Machine Learning

Machine Learning in the Financial Industry

AI and machine learning have taken hold in the financial services arena in a big way. Many banks are using complex algorithms to assess loan risk, and approve or deny based on their conclusion alone. The traditional loan officer is no longer needed, other than to pass along the decision to the client.

Robo-advisors are set to disrupt the industry in a way never before seen, and there are already plenty of them plying their trade online. Some estimates have them controlling about $2 trillion in assets by 2020. They could be managing 17% of total assets within the next five years, according to others.

Platforms like Vanguard VPAS, Wealthfront, and Betterment make it easy to harness the power of AI for your finances.

Machine learning systems evaluate your online presence for clues about your values, beliefs, and personal ethics to further align your portfolio with everything about you. The system can make real-time portfolio adjustments based on market fluctuations and changes in personal circumstances.

And let’s not forget about the powerful prediction and probability algorithms that are used to make decisions about this stock over that stock, or the best time to buy or sell. AI and machine learning software can process thousands – if not millions – of data points in the blink of an eye. You’ll never miss a once-in-a-lifetime opportunity again.

It doesn’t stop there. Intelligent virtual assistants and service reps can help customers with a wide variety of questions and problems. The national bank of Sweden recently deployed Nina, an online assistant that achieved 30,000 conversations per month, and first-contact resolution of 78%.

Machine Learning in Business and Marketing

General business is not immune to the AI invasion.

An MIT survey of 168 large companies found that 76% are using machine learning technologies to assist their sales growth strategies.

MarketMuse is banking on AI taking over your content marketing strategy, too. Its product uses AI and machine learning to determine the best topics to write about, and how to cover them completely. It then analyzes your existing content inventory to create a comprehensive content plan designed specifically for you and your website.

Other use cases include recommendation engines like those seen on Amazon, the New York Times, and Netflix. These learning algorithms examine your past activity or purchases, and compare them against millions of others to (fairly) accurately predict what you might like to buy or read or watch next.

Google’s own RankBrain uses AI and learning to better process search queries and return more relevant results. It gets smarter with each inquiry.

A service like SailThru apply machine learning to your email marketing. It analyzes customer behavior to identify the perfect time to connect and engage, delivering double-digit increases in revenue for its clients.

Intelligent chatbots to communicate and assist customers 24/7. Incredible data-backed insights and customer profiles. Increased productivity. Higher profits. When it comes to AI and ML in business, the sky’s the limit.

Machine Learning in Healthcare

Diagnosis, prescription, treatment, and even medical procedures themselves are getting the machine learning enhancement seen almost everywhere else.

Computers can quickly compare symptoms and genetic details against millions of possible diagnoses, determining the probability of each and the most likely cause in a fraction of the time it takes a human doctor.

New biometric sensors and massive databases allow machines to process, analyze, and compare data that could lead to faster diagnoses for many diseases and conditions.

Beyond that, robot surgeons can perform operations with an unwavering “hand,” near-perfect precision, and virtually zero possibility of error. That means smaller surgery scars, faster healing, less time in hospitals, fewer (if any) mistakes, and ultimately, less expense. That’s a major win for patients.

Intuitive Surgical Inc. has a robotic laparoscopic surgery system called da Vinci that performed nearly 600,000 procedures in 2014 alone. It’s not a replacement for a surgeon (yet), but rather a robotic extension of his or her skills.

IDC predicts that up to 30% of care providers will be using cognitive analytics to assist with patient data by 2018. That’s almost a third of healthcare workers turning to AI for help and suggestions within the next 12 months.

Lumiata, for example, is bringing AI to predictive medicine. Its motto? “Health can be unpredictable. We’re using AI to change that.”

Lumiata’s program simultaneously looks at patient records, family history, ongoing studies, medical journals and articles, and other unstructured data sources to make educated predictions about patient health in the short and long-term.

It combines data and medical science, which basically means it learns in much the same way a doctor does.

And it doesn’t stop after treatment. Some practitioners are using AI bots like NextIT as a virtual doctor-on-call with patients after they’ve gone home. It can answer complex questions, send reminders about medications and recommendations, and even ask about symptoms and side effects. It then relays everything back to the doctor.

Diagnostics and imaging. Patient monitoring. Drug discovery. Treatment. Follow up. Industry advancements. New techniques. There are no fewer than 106 startups working with AI in the healthcare field today, covering everything across the full spectrum.

Machine Learning in Education

Hotly contested and debated by those in and outside of the industry, the idea of robots replacing teachers seems, too many, ludicrous. After all, teachers explain more than just maths and science. They’re responsible for modeling socialization skills, proper behavior, self-esteem, confidence, self-regulation, and a host of other life skills not covered in a textbook.

Can a robot do all of that? Doubtful, but it remains to be seen.

What we do know, though, is that technology has already moved into the classroom in a big way, with interactive smartboards, laptop computers, wifi, tablets, voting devices, multimedia presentations, and so on.

AI and machine learning would allow for faster identification of learning disabilities. Student work could be collected, compiled, analyzed, and compared against known difficulties. Diagnosing a learning obstacle can currently take years, and is often missed completely.

Intelligent tutor systems (ITS) like AutoTutor can actually evaluate student steps and problem solving to identify misconceptions and gaps in students’ knowledge. They can guide, give hints, and provide feedback on a student’s performance.

Online learning has been broadened by machine learning systems that not only allow teachers to reach students far away – possibly beyond the reach of a traditional classroom – but also work to highlight their weaknesses and areas that need attention. A teacher can efficiently “teach” a larger student body without sacrificing quality or attention.

Outside of the traditional classroom, language programs like Carnegie Speech and Duolingo harness the power of automatic speech recognition (ASR) and natural language processing (NLP) to listen for speaking errors and correct them with users. With them, students can master even a native accent without ever having a native instructor.