It was a busy summer for four of America's biggest banks. In September 2024, Bank of America, Citigroup, Goldman Sachs, and JPMorgan Chase had to submit an action plan to regulators to close the gaps in their resolution plans.

What is a resolution plan?

A resolution plan is a detailed strategy developed by financial institutions and overseen by regulators to manage the orderly resolution (or winding down) of the bank in the event of financial distress or failure, without triggering a financial crisis or requiring a government bailout.

This regulation was introduced in the wake of the 2008 financial crisis, with the main aim of ensuring that bankruptcy could be resolved without the need for taxpayer intervention and without significantly disrupting the financial system. It applies in every major jurisdiction.

These plans include a number of components, including the Trading Wind-Down. 

What is Trading Wind-Down (TWD)?

When a resolution plan is launched, one of the bank's first objectives is to reduce its exposure to trading activities as quickly and efficiently as possible, in terms of both market and counterparty risks. The Trading Wind-Down (TWD) exercise requires the financial institution to very precisely formalize the scenarios for reducing activity and estimate the associated costs, consequences for its financial ratios, and impact on financial markets.

The UK Prudential Regulation Authority (PRA) detailed what needs to be formalized in its Supervisory Statement SS1/22:

1. Division of the trading activities portfolio into sub-portfolios or segments, each of which will be subject to a specific exit strategy;
2. Close-out or termination of positions prior to maturity (subject to stays that may be in effect in resolution in the relevant jurisdiction);
3. Contractual run-off (allowing contracts to run to maturity without being replaced or renewed);
4. Auction or transfer of positions to a third party, or novation (the termination of a contract and its replacement with a new economically equivalent contract with a different party) of such positions;
5. Actions taken to reduce risk via hedging; and
6. Actions taken to manage the liquidity of the balance sheet.

The PRA provides templates as part of its expectations for firms' wind-down planning, which helps ensure that firms can exit the market in a controlled and efficient manner, minimizing the impact on the broader financial system. The information to be included in Trading Wind-Down templates is many and varied from identification of critical trading activities, timeframe for the wind down, and impact analysis.

Starting next year, financial institutions to which TWD applies will be required to produce and maintain sufficiently detailed data consistent with these templates and at a frequency of at least monthly.

For these templates, all of this data has to be available to make the calculations:

• Static data that has to be available:
  • Products segmentation rules
  • Hedging strategy rules
  • Spreads referential
  • Exit cost calibrations
  • Client and product hierarchies

• Orchestration engines allowing:
  • Segmentation of trades
  • Calculations of:
    • Hedging costs
    • Variation/forecast of market sensitivities, capital, and liquidity metrics  

The ability to produce and understand the templates from the aggregate level to the most granular is critical in the entire TWD framework and governance. This unique combination of market risk, credit risk, liquidity risk, and capital highlights the data challenges linked to this implementation as data are fragmented across multiple systems.

Why Opensee for Trading Wind-Down?

Regulators require these indicators to be available almost automatically on demand, with the ability to access the most granular view of each transaction. It requires real-time data capabilities to make timely decisions about which trades to unwind or retain. Manual collection and calculation processes are not compatible with the required frequencies and are creating too many operational risks.

Financial institutions need to be able to bring together a range of data scattered across different information systems in a single dataset, ensure accuracy, and add missing data such as the parameters used to define “wind-down” costs by type of instrument and family of counterparties, and then use this data to perform a number of calculations to arrive at estimates of costs and residual exposures.

To do this, banks need an extremely powerful and scalable data management and analytics solution capable of solving these key challenges of data completeness and accuracy, data Integration across systems, data granularity and transparency, real-time data processing capabilities, calculations, and last but not least, scenario analysis.

Opensee is able to solve these challenges through:

• Implementation of a predefined data model enabling the collection of market risk and CCR information, liquidity, and capital
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• Complete scalability in terms of storage, enabling access to:
  • The most granular data on individual positions, including specific details about counterparties, collateral, and funding
  • Unlimited historical data which allow testing of various scenarios in different wind-down conditions, typically on market events, liquidity patterns, and prior trading activities
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• Flexible Python calculators (User Defined Function) to produce the required calculations from the different datasets, like quantifying the financial impact of winding down certain positions, in terms of mark to market losses and potential margin calls
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• Ability to perform adjustments and simulations on the fly while keeping all the necessary audit trails. TWD is all about simulations helping firms quantify the financial implications of various TWD exit strategies, including potential losses from liquidating positions, funding needs, and collateral requirements. Regular simulations enable firms to refine and improve their TWD plans, using insights gained to adjust strategies, recalibrate assumptions, and update processes.
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• AI tools for data quality and data exploration: Incomplete or inaccurate data can make it difficult to assess the scale of the positions that need to be wound down, as well as the exit costs and impacts. It may lead to incorrect valuations or potential exposures that are not identified. Ensuring that this data is complete, accurate, and up-to-date across various trading desks, portfolios, and jurisdictions is critical. Opensee’s AI outlier detection helps to identify the potential inconsistencies at a granular level, while the AI data exploration tools help to focus and analyse the key drivers of significant TWD impacts.

Bank resolution plans and Trading Wind-Down are essential tools for ensuring the resilience and stability of the financial system. By enabling the orderly management of bank failures and reducing the risks associated with trading activities, these mechanisms play a crucial role in preventing financial crises especially in times of distress.

While the main regulators (PRA, ECB, FED) all aim for a compliance horizon for Trading Wind-Down by 2024-2025, with expectations varying according to jurisdiction and financial institution size, the recent example of the Bank of London, which had to provide a detailed Trading Wind-Down plan in view of its sudden need for capital, shows that the subject is very real.

The ability of financial institutions to meet this timetable will depend on their ability to implement a solution like Opensee as it underscores the importance of robust data management frameworks. These frameworks need to ensure that data is accurate, integrated, and accessible across trading, risk, and reporting systems. Financial firms must address the data-related challenges associated with TWD.