Banking From Your Brain? 5 Mind-Bending AI Ideas That Will Change Banking & Financial Services Forever
The Future of Money is Here: 5 AI & ML Breakthroughs That Will Make Traditional Banks Obsolete
Introduction
Matthias Thiemann, professor at Sciences Po Paris, and author of a new book “Taming the Cycles of Finance? Central Banks and the Macro-prudential Shift in Financial Regulation (Cambridge University Press, 2024)” argues that the financial system was already headed for a crisis before the COVID pandemic. He implies that the pandemic's economic effects masked the underlying problems, and we risk overlooking those problems if we focus solely on COVID. Thiemann points to overvaluation in commercial real estate as a specific example of a pre-pandemic issue causing trouble today.
In another update Barbara Pianese highlights how the governments are strugging to deal with the risks posed by the expansion of non-bank financial institutions, comprising nearly half of global financial assets.
In the face of such gloomy scenarios, the era of rapid technological innovation presents a transformative opportunity for the financial sector. With the advent of Artificial Intelligence (AI), traditional banking practices are being reimagined and reshaped, promising a future where financial services are not only more efficient but also deeply integrated into our daily lives. This essay delves into five disruptive startup ideas driven by AI that have the potential to transform banking as we know it, along with a speculative timeline of breakthroughs and transformations in the banking ecosystem.
1. Cognitive Banking
Imagine a banking experience that seamlessly integrates with your thoughts and preferences, anticipating your financial needs before you even articulate them. Cognitive banking, powered by advanced AI algorithms, aims to do just that. By analyzing vast amounts of data from a customer's past transactions, behaviors, and even physiological responses, cognitive banking systems can predict future financial decisions and offer personalized recommendations tailored to individual needs. For instance, if a customer is about to overspend, the system could suggest budgeting strategies in real-time.
Timeline:
- 2025-2030: Initial prototypes of cognitive banking systems are developed by tech startups, focusing on basic financial recommendations.
- 2030-2040: Cognitive banking becomes mainstream as major banks adopt the technology, offering highly personalized financial services to customers.
2. Blockchain-Based Smart Contracts
Blockchain technology has already disrupted the financial industry with cryptocurrencies like Bitcoin and Ethereum. However, the true potential of blockchain lies in its ability to facilitate smart contracts—self-executing contracts with the terms of the agreement directly written into code. AI-driven smart contracts could automate various financial processes, such as loan approvals, insurance claims, and trade settlements, eliminating the need for intermediaries and reducing transaction costs and processing times.
Timeline:
- 2020-2025: Initial experimentation with AI-powered smart contracts in niche financial applications.
- 2025-2035: Wide-scale adoption of smart contracts across various financial sectors, leading to increased efficiency and transparency.
3. Robo-Advisors with Emotional Intelligence
Robo-advisors have gained popularity in recent years for their ability to provide automated investment advice based on algorithms. However, the next evolution of robo-advisors involves imbuing them with emotional intelligence—AI systems capable of understanding and responding to human emotions. By analyzing factors such as tone of voice, facial expressions, and user feedback, robo-advisors can tailor their recommendations to align with the emotional needs and risk tolerance of individual investors, fostering trust and engagement.
Timeline:
- 2022-2025: Initial integration of emotional intelligence features in robo-advisors, focusing on basic emotional recognition.
- 2025-2030: Advanced emotional intelligence algorithms enable robo-advisors to provide highly personalized investment advice, leading to increased user satisfaction and loyalty.
4. Quantum Computing for Financial Modeling
Quantum computing represents a paradigm shift in computing power, with the potential to solve complex financial problems that are currently intractable for classical computers. By leveraging the principles of quantum mechanics, quantum computers can perform calculations at speeds exponentially faster than traditional computers, enabling more accurate and efficient financial modeling, risk assessment, and algorithmic trading strategies.
Timeline:
- 2020-2030: Incremental advancements in quantum computing technology lead to the development of rudimentary financial applications.
- 2030-2040: Quantum computing becomes mainstream in the financial industry, revolutionizing risk management, portfolio optimization, and high-frequency trading.
5. Biometric Authentication for Seamless Transactions
Traditional authentication methods such as passwords and PINs are vulnerable to security breaches and inconvenient for users. Biometric authentication, which verifies a person's identity based on unique biological characteristics such as fingerprints, iris patterns, or facial features, offers a more secure and user-friendly alternative. AI-powered biometric authentication systems can accurately authenticate users in real-time, enabling seamless and secure transactions across various banking channels.
Timeline:
- 2015-2025: Adoption of biometric authentication in banking applications, primarily for mobile banking and ATM transactions.
- 2025-2035: AI advancements improve the accuracy and reliability of biometric authentication, leading to widespread adoption in all banking channels, including online and in-person transactions.
Summary
The future of banking is undoubtedly intertwined with AI, with innovative startups leading the charge towards a more efficient, personalized, and secure financial ecosystem. From cognitive banking systems that understand your financial needs to quantum-powered financial modeling and blockchain-based smart contracts, the possibilities are limitless. While the timeline for widespread adoption may vary, one thing is certain: the era of AI-driven banking is upon us, and its impact will be nothing short of revolutionary.
References
https://www.thebanker.com/Non-banks-can-be-too-big-to-fail-at-least-in-the-US-1706776073
Bollen, J., & Mao, H. (2017). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.
Li, Y., & Wang, S. (2020). A Review of Blockchain and AI Innovations in Financial Services. Journal of Finance and Data Science, 6(2), 111-124.
Statista Research Department. (2023). Number of mobile banking users worldwide from 2019 to 2023 (in billions). Statista.
Vapnik, V. (1998). Statistical learning theory. Wiley.
Weng, J., & Liu, Y. (2018). Emotion Recognition in Conversations with Transfer Learning from Generative Conversation Modeling. arXiv preprint arXiv:1806.00781