It may seem overwhelming, but the numbers are in, with data as the new “oil” and advisory work is the new “compliance”. In order to deliver on new client demands, having the right software and on-the-money data are musts.
Why? Because this data provides you with near real-time financials so you can:
- Move seamlessly into other services like cash flow or tax planning
- Build your advisory muscle by showing clients the business impact of their financial data
- Charge a premium for the high value services you’re providing
Imagine if you could advise your restaurant clients on the best methods of online ordering and delivery based on national and regional data. You could literally revolutionize their businesses through the application of data analytics and tech! Oh, and that’s on top of all the internal advances your firm could make by incorporating machine learning (ML), workflow technology and smart data.
Data validation and transaction identification are time sucks robbing you of otherwise useful hours. However, there is software that automates these tasks. For those of you who are still software resistant, let’s take the mystery out of the way software is being used to identify transactions and find trends in data.
The Magic That Makes Automation a Reality
Generally speaking, engineers write algorithms to tell machines how to look at multiple data points, find the similarities and apply probability to group the data. Probabilistic modeling is one of the key layers of intelligence applied to client data, which aids tremendously in the identification process.
Probabilistic models are more successful when the information they’re looking for has occurred many times in a firm’s historical data. In order for this to happen, a lot of data is needed.
Fortunately, larger CPA firms (like the Big Four) have more than enough data to provide a solid foundation for probability. The same holds true for automated platforms, like Botkeeper, though. By pooling data from all businesses in the system, accurate predictions are possible.
Still confused with probability modeling? Think of it as writing an algorithm to estimate the odds of a coin flip. If you flip that coin twice and one time it’s “heads” and the other time it’s “tails”, would you assume 50/50 odds? How confident would you feel in that assumption? How much more confident would you be if you flipped that coin 100 or 200 more times to support the 50/50 assumption? The more data, the more accurate prediction you will have.
Predictions Come with Confidence Levels
So how does software predict the correct categorization of a transaction? It requires both probabilistic modeling and ensemble learning, another type of artificial intelligence. The ML behind accounting software:
- Identifies novel cases and then sets about tracing similar cases, based on description and amount, in the general ledger.
- Interrogates the transaction by applying a series of questions that range from a simple yes/no to more complex ones related to dollar amount, vendor name and occurrence.
- Produces logic around transaction identification that looks a lot like a web of transaction similarities and differences.
For user peace of mind, transaction identification comes with a confidence level. If that confidence falls below 90 percent, the transaction is flagged for human approval or re-assignment. This is how the ML learns and gets better at its job. And here’s the key, this all happens in seconds!
Don’t Be Fooled by Limited Identifiers
Think about the rules you place in QuickBooks Online around how a transaction should be treated. They aren’t fool-proof, right? That’s because the data feeds are set by humans and have limited identifiers. Those identifiers are different based on a number of subjective items, such as a location or even an extra character in a description. An extra character can create a distinction in the vendor name and then rules don’t stick.
Now consider an accountant who studies a client’s historical data and figures out that every time a specific airline appears, he knows the transaction is a travel cost. He then creates a rule in QBO that’s specific to that client and specific to that account category.
Using the same data the accountant had, automated accounting software will automatically apply logic to recognize the same transaction as travel, without a human telling the software to do so. The ML remembers everything and can use that knowledge and logic to identify thousands of transactions without a single human created rule.
The following are some of the tasks that automation can do for you:
- Categorize and reconcile cash and credit card transactions
- Fetch bank statements
- Track workflow throughout the bookkeeping process
- Enable work from home or an anywhere office
The Snowflake Effect
No two of your clients are exactly alike. They each have unique needs and different charts of accounts, accounting processes and specific policies. No one set of rules can be universally applied to them, however, automated accounting software can interpret the client’s history to find patterns and make predictions just like an accountant would.
What is a game-changer is, unlike an accountant who can only retain and remember so many nuances, client cases and historical data, software is capable of remembering and retaining an infinite amount of data. It also sees and identifies patterns in data sets that would be unrecognizable to humans.
Bringing the right automated solution into your CPA or accounting firm can make a big difference to how your firm conducts its business, differentiates itself from the competition, adds strong advisory work to its deliverables and positions itself for future success. In the end, you will be able to:
- Build capacity so you can focus more on your clients with higher level advisor work
- Manage work and remote workflow
- Attract the next generation of talent who embrace tech that can to the grunt work
So, what are you waiting for? Look around your firm and start identifying activities that are occupying your firm’s time and ask, do these activities add value? If the answer is no, there is likely an automated solution in the market today that can help.