CreditRiskMonitor.com, Inc. (OTC:CRMZ)

CreditRiskMonitor.com, Inc. (CRMZ) engages in the provision of interactive business-to-business software-as-a-service (Saas) subscription products for corporate credit and procurement professionals in the United States. The companyreported Q3 2023 results:

• Sales of $4.79 million vs $4.54 million in the prior year

• EPS of $0.04 vs $0.05 in the prior year

"Corporate bankruptcy filings continue to increase relative to last year, and within our coverage universe of FRISK® and PAYCE® scored businesses, bankruptcies are almost2.5 times greater year-to-date in 2023 versus 2022. In the same year-to-date comparison between 2023 and 2020, bankruptcies are 1.7 times greater in 2023, without a pandemic shutdown or significant bear market stressors like what occurred in 2020. We continue to monitor the zombie company contagion in markets with massive corporate debt maturities starting to come due in 2024. With the ‘higher for longer' rate environment taking root in society and increasing geopolitical tensions, a sustained increase in corporate bankruptcies of highly leveraged businesses seems more probable.

CreditRiskMonitor.com, Inc. (CRMZ) ($2.85; $30.5M market cap)  engages in the provision of interactive business-to-business software-as-a-service (Saas) subscription products for corporate credit and procurement professionals in the United States. The company reported Q1 2023 results:

• Sales of $4.59 million vs $4.34 million in the prior year

• EPS of $0.03 vs $0.01 in the prior year

"In Q1 2023, our performance reflects increased recessionary concern in the economy among B2B risk professionals. Corporate bankruptcies have surged as compared to the record lows of the past two years, highlighting that the most effective strategy for focusing corporate risk teams on alternative solutions is experiencing bankruptcy. More prospect and client conversations are centered around the recent failures of some of our competitors' credit risk models to identify a counterparty's financial distress before a bankruptcy filing. Many of these models are driven by sparse and stale payment data mixed with generalized firmographic data. Using such error-prone models creates a false sense of security around counterparty creditworthiness only to be exposed in periods of high volatility.