Each Archegos lender already held enough data to act
Archegos is usually told as a story of positions hidden across many prime brokers. The costlier failure sat inside individual banks, in three layers: what each could see of its own exposure, what it could infer about the rest, and whether anyone acted.
Looking Back revisits one week at a time, from January 2020 to today, and asks what it meant for the banks, health systems and public agencies we serve. The series was written in 2026, with the benefit of hindsight, and each article says which week it looks back on.
The week
On Friday, March 26, 2021, several large banks began selling big blocks of shares in companies including ViacomCBS, Discovery and a number of China-based firms listed in the United States. The sales followed margin calls that Archegos Capital Management, the family office of investor Bill Hwang, could not meet. Archegos had built very large, concentrated equity exposures largely through total return swaps with several prime brokers, so the positions did not appear in its name in public ownership filings. As a family office, it was not registered with the SEC as an investment adviser. Banks that sold quickly limited their losses. Those that moved more slowly took heavy ones: Credit Suisse later put its loss at about $5.5 billion, and Nomura reported a loss of nearly $3 billion.
The common account of Archegos stresses opacity between banks: each prime broker saw only its slice of a much larger position. That is true, and it drove later debate about swap reporting. It also lets individual institutions off too lightly. The independent review Credit Suisse published in July 2021 describes a firm that had the data to see trouble and did not turn it into action. For risk, data and technology leaders, the episode divides into three layers: what a bank could see about its own exposure, what it could infer about exposure elsewhere, and what it did with the signals it had. Only the second was truly a market-structure problem.
Layer one: the bank’s own exposure
Each prime broker knew the size of its swaps with Archegos, the underlying names, the margin it held and the concentration in each stock. It could calculate how much a given price move would cost and whether margin would cover it. That is basic counterparty credit risk work, and it needs no information from competitors.
The Credit Suisse review found that internal limits had been breached repeatedly and that margin terms stayed static even as positions grew more concentrated and volatile. Some other banks reportedly used dynamic margining, in which collateral requirements rise automatically as a position grows more concentrated or less liquid. That design choice shaped who lost what.
Architecture mattered as much as culture. Exposure to one client often sat across several systems: swaps in one booking platform, cash equities in another, margin calculations in a third, credit limits in a fourth. A single current view of a client’s total exposure, stressed for concentration and for how many days it would take to sell each position, meant joining data never designed to join. Where that view was produced weekly or by hand, it lagged the market. Where it ran daily and automatically, risk managers could argue from current numbers. A useful single figure is days to liquidate: the bank’s position in each name divided by a cautious share of normal daily trading volume.
Layer two: inferring the rest of the market
The harder problem lay beyond each bank. Swap positions did not show up in the public filings that normally reveal a concentrated holder. Still, there were signals. A client holding a very large share of a company’s float through one bank probably holds more elsewhere. Unusually large positions in thinly traded names, the stock’s own trading patterns, and the client’s answers about its other relationships all carried information.
Using that information means treating inferred data as an input to limits and margin, alongside what the bank can prove. A practical method is to assume the client holds a multiple of the bank’s own position in each name, and to stress the liquidation scenario on that basis. It also means asking clients direct questions and writing contract terms that allow margin to change when the answers are unsatisfactory or absent. Some banks did this better than others, and it showed in their outcomes.
Cross-firm opacity was real, and no single bank could fix it. But a bank can decide how to price what it cannot see. Treating the unknown as zero was a choice, and an expensive one.
The trigger itself was public. Earlier that week, ViacomCBS announced a large stock offering, and its share price fell sharply over the following days. A bank that knew its client held a large swap position in ViacomCBS could see the problem in market data it already licensed. The client’s collateral was eroding in the very names where it was most concentrated. Joining internal exposure with external market events is a modest data engineering task. It is also the kind of alert that turns a weekend fire sale into a Tuesday margin call.
Layer three: from signal to action
The final layer is governance. Risk data is worth only the decisions it triggers. At the banks that lost most, warnings were raised and then deferred, often because the client was profitable and the relationship valued. Several banks that exited quickly appear to have given risk teams clearer authority to act on their own data, including on a Friday afternoon.
The Basel Committee’s principles for risk data aggregation, published as BCBS 239 in 2013, were written for exactly this kind of failure: complete, timely, accurate risk data that reaches decision-makers, including under stress. Archegos showed that compliance on paper differs from a risk function able to force a decision about a valued client. Useful tests include how long a limit breach can remain open before it escalates automatically, who can override it, and whether overrides for high-revenue clients are reported to the board risk committee.
The three layers apply well beyond prime brokerage. Wealth managers see concentrated client positions and margin loans against them. Regional banks see commercial borrowers with debts at other lenders. Credit unions see members with obligations elsewhere. In each case the questions are the same: can you aggregate what you already know, can you reason about what you do not, and does anyone have the authority to act?
With hindsight
From 2026, Archegos reads as an early chapter in Credit Suisse’s decline. The bank was taken over by UBS in March 2023 after a crisis of confidence. In July 2023, the Federal Reserve and the UK Prudential Regulation Authority fined Credit Suisse entities over the risk management failures that Archegos exposed. Bill Hwang was convicted of fraud and market manipulation in 2024 and sentenced to 18 years in prison. The SEC proposed position reporting for large security-based swap holdings, but cross-firm visibility stayed partial. The inside-the-bank findings aged best. Concentration data was again available and underused in the regional bank failures of 2023, where uninsured deposit concentration was visible in each bank’s own records well before the runs.