Excerpts from ASIAS Project 

Aggregating private and public system-wide data 

The aviation regulator in the US conducts a collaborative government and indush-y 

program known as the Aviation Safety Information Analysis and Sharing program 

(ASIAS), which aggregates national aviation incident reports and safety data from 

different data sources. The program provides a comprehensive and consistent data 

environment that enables systemic issue analysis and identification. It also complements 

incident and flight operation data with contextual information on weather, terrain and air 

traffic control. By combining isolated data sources, the ASIAS program provides a 

comprehensive and consistent data environment that enables systemic issue analysis and 

identification. Thls ultimately helps detect systemic issues earlier and more effectively. 

Additionally, the ASIAS board- a mix of public and private constituents including the 

FAA, NASA, engine and airframe manufacturers, airlines and pilot unions - flags 

specific issues and employs analysts to dive into data in search of answers. Ultimately, 

through more extensive data sharing and better safety information exh·action from the 

data, the goal is to more effectively detect potential systemic safety issues before they 

occur and to mitigate them. 

In financial services, large amount of data is already collected from financial institutions, 

but this data is not aggregated or exploited for systemic issue detection. On its own, the 

aggregation of data will not increase the resilience of the system. Therefore, the industry 

should first focus on "asking the right questions" in order to detennine a set of critical 

systemic stability indicators that could contribute to an efficient early-waming 

mechanism. Development of these indicators should be adaptive to keep pace with 

technological and financial innovation, and could work alongside efforts to enhance data 

mining and management techniques. 

Application to financial services 

There are clear differences between avtatton and financial services from a tisk 

management perspective. Most pertinently, taking risk is undesirable in aviation, whereas 

it is a vital part of the business model in many areas of fmancial services. However, both 

industries generate large amounts of data in their operations and rely on it to manage risk. 

Financial services fmns already process significant data volumes and share them with 

their regulators and other pruties (e.g., market and pricing providers). Similar to the 

aviation industry pre-ASIAS, this infmmation is currently for the most part fragmented 

and not consistently structured, ultimately preventing a coherent view of risk across the 

system. During the recent crisis, it was therefore not possible to get a precise system-wide 

readi11g on key parameters of systemic stability, such as leverage, liquidity and 

counterparty connectedness. 
The direct ASIAS analogy in financial services would be to create a complete systemwide database of transaction level data for every trade made to monitor risk. While such a 

deep and detailed data repository in the financial system is neither feasible nor desirable, 

the broader analogy has some merit, particularly with regards to how the aviation 

industry uses the aggregated data. We, therefore, propose a two-step approach for 

consideration: 

1. Through a working group between regulators, experts from research institutions, and 

industry pa1ticipants, determine a framework of critical systemic stability indicators for 

which ongoing collection of data would be beneficial (i.e. identifying the right questions 

to be asked). A starting point for discussions, based on lessons from the recent financial 

turmoil, could be indicators including the following: 

Indicator 

Connectedness of counterpa1ties 

Leverage 

Liquidity 

Significant changes in transaction volumes 

Concentration of exposures 

Rationale 

Measure of potential for contagion 

Key ingredient in most financial crises 

Expression of viability of systemic nodes 

Potentially Linked to product 'mutations' and 

crowded trading strategies 

Monitoring the knock-on effects from price or 

valuation volatility 

2. For those indicators, aggregate the relevant (which does not necessarily mean most 

granular) data (i.e., transactions vs. positions) to both monitor ongoing systemic risk and 

allow deep-drill analyses in case of 'near misses'. The exact nature of a 'near miss' is 

harder to define in financial services than in aviation, but could include hedges that did 

not work as intended, or losses in single product lines that, while significant, did not 

' bring down the house' . The data would have to be at a level of granularity appropriate 

for each indicator (e.g., aggregating the embedded leverage in many derivative products). 

While ultimately the analyses need to be anonymous for competitive reasons, the data 

should be available to the analyzing body on a named basis wherever relevant for 

measuring system connectedness. 

To ensure that this eff01t is targeted it should apply to all fmancial institutions where 

systemic risk is accumulated rather than to specific types of institutions (e.g., insurers or 

banks). This will ultimately allow treatment of sin1ilar operations in the san1e way, while 

not trying to fit a solution to institutions where it does not apply. It should be noted that 

while building such a data collection will not be trivial, a lot of the data already exist 

(e.g., with exchanges, regulators, BIS, Financial Market Utilities (FMUs), data 

providers, et al ), and it will often be only a matter of improved coordination and 

common taxonomy between data sources, rather than building systems from scratch. The 

move towards centralized clearing for many products should also make this endeavor 

easier. 
Some regulators are already going down this route in certain instances - e.g., the UK 

FSA is mandating near-live reporting of banks' liquidity. The Financial Stability Board is 

engaging in an exercise that is similar in spirit to what is described here. By getting the 

indicators (the 'questions to ask') right and moving away from blanket data dumps, it is 

our hope that such efforts can be made both less cumbersome and more effective. 

Ultimately, the information should be gathered and monitored at a global level, due to the 

global nature of financial markets. But starting the effort at a national level with all 

relevant institutions (particularly collecting 'near misses' in a systematic fashion) will be 

initially more feasible and still be an improvement from today's situation. 

The practical details of such a scheme need to be further worked through and the 

industry's concerns over implementation need to be addressed. However, the experience 

in aviation (and that of the recent financial crisis, where system-level data were often 

unavailable at crucial moments) suggests strongly that such a repository, if properly 

constructed, will be of great value to maintaining systemic stability in financial services. 

When doing this, it needs to be understood that simple product-level data composition 

will not enhance stability on its own - the data need to be complemented with systemic 

understanding (as expressed in "asking the right questions"). 

The possibility exists that a common taxonomy as is being pursued by regulators today 

(i.e., the US Treasury's legal entity identification system and the ECB's common 

reference data concept for financial products) can allow uniquely identified and 

structured data to be aggregated and analyzed "on-the-fly" leaving the data in place at its 

point of origination (i.e., at a SIFI or FMU). Through indexing and search technology it 

may be possible to eliminate the actual and continual transmission of large amounts of 

data to a central data base which, itself, could introduce significant operational risks 

related to reconciliation issues, even if the data is only at the position level and only sent 

periodically. 

To conclude, it could be argued that crashing an individual plane is unlikely to cause a 

systemic issue in aviation, whereas failure of a single financial institution can more easily 

be systemic (as proven by Lehman). So the ASIAS analogy needs to be applied carefully 

-it is relevant where it identifies common components or procedures that, if left faulty, 

could cause a plethora of crashes and thus destroy confidence in air travel. In that spirit, 

the 'near miss' concept in particular is very pertinent.