Monitoring Intraday Liquidity with Atoti CE

A clear understanding of your daily liquidity, so you know your daily reserves requirements

With BCBS 248, the Basel Committee on Banking Supervision outlined a clear set of reporting criteria for monitoring daily liquidity, leading to a better understanding of the required capital needed on hand. Understanding payment flow is critical for determining this, especially given the trend towards real time cash flows like real time gross settlements. It is clear intraday liquidity is such an important topic for a company’s comptroller.

But intraday liquidity extends beyond the Treasury department. It matters for the trader who needs to monitor the movement of their client’s accounts. Or for those working in capital optimization trying to assess risk. It matters for those who are working on regulatory compliance initiatives like adherence to BCBS 248 or the US’s Federal Reserve’s Payment System Risk Policy on Intraday Credit. And, of course, for the collateral management team who need to mitigate risk in unsecured financial transactions. With intraday liquidity being so important to so many teams, a bank would ideally build out one system to tackle all of its intraday liquidity related concerns–something that is possible to do with Atoti.

How do you monitor your liquidity?

Background

We explore monitoring intraday liquidity using Atoti CE, demonstrating how to understand your cash flow, not only allowing you to meet your reporting requirements but to better understand your liquid asset needs.

As many of the world’s Central Banks move to real time gross settlements, a bank’s daily cash flow tracking has become far more complicated. Add to this mix complications like intraday margin calls (anyone else remember the wave of margin calls during the Brexit vote results???), stress events like counterparty defaults, and cross currency transactions (do you have the cash in the right currency?), suddenly it is clear that liquidity is more than just simply keeping a time based ledger.

Here, we built out an intraday monitoring system using Atoti CE. Using simulated payment data, we demonstrate how we can keep track of payments and their directions, capable of easily answering questions like:

What is the gross total payment sent or received in a day?
What are the top three ingoing and outgoing payment amounts?
When do we experience the highest and lowest liquidity?

These questions and more are at the heart of BCBS 248, the Basel Committee on Banking Supervision’s framework for Intraday Liquidity Management.

Setup

To begin our exploration, we’ve created simulated transactions experienced by a bank for September of 2021. Each transaction record details the:

legal entity
country
correspondent account
payment directionality
settled amount
settled time and date
transaction currency

and much more.

Knowing the precise minute for each transaction is imperative, as part of BCBS 248 requires reporting the max three net positive and net negative cumulative positions. Thus, the order of each transaction matters.

We compute our key metrics required by BCBS 248 sing the transactions data.

Basic metrics

The originating data contains several amounts in each record, a direction of flow (in versus out), and each transaction could be in a distinct currency. Accurately capturing the running total based on these amounts requires treating the currency and directions correctly.

To start with, we create basic metrics for each numerical column where we have currency data. This ensures we only total data where we know how it should be treated: anything without a currency should be investigated.

From here, it is also straightforward to compute the gross payment sent and received, using Direction to filter for outbound transactions for gross payment sent, and Direction and status for inbound payments. Using status is critical for inbound payments as it prevents counting cash that is still pending receipt.

With the building blocks set, we can move towards constructing metrics related to reporting requirements.

Max and min metrics

Understanding liquidity needs requires understanding what is generally speaking the best and and the worst position we can expect to find ourselves. This is handled by considering on a given day what is the max cumulative positive set of transactions (best position-we are flush with cash on hand) and what is the max negative cumulative position (our “worst” position-we are due to send out quite a bit of cash).

To ensure we are effectively handling outliers, the BCBS requirement asks for the three such highs and lows, and the computed average of these.

Value Added

With Atoti CE and real time integration, tracking the movement of cash is easy to monitor. Leveraging atoti’s on the fly aggregations, each measure dynamically updates as the data is ingested. It allows us to see in real time how our current balance, available balance and forecast amount evolves.

Curious about this use case? Reach out to us to see this use case in action, or contact ActiveViam for a demo.

Curious what this would mean if everything were converted to a single currency? Check out our article explaining how we can implement currency conversion here.

Latest posts

Understanding Logs in Atoti

From the default log to how to configure additional logging Application logs are extremely important in any system! Most commonly, they are used to troubleshoot any issue that users may encounter while using an application. For instance, developers use them for debugging and the production support crew uses them to investigate outages. Not just that, in production, they are used to monitor an application’s performance and health. For instance, monitoring tools can pick up certain keywords to identify events such as “server down” or “system out of memory”. It can also serve as an audit trail to track user activity...

Atoti: Working with dates in Python

What is the most problematic data type you have ever dealt with when working with data? I would say dates! Depending on the locale, dates come in different formats such as YYYY-mm-dd, d/m/YYYY, d-mmm-yy etc. Not to mention, sometimes it comes with timestamps and time zones! We can let programs infer the date format or explicitly cast the data to date with a specific format e.g. in Python with Pandas DataFrame: What if we open the CSV file in Microsoft Excel, update it, and try to read it again? The above code snippet will throw out exceptions such as this:...

Understanding conditional statements in Atoti

When do we use filter, where and switch statements? We know that we can perform aggregations and multi-dimensional analysis with Atoti. Aggregation is not always as simple as 1 + 2. Sometimes we end up in an “if…else” situation, and that is where conditional statements come in. Let’s explore some examples with the XVA use case from the Atoti CE Notebook Gallery. Some definitions before moving on: Measures – we refer to metrics or quantifiable data that measure certain aspects of our goals. In the code snippets below, they are represented by measure[<name of metrics>]. Members of a level –...