Artificial Intelligence (AI) and the Bank of the Future

Related Topics: Digital Strategy, Financial Services

by Joe Mendel – 

The Bank of the Future will be a cloud-native, digital native entity that is founded on behavioral insights generated by intelligent hyper-automated systems.  Existing organizations will compete with the digital start-ups and neo-banks by driving innovation from within in addition to partnering with open-banking API solution fintech providers.

Great strides are being made with quantum computing, AI and ML, and Augmented Reality (AR) as it applies to banking.

  • Re-imagined engagement layers that deliver seamless customer experiences at opti-channel, and omni-channel banking levels and across partner ecosystems
  • AI powered decisioning levels that automate and personalize predictive and prescriptive engagement opportunities
  • Advances in architecture using data fabric concepts to optimize speed and access
  • Self-assist and intelligent BOTs that optimize and customize personal interactions and that support internal knowledge workers response capabilities where human interaction is engaged
  • Core banking systems born on cloud technologies like Thought Machine’s cloud native core banking product, Vault. Their cutting-edge solutions offer a highly customizable configuration layer that incorporates intelligent workflows, smart contracts, advanced analytics and reporting and open banking API’s.
  • Quantum computing in finance and banking supports portfolio analysis, asset appraisal, and high-frequency trading. By creating algorithms built on possibilities in quantum computing, banks and financial institutions can mine more value from big data. Some current use cases being evaluated include:
    • Portfolio Analysis: Identification of the most attractive portfolios given thousands of assets with interconnecting dependencies
    • Fraud Detection: Quick and accurate detection of fraud indicators to enable proactive fraud risk management
    • Enhanced Cybersecurity System: Development of next-generation cryptography to protect confidential customer data
    • Optimization: Improved efficiency in clearing large batches of transactions that have varying credit, collateral, and liquidity constraints
    • High-Frequency Trading: Rapid execution of complex quantitative buy-sell strategies will improve financial firms’ abilities to generate greater returns while controlling risk.
    • Clustering: Grouping of seemingly disparate sets of assets to discover patterns in areas like asset performance, consumer sentiment, and risk aversion.
  • Augmented realities, made possible through 5G, allow users to superimpose digital information on top of the real-world environment to create a partially immersive experience via heads up display or smartphone screen. Typical early use cases focus on:
    • Self-service
    • ATM locator
    • Payment experiences
    • Customer acquisition
    • Security
    • Virtual branches
    • Financial portfolio visualizations
  • And the emerging role that Artificial Intelligence and Machine Learning play today in modern quality engineering. Niko Mangahas, Head of the Quality Engineering practice at RCG in his most recent eBook publication, “Organizations playing catch-up will benefit the most from ML technology.  In fact, the biggest successes would come from organizations that are early in their efforts to test and automate, and not those which are well on their way to advanced automation and highly mature and stable. The benefits generated from leveraging the technology actually lends itself to support other transformation initiatives and improvement activities.”

Banks today will have to “run faster” to get anywhere just to remain competitive in the coming years.  Additionally, they will have to become more accustomed to living in three states of evolution: Run the bank, Transition the Bank, and the Evolved bank in an ever cycle of developmental growth.

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