The Evolution of Banking by 2030

Detailed Overview

04 July 2025

1. Personal Financial AI Agents in 2030
Service Suite Delivered

• Payments (Fiat and Stablecoin):

AI agents will facilitate seamless payments in traditional and digital currencies, interfacing with bank APIs and card networks for fiat payments, and supporting stablecoin transactions for faster, cheaper transfers. For example, if a user needs to pay an international vendor, the agent might convert GBP to a regulated GBP stablecoin and transmit it over blockchain rails for instant settlement. The agent handles currency conversion, fee optimization, and compliance checks in the background. All payments come with detailed reporting and analysis.

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• Idle Balance Optimization (Staking and Deposits):

AI agents automatically allocate idle funds to yield-bearing options, such as high yield savings, money market funds, or DeFi staking pools. They dynamically shift funds to the best risk-adjusted returns, handle wallet keys or custody, diversify across protocols, and auto-withdraw if risks rise. Smart contracts on networks like Ethereum automate these processes, providing transparency and security. However, returns are contingent on market conditions and regulatory permissions.

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• Dynamic Investment Management (Crypto and Tokenized Stocks):

AI agents act as autonomous robo-advisors, allocating portfolios across cryptocurrencies, tokenized equities, ETFs, and traditional securities. They monitor real-time market data, macro news, and social sentiment, making rapid adjustments to optimize returns or manage risk. By 2030, tokenized stocks and bonds will enable 24/7 trading and fractional ownership, with AI agents executing sophisticated strategies akin to hedge funds—though returns and risks must be clearly communicated. 

System Flow

A personal AI banking agent operates at the centre of a web of connections linking the user’s devices to various financial platforms. The agent interfaces with traditional banking APIs (for fiat accounts, credit, and custody), connects to DeFi protocols via smart contract APIs, and  interfaces with stablecoin networks/blockchains for digital asset transfers.

Example User Scenario:
The AI agent consults the user’s financial data and external market data, then informs a small business user that they have a current account balance, net of upcoming direct debits, of £2000. The agent requests
permission to invest the balance across preconfigured savings options, whilst reserving sufficient funds to pay a supplier invoice due next week. Once approved, the AI agent executes the task.

Architecture and Deployment

• By 2030, banks will likely operate a regulator-certified cloud control plane that works in real time with their on-premises ledgers. In this model, AI decision logic, payment orchestration, and liquidity engines run in an elastic cloud environment—enabling rapid, secure model updates and scalable computing. Core transaction ledgers remain on protected bank hardware or private cloud, but share rich, real-time event streams with the control plane. This gives the AI full context for decision making, while preserving sovereignty and low latency settlement.

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• Supervisors gain live observability into the cloud control plane, with transparency over model lineage and data flows—far exceeding traditional audit cycles. Privacy is maintained through end-to-end encryption, data minimization, and zero knowledge proofs, ensuring customer data stays within the bank’s secure perimeter unless a lawful, cryptographically validated request is made.

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• This “hollow core” approach delivers auditability and agility without shifting every ledger workload to the cloud. The cloud acts as the analytical and orchestration layer, while settlement integrity remains

anchored in bank controlled infrastructure.

2. New Revenue Streams for Banks

Banks that deploy AI agents can tap new revenue streams:
 

• Institutional Grade Portfolio Optimisation:

Banks can charge performance fees or subscriptions for managing customer investments algorithmically. Retail investors could access quant strategies previously reserved for hedge funds, but
returns are not guaranteed and depend on market conditions.
 

• Stablecoin Merchant Services:

As UK regulators bring sterling stablecoins into the payment system, banks can earn interchange like fees on stablecoin transactions. The UK is the first jurisdiction proposing to offer stablecoin issuers access to central bank reserves, paving the way for mainstream stablecoin payments.

• Cross-Selling:

Banks can leverage their trusted brands and regulatory compliance to cross sell AI agent services. According to a 2019 Deloitte survey, 81% of consumers trust their primary bank with data, far more than
tech companies (44%), giving banks a trust advantage in offering AI financial tools.

3. Competitive Edge Through Regulation and Trust

Strict EU/UK regulations (including MiCA, the EU AI Act, and the UK’s FSM Bill amendments) ensure robust oversight, reinforcing consumer trust and safety. PRA model-risk management principles further strengthen the
regulatory framework. Banks that proactively adopt AI agents within regulated frameworks can differentiate on security and trust, whereas laggards risk losing tech savvy customers to less regulated alternatives.

4. Regulatory Threats & Shifting Landscape
Regulators face a dilemma—unregulated AI financial agents (potentially operating offshore or open source) could tempt customers with high yields and rapid innovation, bypassing traditional banks. This raises risks of fraud, bias, and financial instability if left unchecked. Central banks could see a diminished role in monetary policy as more lending and investing flows to DeFi and stablecoins, weakening the link between central bank rates and market rates. Global payment networks are also in flux—SWIFT may be challenged by blockchain based rails, and traditional infrastructures must evolve to support 24/7 tokenized asset trading.

5. 24/7 Markets and AI Driven Trading
By 2030, tokenized stock markets operating 24/7 will likely be a reality, enabling retail investors (via AI agents) to engage in high frequency trading and arbitrage. AI agents can monitor these markets continuously and execute split second trades across time zones, increasing market efficiency and volatility. Regulators will need to extend surveillance to a 24/7 cycle.

Risk Notes:
The rise of AI driven trading introduces risks such as counterparty opacity, oracle failure, and ESG scrutiny. Pilot
sandboxes with capped exposure are recommended to mitigate these risks. Additionally, the ubiquity of AI
agents could lead to systemic liquidity feedback loops, herding, and flash crashes. Circuit breaker design and
robust supervision will be essential to manage these second and third order impacts.

6. Strategic Use Cases Demonstrated

• Simple Use Cases:

Automatically sweeping idle cash into stablecoins earning higher APY than typical bank deposits.

• Complex Strategies:

Splitting portfolios between borrowing stablecoins and staking in DeFi for positive carry.

• Corporate Treasury:

AI agents can manage company liquidity across bank accounts, money markets, and crypto yield platforms, integrating with standards like ISO 20022 for seamless real-time money movement.

7. Action Items for Banks
Banks that begin controlled pilots in the next 12–18 months will position themselves ahead of the learning curve. Banks should use their regulatory know how to shape proactive frameworks for AI agents, ensuring
KYC/AML compliance and certifying AI models for fairness and transparency. Such regulation can protect consumers from rogue AI risks while cementing banks’ role as trusted AI finance providers.

8. Risk and Systems Thinking
The deployment of AI agents introduces operational, market, and regulatory risks. Systemic risks such as crowding effects, herding, and flash crashes must be addressed through circuit breaker design and robust
supervision. Banks and regulators must collaborate to ensure the stability and integrity of financial markets as AI becomes ubiquitous.

About the Authors
This document was prepared by David Charitos and Allan Gray of TASL.ai. TASL.ai is currently developing a pilot/demonstrator system to showcase Personal Financial AI in real world banking environments. TASL.ai is
available to advise banks and financial institutions on AI adoption and integration. Our approach is collaborative and supportive, aiming to help institutions navigate the evolving landscape of AI driven finance.

This document is intended as industry insight and does not constitute financial or legal advice. All claims and forecasts are subject to market conditions and regulatory developments.