BLOG VIEW: Machine learning grew in popularity in the late 2000’s, when Google and others demonstrated the importance of data and how machine learning could put it to work.
With the explosive growth of data that followed, it became apparent that the traditional ways of utilizing it were no longer working. Something had to change. This led to the notion of “big data” – and how data can be turned into gold.
In recent years, machine learning has been playing a larger role in the mortgage industry, where it is used for a wide range of business needs, such as predicting purchases, defaults, clicks or nearly anything else. Although we know a lot about how machine learning can help the mortgage industry, there are numerous misconceptions about machine learning that should be addressed.
- Machine Learning Can Do Everything a Person Can Do
This is arguably the most common misconception about machine learning. While machine learning is very advanced, it struggles with certain judgements. There are nuances that an algorithm simply cannot pick up on.
Machine learning also has a hard time engaging customers at an advanced level. Many people fear its capabilities because they believe it will replace people’s jobs entirely, but this is simply not true. Machine learning should provide aid and support to employees, not replace them.
Finally, machine learning is not visually creative – at least not in the way that a human is. While it can deal with huge amounts of data, it cannot deal with complexities that are not encoded therein. This is another nuance that this technology has yet to pick up on.
- Machine Learning Always Will Help You Succeed
It’s the same with any technology – just because you implement it does not mean you will instantly see its effect on your bottom line. You need the right tools, the right applications, and the right people. When applying machine learning, you need both technical and business people working together. The business-minded people understand the strategic problems that need solving and can inform the right applications. The technical-minded people can then identify the right tools for the job.
Further, machine learning is not always the solution you need for every given problem. Sometimes a simple business rule is all that’s needed.
- Big Algorithms Are Better
Bigger is not always better. With machine learning, you need to start small and get sophisticated later through a test and learn process. Simpler algorithms are easier to implement and make sure you can execute quickly to create value and efficiencies. You must crawl first, then walk before you’re able to run.
When starting out, you may not have the time or tools to execute on big, complicated algorithms. It’s best to keep your eye on the prize, which is helping the customer and executing well on strategically important initiatives. Machine learning is simply a means to that end – not an end on its own.
- Only Fortune 500 Companies Can Afford Machine Learning
There are opportunities everywhere. If you don’t have a big data science team, there are analytics and infrastructure services for you to leverage. With the wide availability of cloud computing, it’s easier for organizations to plug and play with machine learning tools and only pay for what they need.
- You Must Own Everything to Do Machine Learning
You do not have to own all the tech and infrastructure or employ the team that powers your machine learning. It is completely normal to have external teams or vendors managing your infrastructure for you. This makes your options for machine learning incredibly flexible.
- Machine Learning Will Cause a Revolution
Machine learning is incredibly innovative especially for the broader market and society, but it will not change your business or customer management entirely. Machine learning will make progressive and iterative improvements, help you understand customers, and take meaningful action toward them. These small changes and insights are what will help change your business for the better.
Machine learning can be a great tool for business initiatives, as it can put all your information to work for you. Just be mindful that like every piece of technology, there are strengths and opportunities. Once we break through the misconceptions of what machine learning is and is not, it will be able to be applied to its full potential.
Alex Reda is senior manager of data science at Enact (formerly Genworth Mortgage Insurance), where he leads a team that helps business practitioners and leaders leverage data-driven decision making through machine learning and advanced analytics methods.
