At the end of 2024, we witnessed the Archegos season finale. The financial services soap opera ended with Bill Hwang sentenced to 18 years in prison for fraud that resulted in a collapse costing banks around $10bn. The episode was bruising for the banks that acted as prime brokers, hit by leveraged exposures to Archegos, and a wake-up call for the broader industry. However, the question remains: did Bill Hwang’s prosecution usher the closing credits or could the saga reprise?
The subsequent post-mortem revealed the extent of Archegos’ deception and over-leverage but also the under-preparedness of the institutions caught in its web, bringing into question the risk management practices and their preparedness for future shocks. Pre-Archegos, many firms had robust risk management tools and capabilities in place. However, a lack of real-time data, adequate stress testing and a sometimes-lax attitude to breaches set the scene for the crisis that followed.
Addressing technological gaps, flawed processes and siloed practices are key to consigning Archegos-style risk events to the history books. This means deploying risk models that integrate prime brokerage data—most importantly, in real-time. Without it, prime brokers are flying blind, reacting too late while competitors stay ahead by making informed, real-time decisions.
Iceberg, right ahead
To understand the lessons of Archegos, it’s important to revisit the events leading to its collapse on March 26, 2021. At its core, the crisis stemmed from excessive leverage concentrated in a few stocks—primarily ViacomCBS and Discovery—through total return swaps. This magnified Archegos’ exposure, as well as that of the banks holding the underlying stocks. When prices fell sharply, margin calls were triggered but went unmet, forcing banks into panic selling, which only accelerated the collapse.
What prime brokers failed to realize was that they were only seeing the tip of the iceberg. Beneath the surface, the actual risk was far greater than what they had been led to believe. This highlights a fundamental challenge: how can risk management teams operate effectively when their view of exposures is incomplete?
The solution lies in breaking down data silos and ensuring risk systems incorporate firm-wide risk data that is centralized, accessible, and shared across all departments. More critically, risk management and prime brokerage functions must be connected in real time. Without this, banks lack the visibility to anticipate and react to crises before it’s too late.
Leverage itself isn’t the problem—it’s the inability of banks and prime brokers to visualize, anticipate, and act on leverage that turns it into a systemic risk. In high-stakes situations like Archegos, speed is everything. Access to the right data at the right moment can mean the difference between mitigating losses and being left with the fallout.
Breaking silos
In the wake of Archegos, it has become clear that banks’ failures stemmed from three key weaknesses: concentration risk, leverage risk, and inadequate oversight. To prevent another collapse, banks must adopt systems that connect risk and prime brokerage teams, providing a more complete picture of counterparty risk. A centralized, data-driven approach allows prime brokers to:
- Enhance risk management systems: By upgrading risk management systems and practices across all departments and counterparties, banks can flag potential problems earlier through real-time monitoring of client portfolios – triggering pre-emptive actions when certain thresholds (such as losses or leverage levels) are breached.
- Improve margin and collateral management: By conducting regular stress tests on client portfolios and accounting for prime brokerage data, banks can assess the potential impact of extreme market events on client positions.
- Better monitoring of concentration risk: By enforcing required position limits, banks can limit the size of a client’s exposure to any single stock or sector and track aggregate exposures across all clients. This also means that banks could share anonymized data to identify when multiple institutions are exposed to the same risks.
Without major regulatory changes and improved communication between banks—both of which conflict with the competitive nature of the market—there’s little to prevent another Archegos-style event. Traditional counterparty risk models, such as Potential Future Exposure (PFE) and margin period of risk (MPOR), provide some insight but fail to capture the full complexity of counterparty and wrong-way risk.
To stay ahead of the next crisis—and their competitors—risk managers must embed real-time insights into their frameworks. The next Archegos-style event isn’t a matter of if, but when. Only those with the right data, at the right time, will be able to react before it’s too late.
