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SLM Q2 net income falls

SLM Q2 net income falls


Overview

  • U.S. private student lender's Q2 net income fell and GAAP EPS declined yr/yr

  • Private education loan originations rose 4.5% from a year earlier

  • Company completed $200 mln accelerated share repurchase and sold $420 mln in loans


Outlook

  • Company expects 2026 diluted EPS of $3.10 to $3.20

  • Company sees 2026 private education loan origination growth of 12% to 14%

  • Company forecasts 2026 net charge-offs of $365 mln to $385 mln


Result Drivers

  • LOAN ORIGINATIONS - Private education loan originations rose 4.5% yr/yr, reflecting continued demand

  • HIGHER CHARGE-OFFS - Net charge-offs increased to $113 mln, attributed to misaligned third-party debt resolution practices affecting a small, high-ability-to-pay segment

  • BALANCE SHEET ACTIONS - Sale of $420 mln in loans and $200 mln accelerated share repurchase supported earnings and capital deployment


Key Details

Metric

Beat/Miss

Actual

Consensus Estimate

Q2 Net Income

$59 mln

Q2 Credit Loss Provision

$126 mln

Q2 ROCE

9.9%


Analyst Coverage

  • The current average analyst rating on the shares is "buy" and the breakdown of recommendations is 6 "strong buy" or "buy", 4 "hold" and 1 "sell" or "strong sell"

  • The average consensus recommendation for the consumer lending peer group is "buy"

  • Wall Street's median 12-month price target for SLM Corp is $28.50, about 13% above its July 22 closing price of $25.22

  • The stock recently traded at 8 times the next 12-month earnings vs. a P/E of 7 three months ago


Reuters Recommended Reads

  • July 23 - Enova International Inc Q2 revenue beats estimates on originations growth

  • July 21 - Capital One second-quarter profit rises on higher interest income

  • July 23 - Switzerland's Cembra H1 net income rises on lower costs


For questions concerning the data in this report, contact Estimates.Support@lseg.com. For any other questions or feedback, contact reuters.support@thomsonreuters.com.


(This story was created using Reuters automation and AI based on LSEG and company data. It was checked and edited by a Reuters journalist prior to publication.)

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