How to automate P&L forecasting with giniPredict

By
on
Jan 6, 2021

See how easy it is to run forecasts on your P&L data with giniPredict. Find out what impacts your profit most, if and when you may need a loan, and how machine learning improves the whole process.

Getting 100% clear on the financial future of your company is the number one priority of every business owner. But in today’s volatile market, P&L forecasts go quickly out-of-date.

Instead of once a month, you now have to update your P&L forecast every time something changes — which can be as often as daily. Who has time for that?

The consequence of not updating your P&L forecasts often enough, though, is dire — 82% of companies that fail do so because of a lack of understanding of their cash flow and working capital.

The fast-changing environment we now find ourselves in calls for a new approach to P&L forecasting.

Fast.

Powerful.

Accurate.

Introducing: Machine learning predictive models (wait for it) baked into a simple spreadsheet format. 

That’s giniPredict.  

Why automate your P&L forecasts with machine learning?

  • It’s fast. Like, lightning fast. 
    Machine learning models analyse all your variables in every possible combination in seconds.
  • It’s more powerful.
    Machine learning models can handle a large quantity and variety of data, so you can even include external market data in your forecasts.
  • It’s more accurate. 
    Machine learning models are better at identifying patterns in data than you are. And there’s less room for human error.

If you’re panicking at the mention of complex machine learning tech, don’t worry. 

We’ve designed giniPredict for people with zero coding experience. Our machine learning models are built into a spreadsheet format, so you can run data predictions in a few clicks. No formulas or code needed.  

Read: How to level up your business forecasting with machine learning

How to run a P&L forecast with giniPredict

This P&L dataset from a global hotel chain includes income and expenses from January 2018 to October 2020. Let’s see how easy it is to run a 12-month forecast on net profit.

Go to add-ons and choose “giniPredict”.

On the giniPredict sidebar, select:

  • Target variable: Net profit
  • Forecast period: 12 months
  • Additional variables: Select all, except Total revenue and Total expenses. 
    (This will provide a more granular view of how individual variables are impacting profit.)

Click “Forecast”.

Once the machine learning models have analysed the P&L data, a new tab will appear with the forecasted values for all P&L variables populated from November 2020 to October 2021.

The forecasted values for net profit are highlighted in orange.

Below the table, the values are plotted in a graph with a 95% confidence interval between the dotted lines. 

It predicts a peak in profit over the December holiday season, and a dip around July and August 2021 — a seasonality trend picked up in the historical P&L data.

How to see what impacts your net profit most

On the sidebar, you can see a breakdown of the most impactful variables for your P&L forecast. 

giniPredict’s machine learning model calculates this by analysing each variable’s importance based on the predictive power it contributes, while also accounting for complex interaction patterns.

In this case, income from Deluxe rooms and Suite rooms impact net profit most.


How to run a scenario to see if/when you’ll need a loan

Let’s say the worst case happens, and the area goes into full lockdown for three full months from January 2021 to March 2021.

We don’t expect anyone to be paying for deluxe rooms, attending events, or eating in the restaurant, but we do expect minimal income from takeaway food and perhaps a few suite rooms. We could lower our marketing costs and lay off a few employees for this period.

Let’s go ahead and change our forecasted income values directly in the cells to see how net profit is impacted. Edited cells are highlighted in blue and appear on the sidebar automatically. 

(You can see the original value by hovering your cursor over the cell, and change it back by clicking “X” on the sidebar.)

Click “Run Scenario”.

After the machine learning models have run the analysis, an updated chart appears, showing the scenario-based forecast line for net profit in green, with the original baseline forecast line in grey to compare against.

Looks like, as long as things pick up straight away and you have sufficient reserves of working capital, you may not need a loan.

Running quick scenarios on your data like this is very helpful when it comes to preparing for the worst and best cases. Remember, growing at an unexpectedly high rate can be just as costly and also requires sufficient reserves of working capital.

With giniPredict, you can run scenarios faster than doing it manually in a spreadsheet, and you get a more accurate outcome. The machine learning model doesn’t look at any one data point in isolation, it takes the whole picture into account, giving you a better understanding of how variables interact with one another over time.

If you have any questions about giniPredict, please email us at predict.help@gini.co or schedule a one-on-one demo below.

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Contact us
Case study

How to automate P&L forecasting with giniPredict

Case study

How to automate P&L forecasting with giniPredict

The results
A major international bank found gini to be the most efficient data enrichment provider for its digital banking upgrade initiative.

In a pilot project with gini, the bank ran 50,000 transactions through our data enrichment engine.  Within 72 hours, gini had enriched 95.7% credit card transactions and 92.7% EPS transactions. 

“We were surprised just how fast gini’s enrichment capabilities are. What we expected to take 3 weeks took them only 3 days,” said the bank’s Head of Innovation and Strategy. “On top of that, they even enriched EPS transactions, which no other provider has achieved.”
Credit card
transactions enriched
EPS
transactions enriched
The results
gini introduced a successful Savings Goal feature in our PFM app that was adopted by 60% of users within 30 days of launching.
 
Our users engage with the Savings Goal feature an average of 7.4 times a month, which when compared to the once-a-month engagement of most banking apps, is a testament to its value. 

And the reviews were overwhelmingly positive, with comments such as, “Congrats on the release of the saving function, it’s very helpful and motivates me to save more!” and “Makes saving and budgeting a lot easier.”

Makes saving and budgeting a lot easier.
The challenge
Our research showed that users wanted a savings feature that automates their budgeting calculations, and shows how much they have left to spend after putting their savings aside every month.

However, no PFM apps in Hong Kong had a feature like this because it requires complicated algorithms and enriched transaction data. Without merchant names for example, it’s difficult to label recurring transactions accurately, and give the user a clear, comprehensive overview of their finances.

The solution
With data automatically enriched by our machine learning models, gini was able to build a fully functioning Saving Goals feature that resonated with users and increased engagement.

The new feature automatically calculates a monthly OK to Spend amount by subtracting the user’s total monthly expenses (past and upcoming) and Savings Goal from their total monthly income. It also has a traffic light system that warns users when it’s time to reign in their spending.

None of this was possible without first enriching the transaction data with accurate merchant names and categories.
The challenge
A recent digital banking survey showed low levels of satisfaction, with 87% of customers finding it hard to understand their transaction feeds.
My current spending history is confusing. I want to see the ACTUAL shop name.
To address this — and reduce queries — the bank planned to first replace standard transaction codes with clear merchant names and categories throughout its digital banking services. And then to increase loyalty with a personal finance app, built on the foundation of enriched data. 

However, developing the technology to transform such large volumes of transaction data was proving to be a Herculean task — one that would take years. So they looked for an external provider to help clean, structure and enrich the data accurately and quickly.

The solution
Impressed by the quality and speed of gini’s enrichment engine in the pilot project, the bank plans to integrate our scalable software into their own systems to allow for real-time data processing and enrichment. The best part is, gini’s technology is easily accessible as a SaaS solution on AWS Marketplace, avoiding the need for lengthy tech stack integration processes.

Soon, the bank’s entire customer base will have their transaction feeds transformed from confusing codes to recognisable merchant names, logos and categories. This is predicted to have a significantly positive impact on NPS scores.

Equipped with enriched data, the bank’s development team will then be able to build a competitive personal finance app with much richer features than otherwise possible.
Contact us to find out more about our digital banking data solutions
Contact us
Find out how data enrichment can help you build better PFM features
Contact us

Open banking in 2020: Are you ready?

Open banking is primed to become the new norm in Asia Pacific. But, as our research report shows, the majority of bankers in the region are not sufficiently prepared for what’s coming.

It’s time to get smart on what open banking is and how it’s expected to impact the market this year. 
gini's original research report on open banking in Asia Pacific for 2020
Download the research report
Download the open Banking 2020 research report by gini
We interviewed more than 300 finance and technology thought leaders across Asia on the industry’s readiness for open banking this year, with surprising results. 
Download our Open Banking 2020 research report to find out: 

The opportunities in store for all participants
The barriers to adoption
Who is expected to benefit most 
How institutions can generate revenue from open APIs
And more

How to automate P&L forecasting with giniPredict

By
on
Jan 6, 2021

Getting 100% clear on the financial future of your company is the number one priority of every business owner. But in today’s volatile market, P&L forecasts go quickly out-of-date.

Instead of once a month, you now have to update your P&L forecast every time something changes — which can be as often as daily. Who has time for that?

The consequence of not updating your P&L forecasts often enough, though, is dire — 82% of companies that fail do so because of a lack of understanding of their cash flow and working capital.

The fast-changing environment we now find ourselves in calls for a new approach to P&L forecasting.

Fast.

Powerful.

Accurate.

Introducing: Machine learning predictive models (wait for it) baked into a simple spreadsheet format. 

That’s giniPredict.  

Why automate your P&L forecasts with machine learning?

  • It’s fast. Like, lightning fast. 
    Machine learning models analyse all your variables in every possible combination in seconds.
  • It’s more powerful.
    Machine learning models can handle a large quantity and variety of data, so you can even include external market data in your forecasts.
  • It’s more accurate. 
    Machine learning models are better at identifying patterns in data than you are. And there’s less room for human error.

If you’re panicking at the mention of complex machine learning tech, don’t worry. 

We’ve designed giniPredict for people with zero coding experience. Our machine learning models are built into a spreadsheet format, so you can run data predictions in a few clicks. No formulas or code needed.  

Read: How to level up your business forecasting with machine learning

How to run a P&L forecast with giniPredict

This P&L dataset from a global hotel chain includes income and expenses from January 2018 to October 2020. Let’s see how easy it is to run a 12-month forecast on net profit.

Go to add-ons and choose “giniPredict”.

On the giniPredict sidebar, select:

  • Target variable: Net profit
  • Forecast period: 12 months
  • Additional variables: Select all, except Total revenue and Total expenses. 
    (This will provide a more granular view of how individual variables are impacting profit.)

Click “Forecast”.

Once the machine learning models have analysed the P&L data, a new tab will appear with the forecasted values for all P&L variables populated from November 2020 to October 2021.

The forecasted values for net profit are highlighted in orange.

Below the table, the values are plotted in a graph with a 95% confidence interval between the dotted lines. 

It predicts a peak in profit over the December holiday season, and a dip around July and August 2021 — a seasonality trend picked up in the historical P&L data.

How to see what impacts your net profit most

On the sidebar, you can see a breakdown of the most impactful variables for your P&L forecast. 

giniPredict’s machine learning model calculates this by analysing each variable’s importance based on the predictive power it contributes, while also accounting for complex interaction patterns.

In this case, income from Deluxe rooms and Suite rooms impact net profit most.


How to run a scenario to see if/when you’ll need a loan

Let’s say the worst case happens, and the area goes into full lockdown for three full months from January 2021 to March 2021.

We don’t expect anyone to be paying for deluxe rooms, attending events, or eating in the restaurant, but we do expect minimal income from takeaway food and perhaps a few suite rooms. We could lower our marketing costs and lay off a few employees for this period.

Let’s go ahead and change our forecasted income values directly in the cells to see how net profit is impacted. Edited cells are highlighted in blue and appear on the sidebar automatically. 

(You can see the original value by hovering your cursor over the cell, and change it back by clicking “X” on the sidebar.)

Click “Run Scenario”.

After the machine learning models have run the analysis, an updated chart appears, showing the scenario-based forecast line for net profit in green, with the original baseline forecast line in grey to compare against.

Looks like, as long as things pick up straight away and you have sufficient reserves of working capital, you may not need a loan.

Running quick scenarios on your data like this is very helpful when it comes to preparing for the worst and best cases. Remember, growing at an unexpectedly high rate can be just as costly and also requires sufficient reserves of working capital.

With giniPredict, you can run scenarios faster than doing it manually in a spreadsheet, and you get a more accurate outcome. The machine learning model doesn’t look at any one data point in isolation, it takes the whole picture into account, giving you a better understanding of how variables interact with one another over time.

If you have any questions about giniPredict, please email us at predict.help@gini.co or schedule a one-on-one demo below.

Schedule demo banner


Contact us

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