CX and AI personalization to drive FinTech revenue to $638B

Iain Banks, regional vice-president, international markets, TTEC

Banking on CX leadership?

  • “McKinsey research found that the human touch is still important to bank customers in most markets revealing that only 13% of customers prefer doing everything remotely.
  • According to a J.D. Power survey, digital-only banking customers were dissatisfied with three areas: communication and advice; products and fees; and new account opening.
  • HSBC flagship New York branch has Pepper, its humanoid robot banking assistant. Pepper complements the branch team and helps customers with basic questions, product tutorials and information, allowing associates to answer more complex queries.
  • In the UK, NatWest has been testing Cora, its “digital human” banking assistant. If the pilot is successful, the assistant could be used in branches to answer basic customer enquiries.”

  • Fintech Platform Revenues to Reach $638 Billion in 2024: Juniper Research
    • “Research author Michael Larner said, ‘The distinction between the fintech suppliers and traditional incumbents will blur in the 2020s; digital engagement will become the norm. The winners will be those that provide personalisation allied to an outstanding customer experience.”
    • Fintech platform revenues will reach $638 billion by 2024, up from an estimated $263 billion in 2019; driven by increasing consumer acceptance of fintech-powered solutions, according to Juniper Research.”

    An executive’s guide to AI – McKinsey

  • One of the highest rated McKinsey articles of the year is titled, “An executive’s guide to AI.” It’s a quick overview including key elements and terminology.
  • Artificial Intelligence
    • AI is typically defined as the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, and problem solving.
    • Timeline
  • Machine Learning
    • Machine- learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction.

    Types of analytics

    • Descriptive, predictive, prescriptive
  • Major Types
    • Supervised, unsupervised, reinforcement

    Deep Learning

    • Deep learning is a type of machine learning that can process a wider range of data resources, requires less data preprocessing by humans, and can often produce more accurate results than traditional machine-learning approaches.
  • Major Models
    • Convolutional neural network CNN
    • Recurrent neural network

    Business Cases

      Predict Call Center Volume
    • Predict power usage in an electrical grid distribution
    • Detect fraudulent activity for credit cards
    • Simple low-cost image classification
    • Forecast product demand and inventory
    • Predict the price of cars
    • Predict probability of patient joining healthcare
    • Predict prices that will be paid for a product

    69% of time AI and Deep Neural out-perform existing techniques

    The highest monetary impact is in retail with estimated 0.4–0.8 $ Trillions.

    • The consulting firm McKinsey & Company estimates that Artificial Intelligence has the potential to create between $ 3.5 Trillions and $ 5.8 Trillion in value across nine business functions in 19 industries — annually
    • Artificial Intelligence will generate up to $ 2.6 Trillions in additional value in Marketing and Sales
    • Additional $ 200 Billion in value will be added to Pricing & Promotion and $ 100 Billion
    • The study of McKinsey shows that 69 % of time Artificial Intelligence and Deep Neural Networks are able to improve the performance beyond what existing analytic techniques were able to deliver.”