FORECASTING & FINANCE

Cash flow forecasting: see the tension before it arrives

Cash flow forecasting with TIMi estimates the future cash inflows and outflows of an organization, to anticipate tensions and steer financing decisions, and shows the factors behind the forecast.


Inflows and outflows

Both sides of the cash position, projected together.


Real payment behavior

A customer who usually pays late is forecast as such, not at the due date.


Explained forecast

Each forecast comes with the factors behind it.

HOW IT WORKS


How cash flow forecasting works.

Three steps, in one engine, on a standard server with no cluster.


1. Prepare the data

Anatella gathers the history of receipts and payments, the known contractual due dates (invoices, salaries, loan repayments) and the usual payment delays of customers and suppliers, then structures them into variables.


2. Build the model

Modeler projects future flows from historical trends, the due dates already known and the payment behavior observed.


3. Forecast and explain

Each forecast comes with its factors, such as the usual customer payment delay, the seasonality of receipts or upcoming supplier due dates.

Data prep / ETL
Anatella
Receipts, payments and due dates
Automated machine learning
Modeler
Flows projected from payment behavior
Forecast
Cash position and its factors
Illustration: the three steps, in one engine.

GETTING IT RIGHT


Choices that shape a cash forecast.

A cash forecast is only as good as its assumptions on timing.

Start with the short term

A short horizon (a few weeks to a few months) is usually the most reliable, because it relies on due dates that are already largely known. A longer horizon is possible, with more uncertainty.

Model how people actually pay

The historical payment behavior of each customer or customer segment is an input of the model, so usual delays are anticipated instead of assuming payment on the contractual due date.

Add known events

An event known in advance, such as an investment or a fundraising, can be added as an input to adjust the projection next to historical trends.

EXPLAINABILITY


A balance says how much. The factors say when and why.

A cash forecast alone gives an expected balance, not what justifies it. A white-box model shows the factors, so finance teams can tell whether an expected tension comes from a usual customer delay, a concentration of supplier due dates or a known seasonality.


For finance and treasury teams

A short-term financing decision can be anticipated with precise context, instead of being made in a hurry.


For financial partners

A forecast documented factor by factor is simpler to justify to a bank or an investor.


For model owners

Modeler’s built-in Drift Report shows when a model loses accuracy, so you update it when needed instead of on a fixed calendar. Recompute at each significant closing (weekly or monthly), or when a notable event, such as a large late payment or a new due date, changes the situation.

What drives this forecast
Usual customer payment delay
Upcoming supplier due dates
Seasonality of receipts
Known one-off event
Illustration: relative weight of the factors for a typical case. Not real data.

DATA


What data a cash forecast uses.

Anatella connects to more than 80 sources, so this data can be gathered without prior custom development.


Receipts and payments

Historical inflows and outflows.


Contractual due dates

Issued and receivable invoices, salaries, loan repayments.


Payment delays

By customer and by supplier.


Seasonality

Factors specific to the activity.

PROOF


We do not yet have a published customer story on corporate cash flow forecasting. AXA is a related reference: it forecast withdrawals on a savings product to decide how much it could invest.

AXA | Liquidity forecasting

€250 million more could be invested safely

An individual-level model of the probability of cashing out, compared with a time-series forecast, showed how much AXA could invest while keeping full liquidity.

FAQ


Common questions.

What horizon can cash be forecast on?

A short horizon (a few weeks to a few months) is usually the most reliable, because it relies on due dates that are already largely known. A longer horizon remains possible, with more uncertainty.

Does the model take recurring late payments into account?

Yes. The historical payment behavior of each customer or customer segment is an input variable, so usual delays are anticipated instead of assuming payment on the contractual due date.

Can it include an exceptional scenario, such as an investment?

An event known in advance (an investment, a fundraising) can be added as an input to adjust the projection, next to historical trends.

How often should the forecast be recomputed?

Ideally at each significant closing (weekly or monthly), or as soon as a notable event, such as a large late payment or a new due date, changes the situation.

RELATED USE CASES


Keep exploring.


Budget Forecasting

Project a budget line from its history and the structural factors behind it.

Read more

Sales Forecasting

Forecast revenue from the sales pipeline and past conversion, with the factors explained.

Read more

SEE FOR YOURSELF


Test it on your own data.

Run TIMi on your own data with a free trial, or talk to us about your case.

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