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Erick Weitkamp: Hello, everyone. Today, I want to give
you a quick overview of a
few features that are included in
Dynamics 365 Finance Insights 2020 release wave 1.
Key investment areas for this release are
customer payment predictions that forecast which
customers will pay on time, late, or very late,
and cashflow forecasting that provides insights into
future cashflow based on historical data.
Let’s see how these features work.
Cash collections is generally a reactive process, but with
this release, we’re applying AI to customer history so
that we can develop a prediction model for when
customers will likely pay their invoices. And we can start
proactively encouraging customers with payment issues.
Let’s set this up, see how it works.
We’ll start at the setup screen and enable Customer
Payments Insights.
Now we could set the parameters for which data fields
to use and how far back to go in developing the prediction,
but we’ll let the model do that. And we also set the line
between when a customer is late or very late. Yeah.
We’ll use the default 30-day interval and select
create a prediction model.
Once the model is published, you get an accuracy score.
And when we go to credit and collections workspace,
we see that two new titles have appeared—customers
predicted to pay late, of which we have six that have
greater than 50% chance of paying late,
and transactions predicted to be paid late, of which we
have 558 that have a greater than 50% chance of being
paid late.
Now think for a moment about what we just did.
With a single click of a button, we have deployed the AI
model, trained it, published it, and then predicted based
on it. Now no developer
interaction was needed. All of this
was done by a power user with one click of a button.
So, now we click on the
transaction tile and we see a new
list page that lists all of the open invoices with the
on time probability.
For example, the first item has a 27% probability of being
on time. We select it and on the right you can see more
details about this prediction. There’s a 27% chance that it
will be on time, a 40%
chance that it will be late, and a
33% chance that it will be very late.
So, to help you make sense of this prediction, we also
list some information about the model, including top
factors used in the model, the model’s performance
and accuracy. And we include customer insights and
the customer’s payment history.
Now if you’re not happy with prediction accuracy,
you can select improve accuracy and go into the AI
Builder to adjust the data that the model’s using to
develop predictions. Like our initial setup of the model,
we aren’t using code to extend the AI model, just the
power user’s judgment and a few clicks. And this allows
you to easily continue the shift from reactionary to
proactive customer collections.
Now let’s look at cashflow forecasting. There are three
main issues here. First, data’s in silos. And this is why
people often use Excel to forecast cashflow because they
can copy data in and then work with it.
Second, forecasting is often very rudimentary and it’s
based on tribal knowledge, as it’s hard to develop and
deploy the models required.
And third, it’s very hard to measure your forecast
performance. We talked about how hard it is to model
forecasts, well, it’s even harder to measure the forecast
than it is to develop them. Now to make this work,
we’ve developed two data collection features to feed the
forecast model.
First is a touchless data collection from your various
accounting, finance, and sales systems. And the second
is to consume data from Excel files so that we can
leverage the forecasting work that you’ve already done.
Now as soon as you land on a cashflow hub, you see the
summary of daily positions.
Inflows in dark blue bars, outflows in orange bars.
And the bank balance is the light blue line.
You also see your customer collections information,
for example, how much of this is overdue.
And over here, we have a vendor payments information.
And in the bottom section, we can view the major
bank accounts.
The cash position page is a short term forecast and it’s
based on the customer’s payment prediction and the
vendor payment prediction. You can view this by day,
week, month, or quarter. The alternate is just to take
due dates on invoices at face value. Again, in the bottom
section, we see the bank accounts and the documents
that are used to forecast the short term cashflow.
Now the cashflow forecast page is a long term forecast
based on historical balances. Now at any time, you can
create a snapshot and then with a single click compare
forecasts against actual performance or other snapshots.
So, here you can see the variance between the actual
performance on the left of each set of bars and
predicted performance from the snapshot on the right
and then use this comparison to make your future
predictions more accurate.
As you’ve seen, the 2020 release wave 1 update to
Dynamics 365 Finance Insights provides you with
customer payment predictions that forecast which
customers will pay on time, late, or very late,
and cashflow forecasting that provides insights into
future cashflow based on historical data.
Now for more information,
use these specific links or go to
the release overview guide.
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