Edge, using its industry-leading data lake, has gained valuable insights into the influence of Buy Now Pay Later (BNPL) on various credit types. Proprietary research reveals that borrowers engage in familiar patterns such as loan stacking, increased borrowing during financial stress, and heightened default risk due to additional obligations.
Buy Now, Pay Later plans are frequently in the headlines for opportunities and risks alike. BNPL obligations pose a challenge for other lenders because they are largely unreported to credit bureaus. However, these liabilities can be reliably found in bank transaction data – an essential step in decisioning on a complete picture of consumer risk.
At Opal Group's summit, Mark Friedgan, CEO of NinjaHoldings, showcased NinjaEdge's data-driven analysis of 2.5 billion bank transactions, addressing the risks of "hidden liabilities" like BNPL loans and advocating for more profitable underwriting solutions that encompass a consumer's complete financial picture.
Today, we're diving into the world of storefront lending and how automation is transforming the game. We'll explore the incredible advantages of automation and how it's paving the way for more efficient, customer-friendly, and data-driven lending processes.
In the world of lending, making sound decisions is paramount. A borrower's income is a critical factor in assessing their creditworthiness and ability to repay a loan. Lenders require effective tools to evaluate this key aspect of every loan application. Enter EdgeIncome, a solution that empowers lenders to make informed decisions while streamlining their operations.
Bank transaction-based credit risk underwriting revolutionizes lending by providing valuable insights into borrowers' repayment abilities, and EdgeScore excels as a leading predictive risk score in accurately assessing risk across the credit spectrum.
Verification of income has traditionally been one of the most expensive and time-consuming steps in a loan application. With the innovation of instant bank verification (IBV), proof of income can be achieved in near real-time along with a host of risk insights relevant for credit decisions.
Edge, using its industry-leading data lake, has gained valuable insights into the influence of Buy Now Pay Later (BNPL) on various credit types. Proprietary research reveals that borrowers engage in familiar patterns such as loan stacking, increased borrowing during financial stress, and heightened default risk due to additional obligations.
Buy Now, Pay Later plans are frequently in the headlines for opportunities and risks alike. BNPL obligations pose a challenge for other lenders because they are largely unreported to credit bureaus. However, these liabilities can be reliably found in bank transaction data – an essential step in decisioning on a complete picture of consumer risk.
Traditional credit scores are inadequate for assessing risk in nearly half of the U.S., while analyzing cash flow behaviors in checking and savings accounts provides a more comprehensive financial picture, leading to actionable insights that enhance underwriting decisions.