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.