Artificial intelligence is maturing as more and more industries look to AI for efficiencies, but is it right for you? Yes it can be used for everything from customized product marketing and entertainment offerings to high-speed financial transactions, precision medicine, and more. Even manufacturers are leveraging it to improve efficiency, quality, and sustainability, but how realistic is it for your business, and is the timing right?

Despite its widespread and growing use, many are confused about machine learning and its relationship to artificial intelligence. Others wonder how to harness the value of machine learning with limited budgets and a lack of staff experience.

Machine learning refers to a specific subset of artificial intelligence, which uses data to teach computers new skills.

Let’s start with a clarifying definition. Machine learning refers to a specific subset of artificial intelligence, which uses data to teach computers new skills. At its core, machine learning involves fine-tuning statistical models while iterating through large datasets (extremely large in many cases!). The model improves with each new data point, allowing the computer to make more accurate predictions for subsequent data points.

This learning process, based on algorithms, is best suited for:

While not the only way to teach new tasks to a computer, machine learning is becoming increasingly common in our age of big data. Consumer expectations—better, faster, and cheaper—also are creating pressure for companies to adopt machine learning or risk obsolescence as others use it to competitive advantage. But just like the adage: “Garbage In, Garbage Out,” a machine learning outcome is entirely dependent on the quality of the data it is given.

Successful machine learning happens when the right questions are asked – and answered – in advance. Without proper planning, failure is practically guaranteed. In fact, recent research found that close to 85 percent of AI projects fail to deliver on their promises.

This Intertech Executive Brief will highlight some prominent industries effectively using machine learning today, along with some guidelines for evaluating the potential usefulness of machine learning in your organization.

Executive Brief

by Tom Salonek

Financial Services

Have you heard of COIN (Contract Intelligence)? It’s a machine learning program used by JP Morgan to scan and analyze commercial credit agreements and other documents. COIN is nothing short of miraculous, completing 360,000 hours of human work in seconds. That’s just one example of how machine learning is turning the financial services industry upside down. The availability of vast amounts of data combined with more affordable computing power is driving this trend.

Executive Insight

From money laundering prevention, fraud detection, and automated trading systems that run entirely without human intervention, to improved risk management and better chatbot experiences, financial institutions around the world are investing billions into finding ways to use machine learning. Benefits include increased profit through reduced operational costs, increased revenues, increased customer loyalty due to better customer experiences, and better compliance and risk management.

Additional Consideration

The use of machine learning in the financial world also involves risk. A recent report by the World Economic Forum, The Physics of Financial Services – How artificial intelligence is transforming the financial ecosystem, warns that as big players adopt AI and machine learning applications, they are creating a “self-driving AI finance world.” The report also expresses concern that this new financial world will be “centralized with only a few networked players.”

Loans and Mortgages

Nearly one-third of all mortgage lenders are using some form of machine learning or artificial intelligence to assist with mortgage originations. Research by U.S. mortgage giant Fannie Mae has found that lenders are using machine learning to fight fraud and connect borrowers with the most suitable lenders faster and more efficiently.  

Realize Cost Savings

Lenders also are using machine learning to reduce the time and complexity involved in the mortgage approval process. What used to take weeks or months can now be done in under ten minutes thanks to automated approval processes. Equally impressive is the cost savings that automation makes possible. One Silicon Valley startup, Lenda, reports saving their average customer $48,000 on fees and interest compared to those using traditional mortgage lenders.

Executive Insight

Experts believe the use of machine learning in the mortgage industry is in its infancy. Expect to see even more impressive improvements as organizations develop increasingly sophisticated algorithms. Consumers will benefit from more precise methods for determining creditworthiness, especially young people and those without a traditional credit history. Machine learning already is mining more data, such as social media history, to determine creditworthiness

Additional Consideration

Retail

Ever look at a product online and then, sometimes in a matter of minutes, receive online ads for that very product? Machine learning, no doubt, played a role by learning about your consumer behavior and individual preferences. While some find this experience unnerving, retailers are using it to predict and encourage purchases.

Executive Insight

Here’s a short list of how machine learning is transforming the world of retail:

• Offering customers personalized product recommendations

   

• Offering special prices in real-time to boost sales

   

• Providing better inventory planning

   

• Offering faster and more efficient deliveries

   

• Providing more accurate sales predictions

   

• Perfecting app user experience and optimizing website content

   

• Enabling better customer segmentation based on previous behavior

   

  

Additional Consideration

Conclusion

Machine learning is leading us toward new frontiers in many industries. Yet one thing will not change: the need for humans to strategically analyze opportunities, manage the algorithms, and communicate the results in ways that other people can understand and appreciate. Working with subject matter experts and data scientists, your IT staff is the vital link between potential and realized opportunities with machine learning. When correctly deployed by an experienced team, machine learning can produce transformative insights and bottom-line results.

Wondering where to start?

The following three practical applications of machine learning are in common use today. Think about where your company or organization has a problem that could be solved using one or more of these machine learning applications:
 

Classification


If you have a large dataset where a record needs to be correctly sorted based on multiple related – but not necessarily causal – factors, machine learning can be powerful. One example: oil and gas companies are using regression models to predict the likelihood of success for various potential drilling sites using testing data about the surrounding area and geology. They use all the available data to classify and triage drill sites into various categories. In the retail space, companies are using similar models to categorize shoppers by cross-referencing credit card data with order totals, types of purchases, and order frequency. Retailers can classify shoppers based on their needs and preferences, and then market to them accordingly.

 

Computer Vision


When a computer needs to interpret images, machine learning plays a starring role. Luckily, many of these models are already well-developed and open source. They’re ready to be trained and deployed quickly for many computer vision applications. Machine learning is driving the advent of autonomous vehicles, as well as facial recognition technology in most smartphones.

 

Speech & Text Language Processing

The use of machine learning allows them to translate speech to text and even predict the sentiment and intention of your speech by learning the meaning of words and phrases. This ability enables intelligent chatbots to offer context-aware help and recommendations as part of customer support.

One additional consideration

Machine learning cannot solve every problem, and developing custom machine learning applications is both expensive and risky. But there’s good news too! For most companies, deploying a pre-trained model or running simple statistical analysis are low-hanging fruit you can harvest before taking a deeper machine learning dive. When correctly deployed by an experienced team, machine learning can produce transformative results.

About Intertech Executive Brief

Intertech publishes original articles, reports and periodicals that provide insights for business leaders. Our goal is to draw upon research and experience from throughout our professional services organization, to include consulting and training, as well as coauthors in academia and business throughout the world, to advance the understanding of important principles of interest to executives throughout the IT world.

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