Author/Source: Non-Prime Times
Sub-prime lenders must sense the famous saying all the time nowadays, because the more things change, the more they stay the same. Sure, the industry is in a drastically different place today compared to five or even 10 years ago. Today, there are a bevy of data analytics and technology platforms to leverage that not only help them compete more effectively, but they also help in the battle against shrinking margins.
Conversely, even with all of this change, sub-prime lenders continue to grapple with policy oversight, CFPB issues, compliance and, yes, the ever-threatening economic stall.
One of the exciting new technologies available that more sub-prime lenders are leveraging today is Predictive Analytics and Predictive Modeling.
How predictive data impacts sub-prime lenders
Sub-prime lenders today rely on yesterday’s credit and collateral data in order to create the right loan terms for customers. Data such as current market conditions, historical job and income levels and even economic criteria are all being leveraged to create the right loan for the right customer and the right vehicle.
Predictive analytics goes above and beyond this level of risk insight. It leverages science and analytical trends to create algorithms and formulas that combine economic insights along with data mining trends to arrive at a forecasted output that is scrutinized for more accurate loan structuring.
How data forecasting keeps sub-prime lenders competitive
The ability to arrive at better forecasted outcomes on loans helps create a more competitive and profitable portfolio. Sophisticated technology and analytical science are now teaming together to create insightful tools to assist lenders, known as machine learning and predictive modeling. These analytical solutions leverage historical data and uses it to “train” highly accurate predictive models for lenders and their decision-making.
This function helps sub-prime lenders specifically, who are trying to find the right loan for customers while also looking to improve their business and financial performance metrics. Predictive modeling allows sub-prime lenders to create future business insights with a significant degree of accuracy.
Predictive modeling and analytics tools are expected to reach approximately $10.95 billion by 2022, growing at a compound annual growth rate (CAGR) of around 21 percent through 2022, according to Zion Market Research.
Leveraging machine learning for smarter loans
Machine learning and predictive modeling can greatly improve the loan underwriting process. This is critically important since accurate underwriting serves as the foundation for loan loss mitigation. In underwriting, lenders must leverage accurate data to asses the borrower and collateral, which is used to set the risk level, loan price and overall approval. Predictive modeling can leverage algorithms that identify data points that were not historically identified and leveraged in previous loan underwriting processes, such as leveraging alternate data and income verifications.
Machine learning can also be leveraged during loan servicing to forecast certain customers who maybe at higher risk than others to miss a payments. Predictive modeling can be used by subprime lenders to accurately analyze large swaths of data to determine schedules of on-time historical payments. These models can be used to help sub-prime lenders set the right customer contact cycles, which may require additional phone calls, text messages and even email notification.
Why predictive data increases decision accuracy
There are a few reasons why today’s predictive modeling data is more accurate than what organizations had access to in years past. Primarily, predictive modeling is based on actual, empirical data and macroeconomic insights from historical outputs and present-day models. It is far more elaborate than the forecasts based on theory that were used in legacy loan operations.
Empirical data is based on “evidence” derived from previous datasets and other criteria that have proven themselves in actual real-world scenarios. These data outputs are then formulated into precision-based models and scenarios that offer visibility into accurate forecasting techniques lenders and data scientists today use to arrive at certain economic conclusions in their decision making.
Auto lending, particularly sub-prime, is an industry constantly searching for tools to help increase customer satisfaction with the right loan, mitigate risk levels, reach compliance goals, and improve overall portfolio performance. This top-down approach through data analytics will enable more sub-prime lenders to incorporate and grow their predictive modeling initiatives for a greater competitive advantage.
Author/Source: Non-Prime Times