Machine learning and Artificial Intelligence are two buzz terms that could have a profound effect on the overall customer experience related to financial institutions. These technologies are picking up steam as they make their way toward the mainstream in financial services, ultimately, making everything faster and more intuitive. Machine learning is a subset of artificial intelligence that enables computers to learn without being explicitly programmed. With machine learning, computers can analyze new information and compare it with existing data to look for patterns, similarities, and differences. David Gilvin, partner, banking & financial markets leader, IBM Digital Consulting, IBM, discussed this burgeoning theme during a session titled, "Machine Learning & Artificial Intelligence Powering Next Gen CX in Financial Services," at the recent Money20/20 Conference in Las Vegas.

Expense Saving Bots help you save and cut down extra spending in your day to day life. One of the Expense Saving Bot examples is "Trim". Trim is a Finance Chatbot that helps you manage your extra subscriptions, check out bank balances and set up spending alert. Trim can be found in SMS or Facebook messenger like other Chatbots and not in any app. Trim has helped users save $6,322,896 in total.

Choosing features to improve a performance of a particular algorithm is a difficult question. Currently here is PCA, which is hard to understand (although it can be used out-of-the-box), is not easy to interpret and requires centralizing and scaling of features. In addition, it does not allow to improve prediction performance for a particular outcome (if its accuracy is lower than for others or it has a particular importance). My method enables to use features without preprocessing. Therefore a resulting prediction is easy to explain.

Efforts to understand suicide risks can be roughly clustered into traits or states. Trait analyses focus on stable characteristics rooted in and measured using biological processes (Costanza et al., 2014; Le-Niculescu et al., 2013), whereas state analyses measure dynamic characteristics like verbal and nonverbal communication, termed "thought markers" (Pestian et al., 2015). Machine learning and natural language processing have successfully identified differences in retrospective suicide notes, newsgroups, and social media (Gomez, 2014; Huang, Goh, & Liew, 2007; Matykiewicz, Duch, & Pestian, 2009). Jashinsky et al. (2015) used multiple annotators to identify the risk of suicide from the keywords and phrases (interrater reliability .79) in geographically based tweets. Thompson, Poulin, and Bryan (2014) and Desmet (2014) used text-based signals to identify suicide risk that ranged from 60% to 90%.

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Today, consumers have access to modern wearables and smartphones as well as AI and machine learning-powered platforms like BioBeats Hear and Now. Consumers can take it upon themselves to reduce their stress levels and improve their overall quality of life. To be clear, stress management and other types of healthcare platforms/applications are not intended to be replacements for regular visits to a primary care physician (PCP). There are often cases where high blood pressure, obesity, heart disease, and other stress-related health issues not only require regular visits to a PCP, but also specialized treatments and medications. With that said, consumers can use AI and machine learning-powered stress management platforms to be proactive about stress; changing behaviors and reducing stress levels on their own using focused techniques such as clinically validated breathing exercises, biometric feedback, mindfulness exercises, and meditation.

Jean Brillat-Savarin was a 19th-century French lawyer famed for his writings on gastronomy. In his most famous work, he said: "Dis-moi ce que tu manges, je te dirai ce que tu es." Or "Tell me what you eat and I will tell you what you are." This idea--that you are what you eat--has become increasingly popular. Since Brillat-Savarin's time it has been used as the title of various cookbooks and health guides; for some it is a way of life.

As healthcare systems strive to realize IOM's vision for continuous improvement in care delivery, many are recognizing that they have outgrown their data management and reporting capacity. Those that have turned to new machine-learning approaches have found they can expand capacity and capabilities while reducing administrative burden on clinicians. Here's an example of how one health system used machine-learning tools to improve care delivery for intestinal surgery: Until recently, the health system's surgical services team used traditional methods of hospital data analysis to inform their creation of order sets, protocols, and provider and patient education materials spanning the pre-op, intraoperative and post-op phases of care. Then they applied a "machine intelligence" platform that pairs machine learning algorithms with topological data analysis (TDA)--a mathematical process that uses shape as an organizing principal for understanding complex data. By giving visible form to their data, the health system was able to replicate and validate years of analytical insights in a matter of days.

They say what happens in Vegas stays in Vegas, but I keep telling everyone I know about the remarkable innovations I saw at IBM World of Watson 2016 conference, held from Oct. 24 to 27 at beautiful Mandalay Bay, where I was among the 17,000 attendees. As I was "welcomed to the World of Watson," I learned that Watson (yes, the computer that was on Jeopardy) is IBM's researchers' vision "to design an intelligent system that brings man and machine together to create a better world." If that sounds like a utopian fantasy, prepare to be amazed at how real that vision has become: Watson is changing how doctors cure disease, how companies analyze their social media footprints, and how financial services firms adapt to ever-changing regulations. I could write an entire book on all that Watson has to offer, but my focus here is on Watson's ability to help financial services firms meet compliance demands more efficiently and with less cost – a much needed innovation as firms spend $99 billion on addressing compliance, thus limiting their ability to invest in growth, according to Marc Andrews, VP of Industry Analytics Solutions for IBM. If you're scratching your head at why regulatory compliance costs are so high, picture this: Linda, a trader at a high-profile brokerage firm, receives a bad performance review from her supervisor.

From there, it won't be long before we begin to wonder how we ever lived without artificially intelligent financial advisors implementing our own personal monetary policy. U.S. financial literacy levels are unacceptably low, and the widespread availability of artificially intelligent money-management tools won't change that. By enabling us to make simple, direct decisions while taking care of the rest, artificially intelligent financial advisors will decrease the prevalence of consumer mistakes and prompt improvement in our overall financial health.I'm actually a perfect example of this point. And while this figures to make things physically easier, the process still won't be simple.