Tomer Guriel is the co-founder and CEO of ezbob, a proprietary core lending platform that enables banks to migrate to digital services.
I predict that soon we will not be able to imagine our lives without artificial intelligence (AI) and machine learning (ML), with so many tools reshaping the landscape. These tools are evolving fast from being advanced search engines to providing contextually relevant, near-human responses, streamlining research and simplifying decision-making.
In banking and financial services, the next leap involves agent-based AI—AI systems capable of proactive decision-making, automation and personalized recommendations without requiring direct human intervention. This innovation opens doors for transformative changes in how consumers and businesses interact with their financial institutions.
Agent-Based AI: The Next Frontier In Financial Services
Agent-based AI is poised to revolutionize financial services by offering actionable suggestions and automating customer interactions. Let’s explore two compelling use cases: one focused on individual consumers and another on small and medium enterprises (SMEs). These examples demonstrate the potential of agent-based AI to deliver tailored, value-driven outcomes.
Transforming Consumer Banking For Individuals
Today’s approach to consumer portfolio management often applies broad, high-level strategies that lack personalization. However, agent-based AI enables more precise interventions tailored to individual circumstances.
Current Mortgage Payment Process
Consider a typical borrower with a £250,000 mortgage over 25 years at a fixed interest rate of 5.99%. The total interest payable under this scenario would be £232,767.69, based on 300 monthly repayments of £1,609.23.
In the traditional mortgage payment process, income flows into the consumer’s current account, and mortgage payments are deducted monthly via direct debit.
Enhanced Process With Agent-Based AI
Agent-based AI can optimize this process by analyzing transaction patterns and identifying opportunities for cost savings. For example, if the customer’s mortgage payments are voluntarily increased by 15% per month by the customer (e.g., instead of paying £1,609.23, paying £1,850.61) then total interest payable drops to £166,957.32—a saving of £65,810.37 or 28.3% over the loan term. Alternatively, this could result in a reduced loan term for the borrower—meaning that a loan start date of 1st January 2025 would not end on 1st January 2050, but on 31st October 2043.
Personalized Solutions At Scale
Agent-based AI introduces several innovations for consumers:
Automated Payment Adjustments: AI can monitor income patterns and adjust direct debit dates to optimize interest savings, requiring only consumer notification.
Income Forecasting: Using tools like autoregressive integrated moving average (ARIMA) models, AI can predict income trends, enabling tailored financial recommendations such as overpayment strategies or savings plans.
Proactive Suggestions: AI can suggest ways to maximize financial efficiency, such as overpaying mortgages or diverting surplus income into savings or investment accounts.
By providing these services, banks can not only enhance customer outcomes but also align with regulatory requirements, such as the FCA’s Consumer Duty regime, which emphasizes fair value and customer consideration.
Revolutionizing SME Banking: Smarter Cash Flow And Tax Management
For SMEs, cash flow optimization is critical, and agent-based AI can play a pivotal role. Unlike consumers, SMEs often prioritize managing payments to authorities like HMRC and maximizing the utility of surplus cash.
Agent-Based AI Solutions For SMEs
Overnight Cash Management: AI can predict surplus cash on a nightly basis and deposit it into interest-earning accounts, returning the funds with accrued interest the following day.
Integrated Tax Management: AI can integrate with online accounting systems to calculate value-added tax (VAT), corporation tax and other obligations, automating tax planning and fund allocation.
Interest Optimization: By managing surplus cash effectively, SMEs can reduce interest costs on credit commitments, freeing up resources for growth.
These capabilities can help streamline SME financial management while enhancing their ability to meet obligations efficiently.
The Long-Term Impact Of Agent-Based AI On Financial Institutions
The integration of agent-based AI into banking and financial services is more than a technological upgrade—it’s a shift in how institutions interact with and empower their customers. By leveraging AI’s ability to analyze data, predict trends and automate decisions, banks can create more meaningful, personalized and efficient experiences for both consumers and businesses.
Caveats Of Agentic AI In Finance
Agent-based AI provides tools that financial institutions and their customers can use to make decisions about eligibility and creditworthiness, but not without risk. Here are some examples of the issues to consider and their possible mitigants:
1. Regulatory And Compliance
Financial markets are (rightly) regulated, and AI-driven decision-making must adhere to strict compliance requirements. It’s often the case that there’s not necessarily the same speed of change in regulation as there is in model development. AI agents may struggle with interpretability, making it difficult to ensure regulatory transparency and accountability, and financial institutions and nonbank financial institutions (NBFIs) must be prepared for increased scrutiny from regulators regarding how AI models make decisions—and how these can be independently validated.
2. Data Quality And Bias
AI agents rely on high-quality, unbiased data in their training datasets to effectively function. Incomplete, outdated or otherwise biased data can lead to suboptimal decisions, potentially amplifying inequalities or exposing institutions to other risks. Continuous monitoring and data governance are necessary to mitigate these issues.
3. ‘Black Box’ Problem/Lack Of Transparency
Many AI models, and particularly deep-learning-based agents, can be perceived to function as “black boxes,” making it challenging to explain their reasoning behind risk assessments as required by regulation. This opacity can make it difficult for financial institutions, NBFIs and regulators to trust AI-driven recommendations.
4. Operational And Integration Challenges
Integrating agent-based AI into legacy financial systems can be both complex and costly. Investment in infrastructure, employee training and ongoing maintenance to ensure seamless operation is an absolute necessity, and the transition may also disrupt existing workflows, requiring significant adjustments from staff.
5. Market Manipulation And Ethical Risks
AI agents operating in financial markets can behave unpredictably, potentially engaging in behaviors that resemble market manipulation, high-frequency trading risks or herd-like movements that amplify volatility (such as a “flash crash,” albeit temporarily). Ethical concerns around fairness, market stability and unintended consequences must be carefully considered and mitigated; having a human in the loop can be a key to limiting this risk.
6. Cybersecurity And Fraud Risks
AI-driven financial systems may become attractive targets for cyberattacks, with malicious actors manipulating AI models, exploiting vulnerabilities or using AI-generated synthetic/fake transactions to commit fraud. Robust security measures, including adversarial and penetration testing and AI-specific threat detection, are essential to safeguard financial assets.
7. Overreliance On Automation
While AI can enhance efficiency, overreliance on automated decision-making can be dangerous, especially in high-stakes financial environments. Human oversight remains crucial for intervening when AI makes unexpected or incorrect decisions, preventing costly errors or financial crises.
Final Thoughts
As AI continues to evolve, its role in fostering financial well-being will only grow. Whether it’s helping consumers save on mortgages or enabling SMEs to optimize cash flow, agent-based AI represents the future of intelligent, customer-centric banking.
The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation.
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