A screenshot shared by crypto commentator Pumpius stirred the XRP investor community after it appeared to show the Depository Trust & Clearing Corporation (DTCC) Learning Center addressing the treatment of XRP as collateral in institutional settings. The DTCC, a major US post-trade financial services provider, handles clearing, settlement, and information services for financial markets.
AI-generated answer draws attention
The screenshot, posted on X, displayed the DTCC Learning Center’s search interface with “XRP” entered as the query. The resulting page included an AI-generated answer titled “XRP Haircut and Classification,” which explained that the level of collateral haircut for XRP would be determined by its price.
According to the AI’s response, XRP priced at $5 or lower could receive a haircut of up to 100% due to concerns about volatility and liquidity, rendering it nearly worthless as collateral. On the other hand, if XRP traded above $5, the haircut could be reduced, typically to around 35% or based on a Value-at-Risk model, potentially allowing financial institutions to utilize a greater portion of its value for collateral purposes.
Here’s how the DTCC Learning Center AI summarized collateral requirements: XRP at $5 or below could be assigned up to a 100% haircut due to its perceived risk, while at prices above $5, the haircut may decrease to 35% or follow a Value-at-Risk calculation, making institutional collateral use more feasible.
Pumpius interpreted this as support for the notion that XRP would need to sustain a higher price, specifically above $5, to serve as meaningful collateral in institutional finance. He argued that the generated answer demonstrated that a “dirt cheap” XRP would be impractical for large-scale financial operations.
Mini dictionary: Collateral haircut, a financial term referring to the percentage discount applied to the value of an asset when used as loan collateral, typically reflecting the asset’s risk or price volatility.
| XRP Price | Estimated Collateral Haircut | Collateral Value |
|---|---|---|
| $5 or less | Up to 100% | Near zero |
| Above $5 | Typically around 35% or Value-at-Risk | Substantial |
Links to past XRP commentary
Expanding on this view, Pumpius drew connections between the AI-generated guidance and the positions held by key voices in the XRP community. He cited David Schwartz, Chief Technology Officer at Ripple, the technology company behind the XRP Ledger, who has consistently maintained that XRP must have a meaningful market value for efficient support of high-volume payments.
He also referenced Yoshitaka Kitao, CEO of SBI Holdings, a Japanese financial conglomerate and one of Ripple’s prominent partners. Kitao has previously signaled long-term confidence in XRP’s institutional adoption, particularly among Japanese banks, with the expectation that broader use could underpin a much higher price.
In referencing these leaders, Pumpius claimed that the DTCC Learning Center’s response supported earlier arguments that a higher XRP value is essential for serving institutional use cases.
Community pushes back on conclusions
Opposing perspectives quickly emerged among other members of the XRP community. Amanda, an active participant in the discussion, emphasized that the screenshot only showed an AI-generated answer and did not reflect an official DTCC policy or guidance on XRP collateralization. She urged caution, stating that the result simply filled the absence of direct XRP documentation.
XRPL validator Vet took a similar stance, explaining that the AI-generated answer referenced the National Securities Clearing Corporation (NSCC) Risk Margin Guide, not a specific DTCC policy regarding XRP. Vet highlighted that the underlying document does not mention XRP and that the system generated a tailored response because of increased search interest in XRP.
The AI response was formed from general guidelines and recent user queries, not any dedicated DTCC communication or regulatory position on XRP.
As feedback spread, many community members noted that, while Pumpius viewed the response as evidence of long-term institutional potential, it remains an unofficial AI-generated example with limited authority in defining DTCC’s risk policies.
