Future of Financial Services Workforce

UntitledFinTech disruptors have been finding a way in by focusing on a particular innovative technology or process in everything from mobile payments to insurance. A forte of technologies “AI-ML-DL-NLP-CV” is fueling the FinTech innovations. The large financial services companies can’t be complacent as FinTechs have been attacking some of the most profitable elements of the value chain and as well as areas which were historically subsidized.

Let us refresh our memory on these AI technologies and their relevance to the financial services industry.

  • AI makes machines to learn from experience and perform human-like tasks – AI offers robotic & intelligent process automation (RPA/IPA) of financial processes
  • ML is a specific subset of AI that trains a machine on how to learn – ML is enabling algorithmic trading lead to better predictability and decisions around credit and consumer lending, thereby lowering risk to the bank or financial institution
  • DL is s a type of ML that trains a computer to perform human-like tasks, such as identifying images – leverage big data (customer demographics, consumption records, etc.) to parameterize a DL model that can simulate the likely response to new product/service configurations (e.g. new credit card with cash rewards, moderate interest, zero interest on balance transfers, etc.)
  • NLP is a branch of AI that helps computers understand, interpret and manipulate human language – NLP is shaping the future of banking with voice assistants and ubiquitous computing.
  • CV s a field of AI that trains computers to interpret and better understand the visual world –  CV is transforming financial services by using appealing visuals and new solutions for a new world where seeing is believing

These new-age FinTech developments are leading to a continuous transformation of the financial services workforce. The changing landscape and evolving financial services resource pyramid is presented in the diagram above. I would like to highlight a few trends reshaping the talent of financial services on this blog post.

  • AI automating business-as-usual activities of financial services: Robots and AI already started addressing key pressure points, reduce costs and mitigate risks. Building capabilities to target a specific combination of capabilities such as social and emotional intelligence, natural language processing, logical reasoning, identification of patterns and self-supervised learning, physical sensors, mobility, navigation and more are in swing. The goal is to look far beyond replacing the bank teller. There are whole categories of work that had not been seen as cost effective to automate. However, with lightweight software ‘bots’, workers are freed up to focus on higher value activities.
  • Changing patterns with Human vs Machines foray: Are financial services firms moving to re-shoring of work with talented machines? The answer seems to be, Yes. In the last two decades, many financial firms have ‘offshored’ repetitive tasks to lower-cost locations such as India, China, and Poland. However, relative costs for labor in those regions have started to rise. Combine this with improvements in robotics and AI capabilities and machines are becoming credible substitutes for many human workers. As the capabilities continue to improve and technology continues to drive down the cost of machines, these forces will combine to spur re-shoring, as more tasks can now be performed at a competitive cost on-shore. Even functions that seem dependent on human input, such as product design, fraud prevention, and underwriting, will be affected. At the same time, the need for software engineering talent will continue to expand
  • It is not just automation, Technology is picking high-end work: ML is enabling next-generation algorithmic trading systems are moving from descriptive and predictive to prescriptive analysis, improving their ability to anticipate and respond to emerging trends. And while algorithm trading programs were once limited to hedge funds and institutional investors, private investors can now get access to them too. AI soon automate a considerable amount of underwriting, especially in mature markets where data is readily available. Even in situations where AI does not completely replace an underwriter, greater automation would allow humans to concentrate on assessing and pricing risks in the less data-rich emerging markets. It would also free up underwriters to provide more risk management, product development advice and other higher value support for clients.
  • While building machines, the real focus is on accessing the necessary talent and skills to execute strategies and win markets: Financial services firms lack the internal knowledge and expertise need to implement a customer-centric approach. For example, a mainframe programmer who maintains a core banking platform may not have the skills or interests to learn to code AI applications. Many senior IT executives, non-IT staff-members, and even technical personnel do not have the skills needed to build and operate an effective digital channel offering. Financial institutions are starting to realize they will need talent with very different skills. This might mean finding more industrial engineers for robotics work, or retraining underwriters to do higher value work once AI is used to automate certain existing functions. But the issue runs deeper than developing a different competency model. First, firms to understand what is already working and what needs to be done differently. This might involve changes across the human capital strategy through revitalized recruitment, learning and development, partnering and cultural initiatives.
  • The contingent workforce is creating the talent-exchange mindset: financial firms need to address is the growing preference for flexibility and entrepreneurship among many in the labor force. In the United States, the US Chamber of Commerce has found that 27% of the labor force is currently self-employed, and some believe that this ‘contingent workforce’ could rise to 40% or more within several years. Practically, for this reason alone, financial institutions will need to adopt a ‘talent exchange’ mindset, leveraging part-time and/or self-employed individuals in a creative manner. This may range from bidding out specific tasks or work to expanding the use of seasonal or temporary workers. Of course, this will introduce challenges around culture and quality, and this will introduce new opportunities as well. For example, we might see employers using online platforms to manage confidentiality and legal risks in creative ways.

Artificial Intelligence capabilities impacting the financial industry and thereby attitudes toward work continue to change, some of the attributes that have benefitted institutions in the past such as big firm and stable employment are slowly losing their appeal. Refreshing financial firm’s approach to recruiting, learning and development, and culture may offer an effective way to address issues that FinTech has brought into the open market.

Welcome your ideas in further spotting future trends in financial services workforce.

 

AI in Healthcare

AI
Healthcare fueled by AI/ML/DL:
AI Use Cases in Healthcare:
Roadmap to Implement AI in HC
Commoditizing AI/ML in Healthcare:
AI/ML Impacts on HC. AI and machine learning are already delivering value in HC. The following are high impact areas.

AI in Operations (“AIOps”)

AIOps

Recently I was searching for verbatim “AIOps” on Google and got 624K results. Without many surprises noticed that there have been over 100 times rise in search trends since July 2017. That signifies the momentum for AI led Operations.

As my curiosity on AIOps increased, I looked at market opportunity for AIOps. From MARKETSandMARKETS analyst data, the global AIOps platform market size is expected to grow from USD 2.55 billion in 2018 to USD 11.02 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 34.0% during the forecast period (2018–2023).

In this blog post, I am attempting to capture some highlights gathered from my learning curve over a past year or so. Refer to the schematic above that provides a high-level “AIOps Framework”. The following are key elements of the framework.

“AIOps” Verbatim Defined: Simply stating AIOps stands for Artificial Intelligence for IT Operations. Extending AIOps to business operations is inevitable in near future. Adding further, AIOps automates various aspects of IT and utilizes the power of artificial intelligence to create self-learning programs that help revolutionize IT services

AIOps Context: There is a significant opportunity to leverage AI for analyzing enormous data being created by IT and business operations tools, to increase the efficiency of operations, speed up services delivery and ultimately create superior user experiences. The resulting power of AIOps is enabling the progress from siloed to integrated operations backed by intelligent insights.

Signals: In today’s business and IT operations environment, the user is adapting multiple channels of communication for ease and enriched experience. So the backend operations teams as well should expand their ability to sense, analyze and respond to such structured, unstructured and semi-structured data signals. With this in mind, the AIOps platforms are being developed with built-in capabilities to receive and response signals that can encompass any events, alerts, service requests, IoT sensor data, Email, Video, Text, Voice support, UX, Social channels and many other forms.

Interfaces: The way enterprise operations backbone interfacing with signals and external queries also is shaping up in this transformation.

  • The first layer is Machine-First: Giving software/machine/bot the first act on sensing and responding to operations requisitions not only improves the automation of repetitive tasks but also augments cognitive intelligence in complementing human intelligence.
  • Human-Next Touchpoints: Human-next layers take up the operations requisitions that are not solvable by machines. These are the requests which involve human interventions.
  • Ensuring Reliability of Services: Alongside the above two layers, taking an engineering approach to services reliability for constant monitoring, triaging and incorporating insights from advanced analytics of enterprise data brings the culture of continuous improvements and stability to operations.

AIOps Platform: The entire AIOps ecosystem is based on the underlying Platform and Enterprise Core that ties all the components together. As Gartner defined, “Artificial Intelligence for IT operations (AIOps) platforms are software systems that combine big data and AI or machine learning  functionality to enhance and partially replace a broad range of IT operations processes and tasks, including availability and performance monitoring, event correlation and analysis, IT service management, and automation.”

As businesses are increasingly software-driven, operations downtime is becoming more costly and slow is the new down. This is leading businesses to proactively manage and improve experiences of services, applications, cloud, and networks. Along with this business 4.0 is digitally shifting the businesses offering the technologies that increase the volume, velocity, and variety of data. As traditional systems and manual efforts are facing challenges in correlating and analyzing the data or alerts, AIOps is stepping up to augment the enterprise intelligence in operations.

To conclude, the future is bright for IT and business operations with AIOps. The increasing shift of organizations core business toward the cloud, raising investments in the AIOps technology ecosystems, exponentially growing data volumes and increasing end-to-end business application assurance and uptime are driving the growth of AIOps market demand.

 

 

Living in an Artificially Intelligent World

imagesI was reading an article on new research led by the University of Adelaide on the subject of an AI’s ability to predict a patient’s lifespan simply by looking at images of their organs. After taking a deep breath picked up my coffee and started walking thinking about the deeper engagement of AI in human life. While paying for coffee a few minutes ago, I realized that my credit card got misplaced and called the Bank customer service to inform. A bot attended my call and navigated through the issue and without getting a human agent involved, bot blocked my lost card and placed a new one. I am sure it would have simultaneously updated records in the backend with lost and new card information in multiple systems beyond handling customer communications. This is a classic example of automating a standard business process and corresponding workflows. Then I realized ANI (Artificial Narrow Intelligence) has already made inroads into human daily life.

While contemplating further on AI reach on our lives, reached my home. My ten-year-old daughter approached me handing over the home phone and said, I am holding a call for you and it seems that “bot thingy” is on the line. She was right, it’s an automated calling service from credit fraud services. The bot enquired about the recent loss of my card, security, and privacy related queries. Then transferred the call to a human agent, who confirmed card replacement and started offering adjacent services like fraud protection, started analyzing the credit situation identifying potential needs offering a new credit card that in a way precisely address my requirements. My surprise went to the next level, as AI started traversing deeper in my life routines. Of course, post my last call to the bank, cognitive AI might have picked up from ANI and started advanced analytics on data deriving next level insights and triggering a bot to make a follow-up call. As bot done its preliminary job handing over to the human agent, this cognitive AI started helping him in offering adjacent products the make perfect sense in my scenario. The bot may sooner completely replace human interface. Isn’t it nothing but AGI (Artificial General Intelligence)? A machine that could successfully perform any intellectual task that a human being can or aid human being in doing so.

Perfect! I walked into the kitchen and got into a dialogue with my wife. We decide to go shopping. She was on her iPhone as we walk through the aisles, and I noticed that an NLP chatbot advising her on retail product recommendations with a greater personalization with rich images and connecting to VR interface enabling product tryout. What an intrusion of AI in every walk of life. This I call it ASI (Artificial Super Intelligence).

Albeit, in a day of life, I traversed with all three phases of AI with increasing degrees of influences on daily routines, needs, and decisions. Going back to the article I read in the morning, I felt humans natural dependency on AI is on a rising path. 

Digital Revolutions

DR

Markets have been constantly evolving from pre-internet era of viscous state through fluid state over last decade with internet democratized access to information, reducing buyer-seller information asymmetry. Digital Revolutions with the advent of AI, Blockchain, Robotics, AR/VR, and hyper connected driven IoT technologies are forcing companies to functioning in a state of super fluidity in recent times.

Fortune 1000 organizations and VC backed startups are applying AI, ML, AR/VR, Blockchain and IoT to empower enterprises to make intelligent decisions, prioritizing and driving next-gen innovations improving the success rates. As an enthusiast envisioning the success of superfluid markets and with know-how of recent technology developments, I would like to summarize below the driving forces of Digital Revolutions

  • Key characteristics of Digital Revolutions: As businesses are trying to become intelligent enterprises with real times responses, there is an increasing demand for dematerializing their physical assets with digital touchpoints. In these times, business operations, supply chain, supporting infrastructure and technology, and enormous volumes of data becomes software driven making enterprises become hyper connected seamlessly and derive proactive insights. This is leading to Digital Revolutions offering a rich user/consumer experiences.
  • Blockchain and IoT are expediting the pace of Digital Revolutions: We have now entered the age of superfluid markets, which represents the convergence of multiple forces. While many transaction costs were reduced during the fluid market period, costs around contracting, trust and the policing and enforcing of contracts remained high. The maturation of blockchain technology as a transaction engine in which trust is “built in” will reduce even these costs. With the Internet of Things, physical goods are being sensed, tagged and linked to the Internet, with the promise to better match supply and demand. Intelligent agents will soon anticipate buyer preferences before buyers themselves. The intersection of blockchain and IoT will create autonomous markets that run themselves cheaply and efficiently. The gig economy implies increasingly superfluid labor markets. And these developments may just represent the tip of the iceberg. Examples include,
    • Blockchain potentiality to offer intrinsic business value in integrated utilities management with a reliable, low-cost way for recording validating financial or operational transactions across a distributed network with no central point of authority. Peer-to-peer energy trading, Billing of AV charging stations, Power Ledger and Smart grid management systems are few use cases.
    • Visa’s IoT platform designed to bring the point-of-sale everywhere by allowing businesses to introduce secure payment experiences quickly to any device connected to the IoT. Visa’s vision and belief is to securely embed payments and commerce into any device—from a watch to a ring to an appliance or a car.
  • Robotics and Bots are first steps of organization in taking advantages of Digital Revolutions: Robotics are emerging to pick up precision heavy activities and “bots” leveraging AI is taking customer service and experience to the next level. Take a look at inVia that is introducing “robotics-as-a-service” to the new economy with first “goods-to-box” warehouse packing system. This new robotics system that put goods directly into shipping boxes. Instead of investing in a fleet of robots, customers pay a monthly service fee.
  • Artificial Intelligence and Machine Learning are big boost to Digital Revolutions: AI combine with machine learning is paving ways to new business models. AI technologies already pervade human lives progressing beyond simply building systems that are intelligent to building intelligent systems that are human-aware and trustworthy.
  • AR/VR is becoming a driving force of Digital Revolutions. Let us take examples of retail industry transformation. Virtual reality (VR), along with its sister technology augmented reality (AR), offers retailers the opportunity to transform how people shop. One customer might try on shirts without having to travel to the store. Another might order furniture on the spot, confident that it’s right for the house. Applications using either technology stand to eliminate customer pain points, elevate customer service, and create a differentiated, personalized customer experience. The successful incorporation of VR and AR into retail models also has the potential to vastly change the way retailers are thinking about stores of the future

Digital Revolutions are leading to superfluid markets which will continue to evolve differently across different industries and companies. These transformations are what we continue to explore into future. There is a pressing need for companies to collaborate exchanging ideas, trend spotting, and tap innovations to succeed in  future frictionless markets.

Next-Gen Education System

Edu

In gig-economy there is a tremendous opportunity to leverage the full potential of digital disruption including AI, Gamification, and Automation paving path to next-gen educational methods and job reorientation. Firstly, finding ways on how AI technologies could aid education methods, augment human skills in professional jobs and there by the challenges posed by AI. We commonly hear news like – an artificially intelligent computer system built by Google has just beaten the world’s best human, Lee Sedol of South Korea, at an ancient strategy game called Go. The Google program Alpha Go, actually learned the game without much human help. It started by studying a database of about 100,000 human matches, and then continued by playing against itself millions of times. As it evolved, it reprogrammed itself and improved. This self-learning program is based on a neural network, and theories of how the human brain works. Another classic example is Pearson – the world’s leading Education Company tapping IBM’s Watson as a virtual tutor for college students. With continued impact of AI on education and gig-economy, analysts are estimating a net reductions in jobs/workforce between 4% and 7% across various industries. It is simultaneously creating demand for high skilled digital workforce. Likewise AI and advance machine learning is paving new paths to education methods and future focus areas to complement and supersede machines to take full advantage of AI.

Second focus area is Gamification that has become the frontier of training, capitalizing on a new generation born into a computerized world. The idea behind the concept is to take elements of game design and logic and apply it to a work situation. One of the biggest companies to utilize gamification is McDonald’s, which introduced a new till system using a simulation game. Employees were asked to engage customers and use the till while under time restraints. Air Cargo Netherlands also used gamification when they needed to train employees on a specific utility. They created a game version of a new logistic system called Smartgate. They used the game to develop employees’ “chain thinking” and help them realize the consequences of their decisions in a risk-free environment.

Lastly, driving the automation agenda leveraging advances in robotics, artificial intelligence, and machine learning as machines match or outperform human performance in a range of work activities, including ones requiring cognitive capabilities. Examples include guiding customer service representatives to more quickly resolve customer problems and anticipate future purchases, quickly and securely reconciling mass overnight transactions for financial institutions, or giving time back to HR professionals by managing the time consuming on-boarding processes for new hires. Technical, economic, and social factors will determine the pace and extent of automation. Continued technical progress, for example in areas such as natural language processing, is a key factor. Beyond technical feasibility, the cost of technology, competition with labor including skills and supply and demand dynamics, performance benefits including and beyond labor cost savings, and social and regulatory acceptance will affect the pace and scope of automation. Hence the next-gen education should focus on learning futuristic competencies with an aim to complement realizing full potential of automation.