Financial Risk Management is only as good as the quality of the data

Good data helps to prevent financial losses

Data quality has always been important in Information Technology. G.I.G.O. (Garbage In Garbage Out) is one of the oldest acronyms in IT, and it still as valid as ever. In Financial Risk Management G.I.G.O. should really be changed into G.I.L.O., meaning “Garbage In, Losses  Out”, because “Garbage In” can cause huge losses, or huge fines, not to mention reputational damage.

High-performance data quality management and optimized data warehousing processes are what make standardised internal and external risk reporting possible. Given the interdependencies, it’s critical that your organisation moves toward the goal of creating a single source of the truth for reporting, risk control and treasury activities. Investing in data cleansing prior to integrating operational data may be required.

In general, data quality is essential for regulatory reporting. In 2018 and 2019, regulators issued very heavy fines to global financial institutions such as UBS, Deutsche Bank, HSBC, etc. based on the data quality of their regulatory reporting. It is also a fundamental component of the drive to increase profits, reduce costs, and generate new business.

Specifically, the result of a financial risk simulation is as good as the quality of their input. Risk professionals use the results to decide on risk policies, investments, hedging strategies, etc. Although the result of the computer processing is just a support to the decision taken by the human, the human will decide based on the results displayed on a computer screen. Market data providers are not infallible, and it is important to spot any inconsistency. Especially during times of market stress when volatility is high, and it is important to base your decision on the latest correct information. There are several operational risk scenarios that may compromise the quality of your data, for instance:

1) Infrastructure issues may cause skipping an update. If you receive an update every ten minutes, you may not realise that you have missed one update and work with “old data” for a few minutes. Irrelevant most of the times, but very dangerous sometime.
2) Cybersecurity issue. You may not realise immediately that a hacker is blocking the feed or feeding you wrong data.
3) Something might have gone wrong upstream in the workflow, and you did not receive another system’s output in time.

Those were just three examples of what could possibly go wrong. A lot of the data used in Financial Risk Management comes through feeds from trusted sources; it is easy to assume that there is no need for data cleansing. You need to verify the input anyway because the last thing you want to do is importing other people’s hiccups.

How ALFO SABR can help

ALFO SABR is a multi-asset, multi-currency and multi-market real-time risk management and hedging system supporting exchange-traded and OTC instruments.

ALFO SABR performs a data sanity check of all the inputs. It flags anomalies and the user decides which action to take. Humans must decide, not computers. Sometimes automatic correction of anomalies can create bigger problems.

When loading prices, the system checks against previous days, moving averages, etc. A pattern analysis that can flag anomalies when the variation is higher than pre-defined level of tolerance.  For instance, varying the levels of tolerance of statistical measures, it becomes possible to identify “fat fingers”-type of mistakes. An analyst can justify flagged anomalies when they are due to corporate events such as stock splits, or mergers.

Check sums allow you to identify if you have received the data you expected to receive, nothing more and nothing less. Reference-ID that can be recreated to check the validity of the id received in the feed.

General data verification helps to identify data that is not current anymore, such as bonds that have expired or being recalled. Data is also verified against multiple sources, some of them public; for instance you can verify whether the rate you received is the target rate or the effective rate. Mistaking one for the other can lead to difference in risk analysis that can be worth hundredof millions of dollars.

As we mentioned earlier, these anomalies are always flagged, and it will be up to a human being to decide which action to take.

 

About ALFO

ALFO is a Deeptech IT company that excels in using AI driven solutions to deliver multi asset risk management, trading, and investment performance tools to the professional financial services sector. Our clients enjoy pain free access to systems and AI technology they previously thought unattainable and beyond their reach. We make you better, faster. We allow you to do things you thought you could not do.

 

The ALFO advantage:

• Custom-tailored technology that adapts fluidly to your ecosystem
• Quality and performance essential to compete at any level
• Agility and dynamism to embrace change
• Significant Reduction of Total Cost of Technology Ownership

ALFO SABR is a multi-asset, multi-currency and multi-market real-time risk management and hedging system supporting exchange-traded and OTC instruments. Discover more about ALFO SABR