Company: NEWTP
Filing Date: 2025-03-17
Form Type: 10-K
Source: 0001587987-25-000050
Chunk: 165

Company: NewtekOne, Inc.
Filing Date: 2025-03-17
Form: 10-K
Item: Item 1A
Chunk 165
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 BANK

If the credit decisioning, pricing, loss forecasting and scoring models we use contain errors, do not adequately assess risk, or are otherwise ineffective, our reputation and relationships with customers could be harmed, our market share could decline and the value of loans held on our balance sheet may be adversely affected.

Our ability to attract clients to, and build trust in, Newtek Bank is significantly dependent on our ability to effectively evaluate a borrower’s credit profile and likelihood of default. To conduct this evaluation, we utilize credit decisioning, pricing, loss forecasting and scoring models that assign each loan offered through our marketplace bank a grade and a corresponding interest rate. Our models are based on algorithms that evaluate a number of factors, including behavioral data, transactional data, bank data and employment information, which may not effectively predict future loan losses. If we are unable to effectively segment borrowers into relative risk profiles, we may be unable to offer attractive interest rates for borrowers and risk-adjusted returns for investors.

Additionally, if these models fail to adequately assess the creditworthiness of our borrowers, we may experience higher than forecasted losses. Furthermore, as stated above, we hold loans on our balance sheet. We periodically assess the value of these loans and in doing so we review and incorporate a number of factors including forecasted losses. Accordingly, if we fail to adequately assess the creditworthiness of our borrowers such that we experience higher than forecasted losses, the value of the loans held on our balance sheet may be adversely affected.

We continually refine these algorithms based on new data and changing macroeconomic conditions. However, there is no guarantee that the credit decisioning, pricing, loss forecasting and scoring models that we use have accurately assessed the creditworthiness of our borrowers, or will be effective in assessing creditworthiness in the future.

Similarly, if any of these models contain programming or other errors, are ineffective or the data provided by borrowers or third parties is incorrect or stale, our loan pricing and approval process could be negatively affected, resulting in mispriced or misclassified loans or incorrect approvals or denials of loans. If these errors were to occur, we may be obligated to repurchase the affected loans, investors may try to rescind their affected investments or decide not to invest in loans in the future or borrowers may seek to revise the terms of their loans or reduce the use of our marketplace bank for loans.

If collection efforts on delinquent loans are ineffective or unsuccessful, the return on investment for investors in those loans would be adversely affected and investors may not find