Machine Learning

Why every small business should use machine learning?

Small businesses and large enterprises all want to jump on the machine learning bandwagon. Big names like Google and Amazon use technology like this all the time. While smaller companies fear that machine learning is out of reach for them. But it doesn’t have to be.

Machine learning (ML) is an application of AI that uses statistical models and big data to make predictions about future trends.

For example, have you ever noticed how Google Photos can find images of just about anything (let’s say, a horse), without you having tagged the photo in advance? That’s because Google has fed its ML algorithms with thousands and thousands of photos of horses — the technology is able to identify the key attributes of the image that make it a horse, and then notice if future photos have those attributes too.

What’s cool is that ML doesn’t apply just to photos of horses — it has applications across just about every industry, including business and finance.  ML algorithms get smarter as they go, and they can make your business smarter too!

Small businesses can use machine learning too

To avoid being outsmarted by digital competitors, small businesses know that they need to seek out the latest tech solutions. In recent years, these companies have successfully implemented SaaS products, cloud-based software and other digital solutions. Machine learning is just the next step in updating your business.

It’s true that there have been real obstacles to implementing ML solutions for small businesses. R&D in machine learning is costly and the digital savvy needed to create and train your own model is intimidating. Despite these obstacles, businesses are still trying to invest, because 84% believe that adopting AI will lead to greater competitive advantages.

Thankfully, the concerns and costs of small business ML integration are now mostly obsolete. Machine learning technologies are starting to be integrated into every industry due to a rapid drop in price as competitors have entered the market and open source frameworks have become available. The ROI is high because these solutions save you time on mundane tasks and turn data into insight. Integrating AI into business processes can help ensure more timely and more accurate decision-making.

Pre-trained ML models are helping small businesses to build solutions that used to only be available to large enterprises. Because machine learning technology has been in development for a couple of decades now, you don’t have to start from scratch. Open source algorithms and tools are more widely available now.

While you can still train your own model, you don’t need to. Tech giants with huge R&D resources often sell ML software as a service. These kinds of affordable, informed ML solutions are a great asset to small businesses.

Commonplace uses of machine learning in business

Some industries are ripe with big data sources, perfectly suited for new machine learning models. Whether or not this is the case, all businesses can integrate ML in commonplace ways.

Marketing: Machine learning technology helps provide actionable insights and improve marketing decisions. In 2018, 43% of marketers use AI and machine learning to improve predictive models and exceed revenue goals.

Automation: One of the best perks of ML is automating repetitive tasks and increasing productivity. Many companies already use AI-informed email and paperwork automation. Chatbots are another great application of affordable ML solutions that provide better customer service and adaptive responses.

Security: Because machine learning tracks patterns in data, algorithms are great at identifying suspicious account behavior and detecting fraud. These tools can be used in financial monitoring and network security.

SEO: Google’s algorithms are largely informed by machine learning, so companies must use SEO to keep up. Tools like Google Analytics and Ahrefs use AI technology to improve their pattern tracking and offer fresh feedback to their customers.

Customer suggestions: Almost everyone interacts with ML daily through personalized suggestions on Netflix or Spotify. These kinds of predictions can also help to personalize ad campaigns and suggest next purchases for customers (e.g. “you might also like…”). By identifying patterns in user behavior, ML allows you to determine which individual advertisements are most likely to be relevant to specific users.

Other: Financial companies use ML in robot-advisory retirement planning, underwriting, credit scoring, and algorithmic trading. HR departments can leverage ML for recruiting and retaining top talent. Smart assistants, like Google Mini and Amazon’s Alexa function using AI technology.

Machine Learning in Accounting

Accounting manages tasks that are repetitive, predictable and data-heavy. Because of this, bookkeeping software lends itself perfectly to machine learning solutions.

The majority of software products within our industry have already adopted AI technologies in order to improve and automate daily functions. ML makes accounting faster, more accurate and more insightful. The consulting firm Accenture describes how “machine learning and adaptive intelligence are becoming part of the finance team at lightning speed.” Others predict that by 2020, accounting tasks, such as taxes, payrolls, and audits, will be fully automated.

Machines have already aided accounting companies by:

  • Addressing financial queries using AI chatbots
  • Managing expenses with account tracking and alerts
  • Making predictions about business decisions
  • Simplifying invoicing by matching items to amounts
  • Automating bank reconciliations and categories

How do we use machine learning?

As a cloud-based accounting software company, we’ve integrated machine learning to facilitate such tasks. We use ML to compete with products like QuickBooks 2019. Our algorithms help customers by automatically categorizing transactions based off of historical trends. The more data you enter, the smarter our software becomes—making your bookkeeping more efficient and automated.

Additionally, we use data-driven intelligence to support users in improving their invoice processes and business health.  Based on millions of data points, we provide “smart scores” that help clients make their companies more competitive.  This allows Zip Bookers to see what’s working and what’s not, and then use our observations to become even better.

“A true continuous audit”

Another area where accounting is improved by machine learning is auditing. Machine learning models can track errors in books and auditing will be able to occur in real-time.

Forward-thinking leaders in accounting envision a future where auditing happens in real time, and each relevant party is informed along the way— “a true continuous audit.” Rather than analyzing a select sample, auditors will be able to leverage AI to examine 100% of a company’s financial transactions. ML technology will analyze the data, recognize any anomalies and compile a list of outliers for auditors to check.

Machine learning will not replace human intuition

Machine learning offers great benefits for every industry, including the jobs of accountants and auditors. However, with any new tech, there is the fear that machines will take human jobs. While ML will minimize data entry and bookkeeping, artificial intelligence can never replace human intuition, insight, and relationships.

Accountants are already finding it hard to bill for services like data entry and auditing, many of which can be done by bookkeeping software. Rather than try to scramble to keep these tasks to themselves, accountants should embrace the supercomputer. By implementing machine learning within your accounting practice, you can move away from being a number-cruncher to be a trusted advisor.

One accountant posited that “for the first time perhaps in the history of the profession, accountants will genuinely be valued for their analytic and deductive acumen, as opposed to their fluency around spreadsheets.” The super-efficient, super-accurate assistant of machine learning technologies allows accountants to diversify their offerings, develop deeper insight and bring greater value to their clients.

AI will never be able to replicate human intuition, but it can make businesses more efficient. Whatever industry you are in, machine learning can automate the menial and allow you to focus on the meaningful.

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