Analytics-based lending helps improves the speed and quality of lending decisions while simultaneously reducing risk. Two developments in lending technology offer compelling reasons for adopting an analytics-based lending strategy: 1) Cloud-based data sources that provide a more detailed assessment of an applicant’s financial standing; and 2) Machine learning techniques to identify potential fraud and develop credit models that improve lending opportunity without increasing portfolio risk.
The 60-second read: Analysis of alternative credit data and trended credit data give lenders a more detailed and current assessment of an applicant’s financial strength. Machine learning technologies rapidly identify fraud and increase lending opportunities without increasing portfolio risk.
Data Sources Support Better Applicant Evaluation
New data sources—such as alternative credit data available from the major credit bureaus—complement traditional credit scores to provide more details regarding an applicant’s financial position. Alternative credit data may include employment and income history, real estate ownership, utility, cable, or cell phone records, and rental payment records. Alternative credit data gives lenders the ability to make lending decisions based on detailed, accurate, and current application information.
Decision rules applied to alternative credit data can consistently analyze and evaluate applicant financial strength to help determine the optimum deal structure for qualified borrowers.
Trended credit data offers another means of analyzing applicant financial strength. Trended credit data provides up to 30 months of payment history, including balances, minimum payment due, actual payments, and amounts past due. When compared with a credit score, trended credit data shows if an applicant’s ability to pay is improving, declining, or unchanged. Improvement lessens lending risk. Declines increase risk. In both instances, a lender should offer terms that align with the analysis provided trended credit data.
Interested? There’s no better time than now to invest a few minutes and learn How to Use Trended Data in Your Loan Approval Process.
Machine Learning for Rapid, Consistent Risk Analysis
Machine learning techniques are making a profound impact on a lender’s ability to manage risk in two critical areas—fraud analysis and credit models. Machine learning techniques bring a significantly greater degree of consistency and confidence in lending decisions and achieve these results in a fraction of time compared to current practices.
Identify Fraud That Damages Portfolio Performance
False identities, income and employment misrepresentation, straw buyers, and collateral inflation are five of the most common fraud schemes. Lenders of every size risk portfolio damage when they fail to identify and decline fraudulent loan applications. Analyzing the history of millions of loans, machine learning identifies application data that is highly indicative of fraud. Machine learning algorithms are applied to new loan applications to rapidly detect potential fraud, indicate the type, and provide a confidence score. Based on type and confidence score lenders can determine the next steps in the underwriting process—auto decline or further investigation and resolution.
Is fraud taking a toll on portfolio profitability? Invest a few minutes now to recognize The Hidden Patterns of Lending Fraud.
Replace Hundreds of Credit Models With a Single Credit Model
A lender may have hundreds of credit models and the responsibility to continually fine-tune them in anticipation of, or response to, changing market conditions. Given the number of data points involved in credit models, it’s a huge challenge to maintain the models with the goal of balancing risk against profit.
Machine learning, when applied to the volumes of applicant and portfolio performance data, creates smarter, fairer credit models. Drawing insights from thousands of variables, machine learning boosts predictive power and helps lenders accelerate lending decisions, offer deals carefully matched to an applicant’s creditworthiness, and measurably increase lending opportunities without increasing risk.
Machine learning replaces the burden of managing hundreds of credit models. A single credit model can continually and thoroughly analyze all relevant data to arrive at an optimum offer that increases the probability of acceptance while minimizing risk. That gives lenders a competitive advantage over rivals who rely on traditional credit models.
Learn how Unlocking the Power of Machine Learning can improve lending opportunities without increasing risk.
Analytics-Based Lending Is Essential in Today’s Market
The inherent insight contained in data can easily be transformed into opportunity with the aid of analytics. With the wealth of consumer and portfolio performance data now available to lenders, there’s every reason to use analytics to obtain the greatest value from your data. In today’s competitive lending climate, analytics-based lending is essential for portfolio risk mitigation.
Getting Started
defi SOLUTIONS provides configurable loan origination systems, loan management and servicing, analytics and reporting, and a wide range of technology-enabled BPO services. Take the first step toward realizing the benefits of analytics-based lending. Contact our team today or register for a demo.
