to provide you with the most complex models to help you choose better, react to environmental changes, and reduce your risk, and improve overall performance. Everything, tailored to your specific way of operating.
Unlock the full potential of your MCA business and take the first step towards sustainable growth and scalability. Let NISO’s cutting-edge technology and expert analysis guide you toward better deals and improved performance. Schedule a free call with our experts today: https://calendly.com/nisomeet/15min?back=1&month=2023-01