This paper examines the value and impact of using purchase-level information, specifically its role in alleviating the information asymmetry in the credit markets. Using a proprietary lending dataset from a firm operating in the BNPL industry, I evaluate the underwriting practices of the lender and document three findings - i) the lender’s internal score predicts the likelihood of a loan defaulting with 18% greater accuracy than traditional credit score, ii) the lender’s internal score incorporates purchase-level information, and iii) the lender prices a significant portion of loans using purchase-level information. I find that a model that includes purchase-level information can lend 33 percentage points more in loan count, increasing financial inclusion without having a negative effect on the default rate while also generating higher returns for the lender. Similarly, a model that includes purchase-level information reduces de- fault by 3 percentage points. Leveraging a randomized experiment conducted by the lender and discontinuity in lender pricing, I find that a 10 percentage point increase in offered interest rate decreases applicant take-up by 14.25 percentage points with no significant impact on the loan performance.