The Speed of Now: Using AI to Manage Liquidity

Most financial institutions are acclimating to real-time payments in their daily operations. Even if most of the participants in the FedNow® Service are set up to only receive payments for now (rather than receiving and sending), instant payments are here to stay.

Instant payments are also poised to radically change the way that banks approach liquidity and treasury management.

The current strategies and protocols for maintaining good liquidity are built around the current batch payment ecosystems. However, consumers are eager to enjoy the benefits of instant payments.

Banks and credit unions need to rethink their playbooks around liquidity management. They also need new tools that can handle higher payment volumes in real time. Enter the technological marvel of artificial intelligence and machine learning (AI/ML).

What Makes AI/ML a Good Solution For Liquidity Management?

A quick distinction: machine learning is a subcategory of the broader discipline known as artificial intelligence. Machine learning is focused on building statistical models and algorithms that can process data with less human intervention.

Machine learning can support financial institutions by crunching through petabytes of data and identifying meaningful patterns.

Although machine learning isn’t a new branch of AI, the lack of usable liquidity data within the financial services industry has meant that this type of technology couldn’t offer much help. That’s changed in recent years.

For financial institutions to use machine learning algorithms to support their liquidity strategies, there are two vital considerations to make:

False Correlations

Because a machine learning algorithm isn’t context-aware, it can be prone to making false correlations. For example, a positive correlation emerges if you compare a graph of the Federal Reserve’s benchmark interest rate over time with the physical heights of the Fed Chairpersons. That’s a ludicrous connection to make. But an algorithm can’t discern that for itself.

When implementing a machine learning algorithm to monitor liquidity data, financial institutions should establish checks and balances to flag potential issues and prevent them from swaying important decisions.


The number of scientists and computer engineers who understand how machine learning works is small, but it’s orders of magnitude larger than the number of bankers who understand machine learning and the algorithms that drive these models.

However, bankers do carry responsibility for the tools and models they use to manage liquidity and the solvency of the institution overall. Bankers will need transparency into the models, i.e., the reasoning and methodology for making the decision.

The need for transparency has given rise to a phrase: explainable artificial intelligence (XAI). Humans need to be able to trust the algorithms and interpret how they produce a given output.

Drinking From a Firehose of Data Instead of a Drip

Digital payment volume is on an upward trajectory with no end in sight. Combined with the adoption of FedNow and the ISO 20020 data standard, the world of payment data has gone from a drip to a deluge in a very short period.

This is excellent news for institutions ready to adopt machine learning algorithms into their liquidity management strategy. More data allows the models to improve their accuracy and capabilities, which can eliminate concerns like those mentioned above around false correlations.

Here is the basic process for training an AI model:

1.    Feed the AI a historical data set.

2.    The model learns the data set and identifies patterns and correlations.

3.    Feed the AI model fresh data in the format it was trained on.

4.    The model can then offer predictions and recommendations on the next best action.

3 Applications for AI/ML in Liquidity Management

With enough data and well-trained machine learning or AI models, three compelling use cases emerge:

Payments Fraud Detection

Real-time payments also mean real-time fraud. That leaves little room for institutions to rely on manual review processes. The good news is that machine learning algorithms can be trained on historical payment data and taught to identify fraudulent transactions. This provides a baseline to identify future anomalous payments that present a higher fraud risk.

Payments Optimization

As institutions build out products and services using real-time payment rails, and in many cases multiple payment rails, they will need tools to manage high volumes of instant payments. This activity also directly affects the bank's liquidity position and what steps should be taken to maintain proper levels.

In a broader sense, AI can also be used to run different complex scenarios to help leaders at the bank develop response plans before a crisis moves from theoretical to practical.

Cash Forecasting

Although more relevant for large commercial enterprises, cash flow forecasting can also benefit from AI and machine learning by analyzing historical cashflows and improving inflow projections. This helps with accounts payable, anticipating free cash flow, and adjusting payment campaign budgets.

A Machine That Never Sleeps

Traditionally, many institutions have used Excel spreadsheets and other manual processes to manage liquidity. They also have to contend with human mistakes that can result in ruinous costs. These processes will never scale to handle the massive influx of data that instant payment networks will generate.

Deploying machine learning algorithms into your tech stack means that you have a watcher who never sleeps, never takes vacation, and never gets sick. It’s analyzing your data even when the bank is closed.

By integrating AI/ML into liquidity management processes now, financial institutions can face the future of instant payments with confidence and highly-trained algorithms. They can meet real-time payments with real-time intelligence, even if it is “artificial.”


About Author:

Abhishek Veeraghanta is founder and CEO of Pidgin, an Atlanta-based fintech that empowers U.S. financial institutions to offer real-time payments to their account holders. Under Veeraghanta’s leadership, Pidgin recently became one of the first service providers actively facilitating live transactions for financial institutions on the Federal Reserve’s newly launched instant payment rail, the FedNow Service.

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