Transmorphosizing Banking Through Artificial Intelligence

AI innovation in Banking

Banking is re-inventing itself as it always has

  • Giovanni di Medici setting up first credit banking systems in Renaissance Europe where charging interests was considered as a sin
  • The Dutch East India company trading in Tulips as stocks
  • The bond markets fueling the Rothschild to become one of the richest and influential family
  • The evolution of e-Wallets, UPIs, e-Money transfers giving a new definition to funds re-allocation
  • From physical branch-based model to mobile platforms
  • Human interactions with virtual interface like chat-bots/virtual assistants
  • Single player bank to multiple players who are traditionally not banks e.g. blockchain
  • Inclusion of social risks along with traditional-risk analysis
  • Mass market products to hyper-personalized products in real time

Why is it happening now and what changed?

  • Identifying Frauds with better accuracies
  • Cross Selling and Upselling using Chatbots/Virtual Assistance
  • AI-assisted portfolio management
  • IoT to better manage marketing across multi-customer touch points
  • Process automation like Contract Optimization
  • Reliance Jio: They already have the huge customer base over 120 M and this customer reach may increase exponential possessing threat to conventional bankers
  • PayTM: It has expanded from a wallet to marketplace with a customer base of 200 M. With humongous gamut of offers in their kitty and a favorable customer perception, they have already started putting a dent in the online transactions & payment market
  • In global markets: Jawbone and Amex have developed fitness + payment tracker “UP4”, thus helping payment channels as a natural extension of human everyday life or Westpac coming up with Geo-location specific marketing
  • P2P lenders and their alarming growth rate of 300% in the last couple of years

Can Banking be done differently to “thrive than just survive”?

  • Stopping a fraud before it happens- Like Manufacturing can we develop on the lines of “Preventive Maintenance” to sense trends before the “Boiler” fails
  • Incentivizing customers for having a high credit score — Like Insurance sector, where better drivers are awarded “No Claim” bonuses
  • Transforming Virtual Assistants to be Virtual Mentors — Help customers, not just with the best offers but suggestions to fulfill their dreams e.g. help to maximize savings for the down payment of a car by liquidating portion of existing saving portfolios like SIP, Mutual funds etc. seamlessly
  • Shared Risk Platform across multi-partners or other banks — like blockchain, where risks are shared among multiple stakeholders. A recent defaulter with good credit history applies for a loan. His current requirement can be funded by multiple banking partners each sharing a portion of the risk. The risk value, loan amount and lending time/rates can be evaluated through AI to mitigate risk and help customers with better experience while ensuring he is still the part of the banking system
  • Understand customer needs in real time based on what preferences influenced by one’s social setting e.g. what others who can influence him/her are spending on. Our decisions may be correlated by some facts but we humans may not behave rationally. So rather than predicting, since the possibilities through AI

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