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Machine learning and data extraction tools promise to change the future of SMSF admin.
SMSF accounting efficiencies – where to next?
07 October 2021
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3 minute read
Accounting practices across the country waste a significant amount of time manually sorting paperwork and transcribing data into various systems. Advances in machine learning, however, automate much of this work, promising to give accountants more time to focus on client value.
We see basic examples of this through accounts payable or staff reimbursement systems that take digital invoices or photos and extract details into software. And yes, with just one simple document type, this technology is easy to implement. Commercial offerings in this area usually work very well and save firms a lot of time.
Our Innovation Lab has been researching how a similar approach can be applied to streamline superannuation accounting and compliance. The documents we work with aren’t just simple invoices though - there are countless types of documents, each with their own data sets to identify and extract.
We’ve looked to extend the drag and drop mode by developing a batch upload system through either email or bulk upload. The documents are then processed and the data is ready for use - just like a data feed but without the overhead of obtaining authorities.
Over the last few years we’ve been testing both methods to streamline SMSF administration within SuperMate. Annual Tax Statements can be processed through the drag and drop method which has proven significantly more efficient when entering complicated tax components.
More recently, we’ve been building a brand new platform - DataHeroTM which works as an email address you can simply send all the key documents to. They’re automatically identified and the data is extracted and saved in a central location.
As the extraction engine of this platform continues to be optimized we’ve been performing real-use trials of it in SMSF administration across five key document types. During the trial, just shy of 160,000 bank transactions were automatically entered straight into SuperMate, saving significant processing time.
Now that DataHeroTM is ready for wider use, we’re building it into the next generation of SuperMate so that our clients can benefit from this exciting technology. Machine learning document processing is just one of the ways SuperMate will supercharge your practice efficiency.
The next generation of SuperMate is launching in early 2022. To stay up to date, register your interest here.
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