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Digital transformation in healthcare: Improving patient experience, care, and outcomes

Jun 13, 2022

As the pace of technological change continues to increase, digital transformation in healthcare often struggles to keep up. Challenges like integrating aging legacy systems, maintaining patient privacy, and leveraging disparate data sources into actionable insights loom large in healthcare, where time and resources are often at a premium. But the same circumstances that make digital transformation in healthcare more difficult are the very things that underline its importance. When patient lives are on the line, digital transformation isn’t just a “nice to have.” Healthcare systems that achieve their digital transformation goals see immediate improvements in patient experience, quality of care, and patient outcomes. From that standpoint, digital transformation in healthcare isn’t just about adding technology, it’s about revolutionizing the processes and systems that drive the health and well-being of the population as a whole. Case study: Life-saving technology in diabetes long-term care >> Putting patients first While individual healthcare providers commonly put their patients’ needs front and center, the system as a whole did not evolve with that mentality. Due to a variety of factors, including payer systems, consolidation, and the regulatory environment, healthcare systems got a reputation for siloed information, duplicate workflows, lack of clarity, and confusion. As healthcare organizations seek to modernize, smart health systems are taking a consumer-centric approach — redesigning patient experiences and pathways while improving care delivery and outcomes using digital technology. Article: Transforming customer engagement in the digital age >> Planning the future of digital transformation in healthcare During the pandemic, industries accelerated digital transformation efforts across the board, and healthcare was no exception. Out of necessity, more medical touchpoints and interactions moved online, from virtual office visits to automated triage to digital paperwork. Now, two years into the new normal, healthcare organizations are taking stock of their progress, appreciating the speed and scale of their efforts, and mapping opportunities for the future. A recent Deloitte study found that 60% of health systems say they are about halfway through their digital transformation journey. In our experience, working with technology innovators and leaders across industries is where things can get messy. Digital transformation is a long game, and organizations often get bogged down at the halfway mark. To keep moving forward and avoid costly wrong turns, healthcare leaders need a fresh vision and renewed roadmap. Evolving digital transformation in healthcare to meet the changing expectations of patients and providers requires a commitment to a digital-first, people-centric approach, but offers great opportunities for continued growth in connection, innovation, and successful outcomes. Based on our experience, we see five key areas where focused efforts can deliver outsized returns for healthcare systems that are mid-way through their digital transformations: 1. Modernize legacy systems to give providers and patients more options While the vast majority of individual healthcare providers and healthcare organizations use an electronic health records (EHR) system, relatively few seamlessly integrate with patient portals. A recent PEW Health Information Technology (HIT) survey found that almost 80% of respondents wanted to access and view their electronic health records through a website, an online portal, a mobile app, or electronically in some other way. Moreover, the same survey highlights a strong desire for their doctors to share information about the patient’s health status. For most healthcare organizations, integrating patient records across practices and within portals is a headache at best. Adding in the other digital interactions that today’s consumers expect — such as automated appointment and prescription workflows, chatbots, pre-filled forms, and instant answers — might seem impossible. Delivering a better patient experience and giving providers greater flexibility with their tools often takes a more strategic view. Rather than layering in more and more technology solutions, smart healthcare organizations take a holistic approach to modernization, creating flexible, modular solutions that give patients and providers more options in the near term while also making future enhancements easier. Case Study: How an AI healthcare company optimized its digital experience >> Article: Modernization challenges and the path forward >> 2. Mitigate risk to build patient trust In addition to technology lag, healthcare systems also struggle to connect patient health information due to regulatory constraints. To maintain HIPAA compliance in the US and GDPR compliance for EU patients, healthcare organizations sometimes limit the very information sharing that would result in higher quality care. To meet patient expectations of data privacy and personal health data security while also delivering on modern expectations for functionality and connectivity, health organizations need to build in best practices for security and governance throughout their technology architecture. While there are myriad ways to approach this issue, a couple of key options deserve consideration: BYOD Policies A 2019 study found that 63% of healthcare organizations sustained a security incident related to unmanaged and IoT devices. Given the rapid acceleration of digital transformation in healthcare since 2020, we suspect that number is much higher today. As healthcare organizations modernize systems and integrate more virtual and IoT solutions into their technology spaces, having a robust and updated BYOD policy becomes more important. Developing a compliant, enforceable strategy is a critical step in your modernization efforts. Case study: Navigating BYOD in a highly regulated industry >> Containerization One way to mitigate risk is to containerize data, workflows, and applications in the cloud. Although the cloud can sometimes get a bad rap for security, a carefully designed strategy puts security first and can prevent any breach from spilling over too far into other parts of your architecture. Article: Maintaining a composable enterprise >> Blockchain Best known in the context of cryptocurrency, blockchain uses a computerized database of transactions to allow secure information exchange without the need of a third party. Applying blockchain technology to the healthcare industry could improve information security management; healthcare data can be communicated and analyzed while preserving privacy and security. Countries like Australia and the UK have started experimenting with blockchain technology to manage medical records and transactions among patients, healthcare providers, and insurance companies. In both examples, decentralized networks of computers handle the blockchain and simultaneously register every transaction to detect conflicting information, keeping records accurate and making them more difficult to hack. Article: Building trust in your data privacy compliance >> 3. Use voice and wearables to enhance patient experience and outcomes Wearable devices and IoT-based health sensors can track a patient’s conditions and activities remotely, from their vital signs and hydration to the onset of a medical crisis event. The data collected can be helpful to healthcare providers and enable them to better guide patient care. Healthcare providers use IoT and wearable data for remote monitoring and preventative care, providing more specific, personalized connections even with lower staff coverage. Machine learning also drives AI-based natural language processing technology in the healthcare space. As more patients become familiar with voice models like Alexa, Siri, and Google Home, healthcare organizations see potential to deploy the technology for tasks like triage and treatment reminders. For example, the UK’s NHS uses voice technology to field common questions, deliver health information, and remind patients to take medication. Case study: Using wearables to improve patient care >> 4. Put data to work for predictive and preventative care Healthcare organizations collect volumes of data but traditionally haven’t used advanced analytics to translate the information into actionable insights. Today’s leading provider systems are exploring how real-time business analytics, predictive analytics, and AI can transform patient experience and how care is delivered. In much the same way that businesses use data analysis to spot trends, forecast consumer behavior, and drive purchasing decisions, healthcare organizations can use the information they collect to understand patient expectations, discover areas of dissatisfaction or waste, and identify opportunities to enhance the overall experience of patients with their facilities. Likewise, providers can use patient data to understand how a unique individual responds to treatment, spot key diagnostic markers, and even predict potential outcomes so that doctors and patients can work together to minimize risk. Article: Data analytics in healthcare settings >> 5. Automate administrative tasks to focus on patient care The growing number of administrative tasks imposed on physicians, their practices, and, by extension, their patients adds unnecessary costs to the health care system. Excessive administrative tasks also divert time and focus away from providing actual care to patients. Tools like Robotic Process Automation (RPA) can help healthcare systems save time and resources in areas such as administration, billing, and human resources — freeing up more time for face-to-face interaction with patients. When it comes to finding the right applications for automation in healthcare, it’s important to keep patient experience at the center of your strategy. Developing a customer-first automation strategy can help create the perfect blend of automated interactions and human interactions that will meet today’s expectations and delight patients rather than frustrate them. Article: Finding the right use cases for automation >> Evolving patient care through digital transformation in healthcare As the digital tools, apps, and resources pioneered during the pandemic continue to evolve, healthcare leaders must continue to push ahead with digital-first, patient-centric investments in technology, integrations, and solutions. Finding the right balance between patient and provider expectations, maintaining compliance, and enhancing patient care requires a mindset that values the patient’s perspective. Ready to take the next step? Get a machine learning jumpstart >> Get a better view of your data analytics maturity >> Refresh your digital transformation roadmap >> Wherever you are on your digital transformation journey, our team of digital, data, and technology experts can help. Ask us your questions about digital transformation in healthcare >>

How to put AI to work for your business

May 20, 2022

While artificial intelligence (AI) continues to linger in popular imagination in the form of humanoid robots, in real life AI more often exists as a process enabler. Over the past several years, as costs democratized the technology, AI and related emerging technologies like machine learning (ML) and deep learning (DL) became more accessible to mid-market companies. Today, most businesses use AI in one capacity or another — streamlining work, minimizing risk, and gaining competitive insights. These innovations are more than buzzwords. They have powerful potential to revolutionize the way your business collects, processes, and acts on data to solve the real problems facing your business. AI, ML, and DL in the business context To find the right AI applications for your business, it helps to understand your options. Artificial Intelligence Machine Learning Deep Learning Definition Machines programmed to be “smart” Machines that learn from experience provided by data and algorithms ML applied to larger data sets and using multi-layered artificial neural networks Common Examples Smartphones, chatbots, virtual assistants Spam filters, online purchasing recommendations Alexa, Google translate, facial recognition, self-driving cars Example Use Case Configuring a CMS to deliver personalized website experiences using available data points Discovering patterns in data such as “customers who buy X also buy Y,” purchasing cart analysis Processing a large volume of unstructured data, such as images or voice recordings, to generate insights Limitations Machine can only act on specific rules provided Humans must input data parameters as a starting point Requires very powerful – and expensive – computational resources How machine learning differs from AI “ML is the science of getting computers to act without being explicitly programmed.” Stanford University Machine learning takes a different approach to developing artificial intelligence. Instead of hand-coding a specific set of rules to accomplish a particular task, ML trains the machine using large amounts of data and algorithms that give it the ability to learn how to perform a task. Over the years, algorithmic approaches within ML evolved from decision tree learning, inductive logic programming, linear or logistic regressions, clustering, reinforcement learning, and Bayesian networks. Currently, machine learning uses three general models: Supervised learning: Humans supply factors until the machine can accurately apply the distinctions (for example, defining what counts as spam to a filter). Unsupervised learning: The system trains itself on provided data, which is used to surface unknown patterns, as in clustering and association. Clustering looks for patterns of demographics in data and how they predict one another, as in targeting groups of customers with products they will likely need. Association uncovers rules that describe data, as in online book or movie recommendations based on previous purchases and purchasing-cart predictions. Reinforcement learning: Using complex algorithms, the system learns through trial and error toward a defined “reward” of success. Cycling quickly through mistakes or near mistakes, the machine adjusts the weight of the previous results against the desired outcome. How deep learning works As another method of statistical learning that extracts features or attributes from raw data sets, deep learning builds on ML frameworks. While ML requires humans to provide desired features manually, DL uses even more complex algorithms and achieves more sophisticated results without human input. Deep learning algorithms automatically extract features for classification. This ability requires a huge amount of data to train the algorithms and ensure accurate results. To process this volume of data, DL requires specially designed, usually cloud-based computers with high-performance CPUs or GPUs. Using multi-layered artificial neural networks inspired by the biology of the human brain — specifically the organic interconnections between neurons — deep learning trains artificial neurons to identify patterns in information to produce the desired output. Unlike the human brain, artificial neural networks operate via discrete layers, connections, and directions of data propagation. Three common types of artificial neural networks and DL processing applications are: Convolutional neural networks (CNN) are deep artificial neural networks that are used to classify images, cluster them by similarity, and perform object recognition. These algorithms navigate self-driving cars and enable facial recognition, but are also used in leading-edge medical applications such as identifying tumor types. Generative adversarial networks (GAN) are composed of two neural networks: a generative network and a discriminative network. While GANs can be used negatively as in the creation of “deep fake” photos and video, organizations can also use GANs to create privacy-safe data pools for ML. Natural language processing (NLP) is the ability to analyze, understand, and generate human language, whether text or speech. Alexa, Siri, Cortana, and Google Assistant all use NLP engines, and many businesses are exploring ways to incorporate voice into their proprietary applications and digital solutions. Make smart decisions about AI New Era Technology provides cloud infrastructure and emerging technology solutions that accelerate your digital transformation. Our teams help businesses across a wide variety of industries uncover the best use cases for AI, and the right emerging technology solutions to meet your goals. We can help you source, clean, and integrate your data, build and train machine learning models, and iteratively test and improve your solution to maximize results. Not sure how this might work for your business? Check out these real-world examples: Find out how machine learning helps a national pizza chain retain customers >> Discover how AI transforms business processes >> Explore the future of wearables and mobile ML technology >> Learn how ML can help businesses predict sales pipelines >>

4 ways banks can leverage the power of machine learning

Jan 19, 2021

The financial industry has faced waves of changes over the last two centuries. Emerging nations, the American gold rush, the power of the stock market, and even the Great Depression have all shaped how banking works and what consumers expect from their banks. Notably, from 2015 onward, bankers began to list technology risk among their top five concerns1. While these changes have increased banking access and options for the average consumer, they also brought in more tech-savvy competition and greater regulatory scrutiny as heaps of data have become digitally accessible. Ironically, the very technological disruption that has so upended the financial industry will also be what brings new opportunities for growth and increased wallet share. This is especially true with advanced data tools such as artificial intelligence (AI) and machine learning (ML); according to one source2, 83% of early AI adopters have already achieved substantial (30%) or moderate (53%) economic benefits. In light of the benefits machine learning can bring, we’ve compiled four major areas where we’ve seen ML used to reduce costs, increase revenue, and mitigate risk for banks. 1. Acquire new customers Gone are the days where marketing was limited to just a few channels; now banks must maintain an omnichannel presence in order to reach younger consumers who may not listen to the radio or watch TV. Acquiring new customers means reaching them where they are with messaging that’s highly targeted and relevant. Yet as margins get slimmer and budgets are squeezed, reaching these consumers with targeted messaging without breaking your budget can be a challenge if you’re not careful. How machine learning can help Making the most of your marketing involves making the most of your data. Machine learning can help you identify trends in consumer behavior and interests, which can help you deliver the right marketing messages in the right channels at the right time. ML opportunities Identify which existing bank customers will buy another bank product Score your commercial leads based on risk, profitability, and probability to close ON-DEMAND WEBINAR: Learn how to turn data into insights that drive cross-sell revenue 2. Deepen relationship with customers Digital transformation has affected every business in profound ways, especially in the area of reaching customers and managing the customer relationship. Today’s users want a more seamless experience, more targeted messaging, and on-demand access to information, and they’ll move to the bank that can meet their digital demands. How machine learning can help AI and ML allow you to combine your leadership’s decades of experience with customer engagement data. So not only will you have a gut check of what customers want, you’ll have quantifiable data to back it up. Which means your sales and customer relationship initiatives will ultimately be more effective at targeting customers ready to upsell and at cross-selling more of your products to hungry buyers. ML opportunities Identify high-value customers early and engage with them differently Predict the likelihood of a customer taking their deposits elsewhere Identify which disputed purchases are legitimate Project a customer’s lifetime value for those with a limited history with the bank 3. Reduce Financial Risk Consumers are becoming both more credit averse and less credit worthy, which extra pressure on banks and credit unions of all sizes. On top of that, banks face increased risk caused by data breaches, fraudulent activity, and increased costs brought on by regulatory compliance3 . These challenges make maintaining adequate cash reserves more difficult than ever before at a time of increasing market volatility. How machine learning can help Machine learning can give you the insights needed to reduce your overall financial risk by helping you identify fraud and financial liabilities early – so you make and keep more of your profits. ML opportunities Clarify the liabilities on your balance sheet and determine which are the greatest risks Detect fraud and misuse of the company’s finances Project cash reserves to reduce excess bank cash GET THE USE-CASE WORKBOOK: The ultimate guide to machine learning use cases for banks 4. Optimize investment offerings The investment management arm of today’s banks continues to change rapidly as industry challenges increase. Today’s investment managers deal with increased market volatility, capped organic growth, and increasing fees. Because of these challenges, they struggle to keep up with shifting expectations of clients who demand a better investment turnover. How machine learning can help Machine learning can be used to detect patterns hidden in a bank’s historical investment data combined with external financial data. These patterns produce actionable insights that can increase the accuracy of key investment decisions. ML opportunities Match securities to investors based on trade history and market conditions Dynamically price securities based on competitive offerings, market saturation, and risk profile See how one institution used ML to predict their deposit customers' likely deposits on a daily basis, freeing $40,000,000 in excess cash reserves The up-and-coming (and existing) opportunities for financial institutions to win with ML are staggering. One source estimates that advanced data initiatives like AI and ML are predicted to boost overall business profitability by 38% and generate $14 trillion of additional revenue by 20354. And while it’s true that digitally savvy industry newcomers may take advantage of these trends faster than their legacy peers, legacy banks and credit unions hold something the younger competition doesn’t: mountains of historic data that, when mined for insights using AI and ML, can give them a leg up in retaining customers, increasing their wallet share, and reducing their overall financial risk. To adequately leverage these four opportunities, banks will need to embrace the very technology disrupting the industry. Banks that view their data as one of their most important assets and embrace AI and ML to create new insights will likely see growth, whereas those who don’t will struggle to keep up. 1) 2015 Banking Banana Skins Report 2) 2017 Deloitte 3) 2017 Financial News 4) n.d., Accenture

An overview of RPA: Is it right for your business?

Sep 30, 2020

There’s been a lot of hype about Robotic Process Automation (RPA). Headlines tell us we can transform our business process in as little as 12 weeks using RPA bots. Benefits are touted, velocity is promised, trends of growth are noted, and new jargon was coined: “Automation arbitrage, a term Gartner uses to describe the recalibration of human labor to drive business outcomes is one of the biggest enablers in this coming decade.” – Gartner, The CIO’s Guide to RPA and Introduction to Hyperautomation. Hype can be fun, but it doesn’t answer the very practical question, “Can RPA help transform my business?” This article will answer it for you and help you make an informed decision about whether RPA is right for your organization. Read on to learn the best-fit processes, work through a decision flowchart to determine whether your process is suitable for automation, and gather helpful considerations to keep in mind as you’re getting started. There are also links to demos and further resources throughout. First, what is RPA? Robotic process automation uses computer software (bots) to emulate a human worker interacting with digital systems. RPA bots automate repetitive tasks by interacting with software applications, just as humans do while working. In short, companies use RPA software to perform repetitive tasks that would usually be done by workers sitting at their computers. Bots can be programmed to work just like us − logging into and switching between applications, interpreting information, making calculations, and copying and pasting data. Bots can also process data, trigger responses, and communicate with other systems to perform tasks at a high speed without error, which enables organizations to effectively automate tasks, streamline processes, and increase productivity. Often conflated with artificial intelligence (AI), RPA is non-intrusive and does not require system integration. It sits on top of your existing system to perform business processes, using the same interfaces that humans use. And, unlike scripts or macros, RPA will not break every time there is a minor software update. Essentially, bots can work in two modes, attended or unattended. This provides flexibility to better meet specific business needs. Attended bots are typically targeted toward front-office activities and are useful when the entire end-to-end process can’t be automated. These bots are programmed to work alongside humans to complete processes that can pass data between bots, applications, and human workers, or complete specific functions within a process. Unattended RPA bots execute tasks and interact with applications independent of human involvement. Unattended bots can be triggered by events or scheduled. They will run until a condition is met. Read more: Jumpstart your business processes: Using hyperautomation to achieve speed and scale >> Which business processes are the best fit for RPA? An important thing to understand about RPA is that it doesn’t add value to every area of the enterprise. Forrester Research, Inc. counsels caution when considering this “shiny new kid on the block.” Therefore, careful consideration, selection, planning, and governance are crucial to the success of an RPA implementation. First, consider whether your process falls within or is similar to this sample list of business functions that benefit most from RPA: Finance and accounting − orders, claims, vendor management, accounts payable, and collections. IT services − software deployment, server and app monitoring, routine maintenance and distribution, batch processing, password reset/unlock, backup and restoration. HR services − data entry, payroll, time and attendance management, benefits administration, compliance, and reporting. Supply chain − inventory management, demand and supply planning, work order management, and returns processing. Next, ask yourself the following questions about the process you hope to automate (see the decision flowchart below for the entire process): Is it rules-based, standardized, with clear processing instructions or templates? Is it highly manual, repetitive, and prone to human error? Do transactions flow at a high volume and/or frequency? Is it well-documented, stable, and mature? Are there standard, readable electronic input types? The above sections represent the critical first step to determine whether automation is right for your process. Completing the exercise of the decision flowchart will make it clear whether you should pursue business transformation via process improvement initiatives, or RPA implementation. Benefits realized from RPA There is a reason automation is here to stay, and the sooner you implement RPA, the sooner you create a competitive edge for your business. RPA benefits include: Reduced costs − RPA can reduce processing costs by up to 80%. Improved economics, efficiency, and effectiveness through reduction of human error and the costs of duplicate effort, rework, and mistakes. Transformed and streamlined organization workflows. Increased compliance and consistency. Positive impact on operational metrics − reduced focus on non-value-add activities provides time for important strategic tasks and customer relationships. Improved customer service through agent access to readily available information and reduced manual efforts. Non-intrusive, seamless integration with existing enterprise systems, resulting in reduced implementation costs. Extremely scalable across business units and geographies; multiply bots and deploy more as you go. Improved processes − bots constantly report on their progress, so you can strategically improve processes by using operational and business predictability. How to ensure RPA implementation success Clear vision, comprehensive planning, and structured governance are critical factors in the success of any RPA implementation. Proposed changes must be well-defined by leadership, shared by IT and business, and communicated with the affected employees. Below is a list of success factors to keep in mind as your organization takes its first steps toward automation. Plan well − a common automation pitfall is lack of governance. RPA programs need centralized control and governance, including formalized methods and standards to ensure maximum benefits. Avoid working in silos − implementation efforts must be driven by collaboration between IT and the business and based on a clear vision from leadership. Start managing change early − your people strategy can’t be put off until deployment. The successful realization of benefits from RPA projects requires end-to-end organizational change management (OCM) that is adaptable to the size and complexity of the RPA endeavor. Communicate widely and frequently − throughout the implementation process, communication is key because bots will change how people do their jobs. In addition, some workers may fear job loss, so communication, transparency, and training can help them embrace this new frontier in business processes. Manage for, or eliminate, potential surprises − don’t forget to factor in the effects of third-party partnerships and applications. These are an uncontrollable factor of your business environment, so use care when including them in your automated process. Put process over tools − RPA is not only about rapidly developing bots. A robust governance structure, well-defined opportunity identification process, quality development, and reliable operations are more important than any particular tool. Stay objective − avoid implementing automation solely for the wow factor” Be sure you understand what you hope to achieve through automation and that you’ve considered the long-term costs involved. Manage expectations − bots are not the whole solution, and RPA is not a silver bullet; it should be viewed as only part of the automation strategy for the enterprise. You may need a broader strategy, such as system modernization, process transformation, and use of machine learning, to underpin a larger transformation effort. Keep in mind that RPA is not a set-and-forge” process. New bots will need consistent oversight until they are fully trained. They’ll also require ongoing management, especially when there are changes in the system or environment. Summary Now that you have the facts, you can decide whether RPA is right for your organization. And if you need help, our team is experienced in leading Robotic Process Automation programs in both advisory and implementation capacities. Our solid partnerships with Microsoft and UiPath, a top RPA vendor according to the 2020 Gartner RPA Magic Quadrant, help us offer the most appropriate technologies available for your organization’s needs. RPA services that we offer include: Advisory/assessment Set up Center of Excellence (prioritization of applications) Construct team Evaluate tools Evaluate Book of Work (project work having funding associated with it) Process mapping and analysis Implementation Develop and upgrade bots Create run books for bots (procedures for handling tasks, contingencies, and troubleshooting) Perform monitoring and management (steady state) Contact Fusion Alliance to discover if RPA is right for you.

The future of retail: Conversational marketing and machine learning

Sep 8, 2020

This article was originally published at the Forbes Communication Council Amazon has generally been considered the standard-bearer for product recommendations, and for good reason. The retail giant utilizes user data on past purchases, browsed-for items, and even what users have recommended to others to generate recommendations. Just think, recommendations likely popped up in the sidebar during your most recent Amazon binge. “People who viewed this product also viewed...” often appears as you scroll. A chatbot may even appear with ideas related to your shopping history. This is conversational marketing at work. Still, these advancements fall short of creating a truly personal experience that can predict and assist buying behavior by having a full view of who the person is — not just their recent search and purchase history. Even common segmentation methods fall short by making assumptions based on age and gender that fail to account for many outlying factors that can be easily discovered. The future of retail will be defined by immersive, conversational experiences that lead to better customer interactions and increased buyer loyalty from brands that stay ahead of the curve. While conversational marketing has taken many companies this far, using conventional conversational marketing techniques, in conjunction with machine learning, can be the answer retailers are looking to create the experience of the future. Online retail: Blending conversational marketing with AI technologies While conversational marketing has become the trend in business-to-business (B2B) demand generation strategy, there exists a huge business-to-consumer (B2C) opportunity, as well. Conversational marketing practices utilize website chat features and chatbots to initiate in-the-moment interactions with customers and build context to quickly qualify them for the appropriate next step. According to David Cancel’s aptly titled book, Conversational Marketing, both baby boomers and millennials are likely to adopt the use of chatbots, with a majority in both groups finding instantaneous responses and quick answers to simple questions being potential benefits. Aside from the obvious advantage of getting answers to product questions, automated chatbots offer a number of opportunities to enhance shopping experiences when coupled with data. Machine learning and chatbots While many B2C companies are already leveraging chatbots to streamline the customer experience, there lies even greater opportunity with machine learning to truly learn from and predict consumer behavior. Today’s practical machine learning models enable rapid iteration of data and deliver quick, reliable data sets. Data collected from customer conversations about the products they research, buy, and use can tell a deeper story about the customer themselves over time. Instead of a static list of recommended products based on their last purchase, machine learning can help us understand the customer’s lifestyle and habits in such a way as to help the customer make the best purchase in the moment. As an example, imagine an on-the-go, seasoned business professional with a love of podcasts and streaming music. Our traveling audiophile is a regular adopter of new headphone technology and is on the hunt for a new pair. While segmented data and previous purchase history might be able to get us in the ballpark when it comes to their next tech purchase, it doesn’t tell the whole story. In fact, the reason for this purchase has nothing to do with a search for the latest technology, but rather because past purchases have missed the mark for this customer’s need for multitasking and call connectivity. In this case, relying on past purchase history or even peer purchasing information won’t help. However, their experience with a chatbot powered by machine learning can give us helpful predictive data that informs the retailer of their need for a balance between audio quality and the ability to quickly and clearly connect to meetings during travel. A few quick questions allow the chatbot to suggest a new pair of headphones to fit their lifestyle, along with helpful content and reviews that match our customer’s pre-purchase research habits. Marrying predictive data to emerging technologies As advances in artificial intelligence (AI) continue to blur the line between human and bot, and retail brands continue to experiment with augmented reality (AR) to replicate brick-and-mortar shopping experiences, it’s vital that data plays a role in the next phase of online shopping. Not only should brands be placing an emphasis on the aesthetic experience that can be delivered through apps infused with AR, but they should also make room for predictive machine learning data to make the buying process even easier for the consumer, making them more likely to return in the future. In fact, for any brand wishing to be at the forefront of the next wave of retail evolution, I believe it’s vital that a data governance framework be in place and actively funnel information to teams developing emerging technology. The days of keeping customer data siloed away from our product teams need to come to an end in order to fully realize the marketplace potential. The future of retail is filled with possibilities that can completely reshape the way we understand consumer behavior and connect with the consumer to meet their needs in real time. Taking tangible steps to listen to our customers, learn from them, and act to predict their needs, while delivering a stellar shopping experience along the way, is more than a possibility — it’s a reality. At Fusion Alliance, we find our place at the intersection of advanced analytics, experience design, and technology, leveraging machine learning to gain customer insights that inform our strategies. Learn more about our approach to machine learning solutions >>

How your bank can use machine learning to ensure future success

Aug 28, 2020

Amazon, Netflix, Airbnb, Uber, and other disruptors have raised the bar on what customers expect from a business. These online giants have figured out how to use their customer data to make personalized recommendations and predict when customers are going to buy — and present offers at just the right time. Brands that use personalization report an average growth of 20% in sales (Monetate research), and customers feel less spammed and more like they’re in control of the experience. It’s no surprise that consumers are looking for that same personalized, frictionless experience when interacting with their financial institutions, whether through mobile banking, your website, at a brick-and-mortar branch, or at one of your ATM locations. And it pays off for banks who can engage their customers. According to a 2013 Gallup study, fully engaged customers bring in an additional $402 in revenue per year to their primary bank, as compared with those who are actively disengaged. Even better, the research said 71% of fully engaged customers believe that they will be customers of their primary bank for the rest of their life. That could be your bank, but only if you can reach your customers in ways that feel natural and valuable to them. Customers want to be engaged with the right messages at the right time Imagine if you could understand your customers so deeply and predict their buying patterns so clearly that you could deliver targeted marketing only to those ready to invest in more products with your bank. Not only that, what if you could know what to say to them and on which channels to reach them? How would that impact your business? The trend is clear: financial institutions must adopt a customer-centric business model now to ensure success in the future. This puts banks like yours at a crossroads, and the problem is where and how to embark on that journey. Tackle your greatest challenges The formula seems simple. Increase your engagement and you’ll increase your revenue. But meanwhile, you’re under pressure to acquire new customers, maintain your base, forecast/reduce risk, manage capital, navigate security compliance and financial regulations, and optimize the business. You may also grapple with siloed data, legacy systems, and outdated processes, all seemingly monumental challenges that may adversely affect your customer experience. For example, your customers and employees may not have access to the right data at the right time to provide an optimal experience. Or, from a marketing standpoint, different departments within your company may be targeting the same customers, resulting in too many emails. Or your customers may get untimely messages about promotions that have passed or receive communications that don’t apply to their current situation. This creates frustration and a poor user experience that may be enough to make your loyal customers turn away. Other banks have been in your shoes, facing the same challenges and fears, but they’ve made major strides in putting the focus on the customer. They’ve found success through the “magic” of machine learning (ML). ML enables your staff to prioritize your over-capacity bankers’ focus and marketing spend on opportunities that are real. ML is a modern technique that uses algorithms to analyze enormous amounts of data. Machine learning models learn on their own and identify insights and patterns to predict future behavior. Machine learning algorithms connect the dots far faster and deeper than people can, exposing patterns in your customers’ behavior that empower your team to take actions that will impact your business’ top and bottom lines. Unlike traditional analytics tools, ML can evaluate account holders, securities, and transactions in real-time. If you want immediate decisions integrated in the moment, machine learning is the answer. And, good news, even though you may feel you are behind the curve right now, you have something that the younger fintechs you compete against don’t − a wealth of historic data that can be “mined” by ML to answer your specific business questions. Some organizations need help in improving the quality of their data for effective use in the machine learning model, and that’s not an uncommon challenge. But good data will be your key to success. Machine learning applications in finance Banks have found many successful ways to leverage machine learning. For example, they use it to answer specific business questions across all departments, including: How do I increase my customer wallet share, including: What are my best opportunities for cross-sell/remarketing my existing customers? Can I identify customers that we can convert from other banking institutions? Can I identify loan-default risk early enough to take an action? Can I dynamically price securities based on investor demand and market saturation? Can I predict my cash and reserve activity to optimize liquidity levels? Can I identify account holders’ attrition activity before they disengage? What percentage rate and product messaging would make my ideal prospect buy? The first step towards engaging customers with the right messages at the right time is to capture what questions your bank wants to solve. With these questions in hand, you can move to the next step, seeing how much predictive value these “use cases” for machine learning will give your financial organization. Case in point, these very questions are how it started for a large, institutional bank sitting on decades of financial transaction data we worked with. They wanted to more accurately predict member activity and drive better returns on cash reserves – and leveraged machine learning to do it. Our machine learning model identified patterns in their transactions, which spanned hundreds of credit unions and billions in cash to predict the deposit activity of millions of credit union members on a daily basis. The result? We freed $40 million in excess cash reserves. The insights gleaned also empowered the organization to pass on greater returns to members by selling short and long-term securities, arbitrage, and reducing borrowing fees. Another institution, Primary Financial Corporation (PFC), found great success using machine learning to improve their sales targeting. PFC wanted to predict CD issuers’ funding needs and institutions’ desires to invest. They developed machine learning models that synthesized PFC’s financial and competitive data to price securities, identify buyers, and project trade profitability. By the time the first phase of the project was complete, PFC could predict with over 80% accuracy and 70% precision the likelihood of a particular investor to buy a given investment. The common thread in these stories is that both organizations had an abundance of historic data at their fingertips, but they hadn’t explored how ML could help them retain more deposits, sell more products, or reduce their financial risks. The rapid predictive insights that machine learning continues to provide to both companies has been game-changing. And both are now exploring other ML applications. Get started Machine learning is widening the gap between banks who embrace it and their competitors who haven’t. If you don’t improve your banking experience, your customers will turn to another bank or even be serviced by a fintech. As you navigate how to become that customer-centric organization you want to be, explore machine learning as a way to get you closer to your customer and see rapid results. Start by coming up with specific questions that your business needs to answer, and take time to learn more about what machine learning can do in your organization. Contact Fusion Alliance to discuss if ML is right for your project. ON-DEMAND WEBINAR: Learn how to turn data into insights that drive cross-sell revenue

Top 3 reasons to invest in machine learning for mobile

Aug 24, 2020

Artificial intelligence (AI) and machine learning (ML) have completely transformed mobile development. Mobile app users today are often looking for an easy and relevant user experience — one that has been customized for them. The best way to get there? Machine learning. Machine learning identifies anomalies and patterns that ultimately optimize the user experience. If your technology conversations have stalled at the brainstorming or ideation phase, consider why. If you don’t have a clear answer, you’re not alone there either. “Strategic decision makers across all industries are now grappling with the question of how to effectively proceed with their AI journey,” says Marianne D’Aquila, research manager, IDC Customer Insights & Analysis. Despite questions about how to proceed, organizations know they need to invest in ML for mobile before current competitors, and those waiting in the wings, figure out how to profit from it first. Considering the speed at which machine learning is being adopted and spreading, and its potential to quickly help companies on multiple fronts, the time for execution and implementation is now. Here are the top three reasons that make machine learning development for mobile important right now: 1. Machine learning for mobile increases app security “Facial recognition” ($4.7 billion, 6.0%) and “fraud detection and finance” ($3.1 billion, 3.9%) were among the top five categories of AI global investment in 2019, according to the AI Index 2019 Annual Report (an independent initiative at Stanford University’s Human-Centered Artificial Intelligence Institute). It’s not surprising. From TikTok’s recent security flaws to Target’s $18.5 million settlement, app vulnerabilities and potential data breaches are breaking news, and there are few signs of a slowdown. While the short-term financial impact can hurt, the long-term cost of losing the trust of customers and partners can be even more painful. Companies that receive users’ personal information (e.g., passwords, billing addresses, answers to security questions) for processes such as app authentication or making purchases must continually optimize how the data is used. Through machine learning and automating parts of the process, you can identify anomalies faster, allowing you to see patterns and manage potential weaknesses more quickly. Operationally, ML can detect and staunch security issues related to data inside your company, such as logistics or pricing anomalies, that could be a drain on resources. For example, if one of your products is selling faster than usual via a shopping app, it could be related to a pricing error. Do you really want that $450 device on sale for $4.50? The mobile application landscape is comprised of a wide variety of operation system versions, devices, and software systems. This creates a much greater number of attack surfaces that attackers can target. (A first step to optimizing security is risk evaluation and awareness. Contact Fusion to hear more.) 2. Machine learning leads to increased mobile privacy It could be argued that the recent news cycle around privacy indicates a real desire for clarity, if not outright skepticism. In more than 3,600 global news articles on ethics and AI from mid-2018 to mid-2019, the dominant topics were “framework and guidelines on the ethical use of AI, data privacy, the use of face recognition, algorithm bias, and the role of big tech.” You’ve heard about Russia’s role in the 2016 election and the use of personal information for ad targeting. These sorts of debacles haven’t led consumers to give up on digital. Instead, they are demanding more privacy oversight and are being more cautious about the apps they use. Privacy concerns are complementary to security issues. While security comprises keeping personal data from hackers, trolls, or criminals, privacy is more related to keeping personal data in a person’s own hands, away from any individuals or organizations that don’t need to be privy to it. For example, if you use an activity tracking app to record runs, you might appreciate a note when you hit a milestone: “You had a personal record today!” Machine learning makes it possible for the mobile app to directly detect this activity and send a congratulatory message without any human intervention. There’s no need for a stranger to know you clocked a fast 10K. Machine learning on the edge further increases privacy by eliminating the need for data to be sent to the cloud. When ML on the edge is in place, individualized data never leaves the device, keeping the user’s personal information in their own hands at all times. Amazon, Alexa, and Google Home employ ML on the edge, as some functions are offloaded to a device while others have to go to the cloud. In addition to supporting privacy, the reduced travel time for data makes these apps and devices faster. 3. Machine learning for mobile helps create personalized customer experiences Consumers expect their demographic, behavioral, and other personal data to be secure and private, while they also want increasing levels of personalization. Delivering on these demands can be a delicate, real-time balancing act for companies, but machine learning helps make it possible to juggle data acquisition with protection and those prickly questions around how to use the data to everyone’s advantage. But is there a clear business case to pursue personalization? According to a 2019 Salesforce report, the answer is yes, as 75% of 8,000 consumers and business buyers surveyed expect companies to use new technologies to create better experiences. Machine learning for mobile enables you to make user-experience headway on several fronts. First, it can help you build a baseline of customer app usage. Once you have that baseline, you can see patterns in user behavior. Next, particular behaviors or deviations from the baseline can trigger delivery of a relevant coupon, suggested product to explore, or a reminder to revisit an abandoned shopping cart. Even more sophisticated, ML can serve up colors, screen layouts, and language that appeal most to a particular user. And with machine learning, the reactions are in real-time. The more your user engages with your mobile app, the more refined and personalized the experience becomes. Through machine learning, your brand becomes more closely aligned with the customer experience that your customer desires. Getting started can feel uncomfortable at first, but at Fusion, we’ve found that organizations often have low-hanging fruit ripe to benefit from machine learning for mobile. You just need to be able to see and then act on those opportunities. Working alongside you on this journey should be people who understand data science and machine learning, and who can uncover weaknesses to target. Now is the time to move forward on machine learning for mobile initiatives. Current market conditions indicate a shortage of professionals in machine learning and data science. Fusion fills this gap. If you’re interested in hearing more about machine learning for mobile, let us connect you with one of our experts.

The buzz about artificial intelligence, machine learning and deep learning: An overview

Aug 13, 2020

The B2B and B2C markets are abuzz with the terms artificial intelligence, machine learning, and deep learning. But what do these terms mean, exactly? They’re often used very loosely and you may think they’re interchangeable. But they’re not. Here’s a short overview of artificial intelligence, machine learning, and deep learning to help you cut through the static to determine which solution is right for you and your business. Working definitions Artificial intelligence (AI) is the term for the broad discipline that includes anything related to developing machines that are “intelligent” through programming. This includes many daily items you’re familiar with, from smartphones and marketing software to chatbots and virtual assistants. Machine learning (ML) refers to machines and systems that can learn from “experience” supplied by data and algorithms. ML is often used interchangeably with AI, but it’s not the same thing — ML is a developmental outgrowth of AI. Deep learning (DL) is a further developmental outgrowth of ML, but applied to even larger data sets. It uses multi-layered artificial neural networks to deliver high accuracy in assigned tasks. In terms of historical development, AI came first. It serves as the foundational discipline from which ML evolved. And ML is the foundational discipline from which DL evolved. One way to conceptualize their relationship to one another is as nested arenas of AI development along a timeline: Artificial intelligence overview and how it works In its broadest sense, AI refers to machines programmed to act according to well-defined rules and responses. The responses are confined to the set of rules that are provided, and the machines can’t deviate from those rules, except if they fail. A very basic example of AI would be your clothes dryer. You can set a specific time and temperature, and the machine performs the task according to the instructions given. It doesn’t have the ability to make decisions or make any changes by itself. A more sophisticated example would be configuring your CMS to deliver personalized website experiences. By analyzing a targeted selection of data points about your customer and writing the appropriate logic, your website can display the most relevant content. In neither case is the machine capable of being more than its programming — even if that programming makes the machine very capable in accomplishing its assigned tasks. Machine learning overview and how it works “ML is the science of getting computers to act without being explicitly programmed.” Stanford University Machine learning is a different approach to developing artificial intelligence. Instead of hand-coding a specific set of rules to accomplish a particular task, in ML the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform a task. Over the years, the algorithmic approaches within ML have included and evolved from decision tree learning, inductive logic programming, linear or logistic regressions, clustering, reinforcement learning, and Bayesian networks. Currently, there are three general models of learning used in machine learning: Supervised learning Unsupervised learning Reinforcement learning Supervised learning Right now, most machine learning is supervised, which still requires a lot of human intervention to accomplish the training. For example, a “supervisor” has to manually tell the spam filter what to look for in spam vs. non-spam messages (e.g. look for the words “Western Union” or look for links to suspicious websites, etc.) until the machine has gained enough “experience” to learn and accurately apply the distinctions. The training goes something like this. The algorithm would be first trained with an available input data set of millions of emails that are already tagged with the spam/not spam classifications to train the ML system on the characteristics or parameters of the ‘spam’ email and distinguish it from those of ‘not spam’ emails. Unsupervised learning In general, unsupervised learning is more difficult to implement than supervised learning. But it’s very useful when your example data set has no known answers and you’re searching for hidden patterns. The system has to train itself from the data set provided. Two popular types of unsupervised learning are clustering and association. Clustering groups similar things together and consists of dividing a set of elements of the existing data set into groups according to a heretofore unknown pattern. For example, defining a customer demographic and further clustering them based on education or income might affect their purchasing decisions in favor of one product or another. This would allow you to specifically target each cluster of customers more effectively. Association involves uncovering the exact rules that will describe the larger portions of your data. For instance, “People who buy X also tend to buy Y.” For example, online book or movie recommendations are based on the association rules uncovered from your previous purchases or searches. Association algorithms are also used for purchasing-cart analysis. Given enough carts, the association technique can help predict another item you might like to put into your cart. Reinforcement learning The machine learning system learns by trial and error through training characterized by receiving virtual rewards or punishments. Reinforcement learning comes from child-development research that says instead of telling a child which piece of clothing to put into which drawer, you reward a child with a smile when the child makes the right choice on their own or you make a sad face when the child makes the wrong choice. After just a few iterations a child learns which clothes need to go into which drawer. In reinforcement learning, very complex algorithms are designed so that the machine tries to find an optimal solution. It operates according to the principle of reward and punishment, and by this approach, it moves quickly through several mistakes or near mistakes to the correct result by adjusting the weight of the previous results against the desired outcome. This allows the machine to make a different, better decision each time until it is rewarded. Deep learning overview and how it works Deep learning is the newest area of ML and AI that uses multi-layered artificial neural networks to accomplish tasks such as object detection, speech recognition, and language translation − all with an extremely high degree of accuracy. The artificial neural networks (ANN) are inspired by the biology of the human brain, specifically the organic interconnections between neurons. The human brain analyzes information it receives and identifies it via neuron connections according to past information it has stored in memory. The brain does this by labeling and assigning information to various groups, and it does this in nanoseconds. Similarly, when a system receives an input, the deep learning algorithms train the artificial neurons to identify patterns and classify information to produce the desired output. But, unlike the human brain, artificial neural networks operate via discrete layers, connections, and directions of data propagation. Despite the level of sophistication of its algorithms, DL is still just another method of statistical learning that extracts features or attributes from raw data sets. The major difference between deep learning and machine learning is that in the latter you need to provide the features manually. DL algorithms, on the other hand, automatically extract features for classification. This ability requires a huge amount of data to train the algorithms. The accuracy of the output depends on the amount of data, and deep learning requires huge data sets. Additionally, due to the sophisticated algorithms, deep learning requires very powerful computational resources. These are specially designed, usually cloud-based computers with high-performance CPUs or GPUs. There are several kinds of artificial neural networks and DL processing applications you may have already heard of: Convolutional neural networks (CNN) are deep artificial neural networks that are used to classify images, cluster them by similarity, and perform object recognition. These are algorithms that can identify faces, tumors, and navigate self-driving cars. Source: TowardsDataScience Generative adversarial networks (GAN) are composed of two neural networks: a generative network and a discriminative network. GANs are very popular in social media. If you feed the GAN with a large enough data set of faces, it can create completely new faces that are very realistic but nevertheless fake. Natural language processing (NLP) is the ability to analyze, understand, and generate human language, whether text or speech. Alexa, Siri, Cortana, and Google Assistant all use NLP engines. Putting AI, ML, and DL to work for you What most of us think of as AI is, more accurately, machine learning. But understanding the history, development, and distinctions between artificial intelligence, machine learning, and deep learning can help you determine which solution would be right for your goals. The solution you choose, however, is also dependent on the amount and type of data you have access to. Within the last couple of years, almost every company is using machine learning or deep learning (and therefore, by definition, artificial intelligence) in some capacity to move their business forward. The competitive gauntlet has been thrown down. Fortunately, tools that were previously only available to enterprise-size companies are now affordable and accessible to mid-market companies, making machine learning the most accessible playground right now. Fusion Alliance provides cloud infrastructure and other ML services that accelerate machine learning modeling, training, and testing to our banking, financial, and retail customers. Read more about our work in AI, ML, and DL: 3 fusion experts share their machine learning secrets Unlock customer credit insights with machine learning How Donatos uses machine learning to retain customers Conversational marketing and machine learning are shaping the future of retail

3 New Era Technology experts share their secrets for machine learning in banking

Jul 22, 2020

In the quest to solve its most pressing challenges, the banking industry is being transformed by its adoption of artificial intelligence (AI) and machine learning (ML). Financial institutions are under pressure to better understand their customers, drive a more personalized customer experience, acquire new business, forecast risk, prevent fraud, comply with increasing regulations, improve processes . . . the list goes on and on. Most banks continue to use traditional, expensive analytics tools to tackle these challenges, but they struggle to keep pace with demands, and the tools are difficult to maintain. Machine learning relies on statistical and artificial intelligence approaches to rapidly uncover patterns in complex data, patterns that can’t be discovered through traditional tools. The impact of machine learning in banking While adoption of machine learning in finance is in the early stages, institutions who have leveraged this secret sauce are finding it to be a differentiator. For example, a large regional bank leveraged ML to predict institutional customers’ likely deposits on a daily basis, freeing $40 million in excess cash reserves. Another institution, credit union service organization Primary Financial Company, used ML to synthesize financial and competitive data to price securities, identify buyers, and project trade profitability. PFC can now ascertain with over 80% accuracy and 70% precision the likelihood of a particular investor to buy a given investment. For these companies, their early ventures into ML have certainly moved the needle on what they can accomplish. We spoke with three artificial intelligence and machine learning experts at New Era Technology to tap into their experience with banks, learn where the market is headed, and get answers to some common questions. Q: What do you see as the 2020 trends in machine learning for banks and credit unions? A – John Dages: 2020 is the year where we see machine learning become more democratized. Historically, machine learning engagements have required substantial data science and model training investments. However, the major ML platforms are evolving and providing advanced automated machine learning and feature analysis toolchains, lowering the barrier of entry for ML projects. Our team is also actively monitoring new “explainability” techniques to add deeper transparency for ML-based predictions and insights. Historically, the black-box nature of some ML algorithms (specifically deep neural networks) makes it difficult to relate to business principles. Ideally, these emerging techniques will increase confidence in ML models early in their lifecycle. In the banking sector, we have seen a great deal of capital chase trading and investments, but we are also seeing ML flow into loan operations, cash management, and general risk. A – Sajith Wanigasinghe: Machine learning applied to fraud detection is a major trend. Artificial intelligence is beneficial here because ML algorithms can analyze millions of data points to detect fraudulent transactions that would tend to go unnoticed by humans. At the same time, ML helps improve the precision of real-time approvals and reduces the number of false rejections Another leading trend is using robo advisors for portfolio management. Robo advisors are algorithms built to calibrate a financial portfolio to the user’s goals and risk tolerance. Chatbots and robo advisors powered by natural language processing (NLP) and ML algorithms have become powerful tools with which to provide a personalized, conversational, and natural experience to users in different domains. A – Patrick Carfrey: Personalized delivery of banking services is going to improve in 2020. New products are entering the marketplace that enable consumer and commercial bank customers to receive relevant account information in real-time, at the grain and timeliness that customers want. Q: What is your favorite machine learning use case for banks right now? A – John Dages: Machine learning will change the way banks see credit risk. FICO and the five C’s of credit are limited in features, captive to three agencies, potentially biased, and outmoded. The models we are building will allow lenders to view a complete picture of a borrower, offering customized predictions on creditworthiness. The banks that adopt this model will see an increase in lending opportunities while better understanding the liabilities on the balance sheet. A – Sajith Wanigasinghe: Customer lifetime is my favorite use case, where we can predict how valuable would a customer be within X number of years so that the bank can establish a good relationship with the customer in the early stages. A – Patrick Carfrey: Remarketing/cross-selling is a powerful option for banks right now. Given all the customer data that banks own, including deposits, transactions, and more, ML can tell if a customer is a good target for a new product in the bank’s portfolio. This is especially relevant as customers are expecting more. Being able to predict customer needs supports that need. Related Article: 4 ways banks can leverage the power of machine learning Q: What is the one machine learning data tool you can’t live without? A – John Dages: Excel. Sure, the enterprise data tools are highly capable (and the team spends a lot of time there), but the ability to quickly navigate data, perform simple transforms, and share data with a tool everyone knows is critical. I can’t remember a project where we didn’t get exemptions to install Excel in the banks’ datacenters. A – Sajith Wanigasinghe: TensorFlow framework would be one of the tools that I can’t live without because it’s the number one framework that I use every day and in 99% of our projects. TensorFlow is an open-source machine learning library which helps you to develop your ML models. The Google team developed it, and it has a flexible scheme of tools, libraries, and resources that allow me to build and deploy machine learning applications. A – Patrick Carfrey: TensorBoard. This is TensorFlow’s visualization toolkit, and it provides a nice visual interface for tracing key metrics through the model training pipeline. Deep learning models can get complex quickly, and being able to explore a model outside of the command line is nice. Clients love the graphs, too! Q: What are the biggest machine learning myths you wish more people understood? A – John Dages: For those beginning to develop an AI/ML center of excellence, there is going to be a gravity to focus on the cutting edge (deep learning, cognitive science, others). While there is obviously value there, there are a multitude of “traditional” machine learning practices and algorithms and that are lower complexity. A deep neural network should be the last resort, not the first option! A – Sajith Wanigasinghe: That machine learning and AI will replace humans. In fact, machine learning and AI will help you do your job much faster and better, and enable you to focus on the satisfying and important human elements of your role — including creativity and strategy. Think of machine learning and AI in terms of a tool, not a replacement for humans. A – Patrick Carfrey: For every machine learning project I’ve delivered, our clients will inevitably ask “We love the model, but can you tell us more about how the model is making the predictions?” This is a surprisingly challenging question to answer, particularly for black-box neural networks. New Era has a variety of techniques to provide additional details, but they aren’t necessarily directly correlated to the actual model we’ve developed. If it is insights you seek, not decisions, consider business intelligence tools and processes in lieu of machine learning. There is room for both! Meet our panel of experts: John Dages With 15+ years of technology leadership experience, John brings a unique perspective to companies on their advanced analytics journey. He led numerous machine learning initiatives for large enterprises across industries. Those projects range from customer acquisition and retention to securities pricing and trade analytics. John’s background in application development, analytics, systems integration, and I&O helps him formulate how businesses can use data to drive competitive advantage and engineer true intellectual property. Sajith Wanigasinghe Sajith is an expert in machine learning, artificial intelligence, and enterprise-wide, web-based application development. He applies his experience and insights to help enterprises identify and solve challenges across the business that are ideal for machine learning. Sajith led teams that have revolutionized the financial, insurance, food, and retail industries by introducing advanced, intelligent forecasting systems that are powered by machine learning and artificial intelligence. He holds a B.S. in computer science from Franklin University. Patrick Carfrey Patrick joined Fusion Alliance over six years ago, leading a variety of application development initiatives for a flagship Fortune 500 client. Patrick is a firm believer that software is social, choosing to spend as much time in front of end users to build the best possible product. In that capacity, Patrick has developed and deployed practical machine learning solutions to help better understand and predict customer behavior to drive maximum engagement. He is the Competency Lead of Java at Fusion and holds a B.S. in computer science and engineering from The Ohio State University.

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