- The back-office, where you'll find HR,
F and A, and supply chain processes, to name a few,
is full of swivel chair integration,
where employees tend to manually receive
and transcribe faxes, copy form data,
execute rote transactional processes,
or perform repetitive tasks.
This makes it the ideal part of a business
for large-scale automation.
Let's look at examples of digital operations
in the back-office, beginning with some RPA use cases.
A salient example is how RPA can be deployed
for HR processes, across the entire employee
hire to retire lifecycle.
For instance, I've seen it used
to automate salary estimation, vetting,
and candidate reporting to support talent acquisition teams.
Each of these tasks are usually repeated,
again and again for each candidate,
so automating them can save hiring managers
a lot of time and frustration.
There are also opportunities in payroll,
where RPA can be used to perform payroll calculations,
eligibility checks, and pay integration
across multiple HR systems.
Another common area of opportunity
is in finance and accounting.
You might have a high volume calculation-heavy task
like account reconciliation,
which not only takes a lot of time to do,
but is also prone to human error.
These sort of processes lend themselves well to RPA,
which can reduce turnaround times drastically,
while improving accuracy.
Other technologies also come in handy in the back-office,
and they're often paired with general tools like RPA
to automate end-to-end processes.
If you need to convert analog forms to digital data,
technologies like optical character recognition
or OCR can help digitize written text
with a relatively high degree of accuracy.
This works best with structured, computer generated images,
because of the generic samples that OCR engines run on.
If you're working with semi-structured forms
that out of the box OCR can't handle,
you might opt for intelligent character recognition,
or ICR, which trains a neural net on completed examples
to increase the accuracy of document transcription.
ICR is especially helpful
if you're trying to digitize handwritten data
or forms that tend to vary in format,
such as loan applications, insurance claims forms,
or order forms.
ICR is a good example of using AI for digitizing work.
You might also find AI useful for classification problems
like fraud detection.
Banks have already begun applying machine learning
to processes like anti money laundering,
know your customer, and with anomaly detection
of their consumer spending data.
I could spend all day discussing examples like these,
(laughing) but I'll spare you.
I hope the ones I covered here gave a general sense
of the technologies being applied today
and the types of processes that are ideal for automation.
Each discrete process in the overall landscape
of processes interacting, are critical building blocks
of a full digital operation.
Now let's talk about what happens
when you glue it all together.