AI-powered financial advisers and assistants are beginning to change how people manage money, from budgeting and investment planning to lending decisions.
Tasks that once required a banker, financial planner, broker, or insurance agent may now be handled by a chatbot that can explain financial options, categorize spending, suggest a savings plan, or help a lender determine whether someone qualifies for credit.
The shift is still in its early stages, but it is becoming harder to ignore.
The Center for Retirement Research at Boston College recently argued that AI could improve financial planning by helping advisers combine data from different tools, summarize client meetings, and assist do-it-yourself savers with complex money questions.
The report also pointed to an important limitation: AI can be useful for structured questions, but it can still produce misinformation when the topic is nuanced or the user provides incomplete information.
From budgeting tips to wealth management
Perhaps the most familiar use case is the AI budgeting assistant. These tools can scan transactions, identify spending patterns, flag unusual activity, and suggest where a user might save more.
For consumers without access to a human adviser, these tools can make financial guidance more accessible and less intimidating.
In wealth management, AI’s role could extend even further. Instead of simply showing charts, it could analyze a person’s income, expenses, risk profile, investment horizon, and retirement goals.
The CRR article described AI’s potential as a data overlay that can pull information from different financial planning systems into one view, helping advisers spot opportunities or risks more quickly.
That same idea could eventually apply to retail fintech apps. A user might ask whether to build an emergency fund first, pay down debt, invest in a fund, or delay a large purchase. The answer may come instantly, but the harder question is whether the tool is simply educating the user or already providing regulated financial advice.
Lending raises even bigger questions
AI is also moving into lending, where the stakes are higher. A budgeting assistant may offer poor advice, but a credit model can affect whether someone receives a loan, at what rate, and under what terms.
The Bangko Sentral ng Pilipinas (BSP)’s 2024 thematic review on AI and machine learning in Philippine financial services listed credit risk scoring, fraud management, recommender systems, e-KYC or liveness checks, and assistive AI or GenAI among major use cases.
The same BSP review found that 21 of 48 surveyed institutions had deployed at least one AI system in production, while 29 had included AI or machine learning in their roadmap.
This is where the goals of financial inclusion and consumer protection intersect.
AI could help lenders assess borrowers with thin credit histories, but models trained on incomplete or biased data may also reinforce unfair outcomes. The BSP review noted gaps around accuracy, hallucination, data quality, and ethical issues, and stressed that accountability for decisions lies with humans, not AI systems.
The regulator’s challenge: who is responsible?
IMAGE CREDIT: SwiftPay
In the Philippines, the regulatory conversation is not starting from zero.
Republic Act No. 11765, or the Financial Products and Services Consumer Protection Act, protects consumers through requirements for fair treatment, transparency and disclosure, protection against fraud and misuse, data privacy, and timely complaint handling.
The National Privacy Commission has also issued guidelines on AI systems that process personal data. The advisory says personal information controllers must explain the nature, purpose, extent, risks, expected output, and impact of AI-based processing.
It also requires accountability, governance measures, bias monitoring, and mechanisms for meaningful human intervention when automated decisions create significant risks to data subjects.
Jenny Q. Ta, founder and CEO of WEAL28H, says one distinction is often overlooked as AI becomes more deeply embedded in financial services: the AI agent itself is not the investment adviser. Rather, the regulated entity behind the technology is responsible for the advice or service delivered through it. While her analysis is based on U.S. investment adviser law, the broader principle resonates across jurisdictions—that accountability rests with the institution deploying AI, not the technology itself.
Ta notes that fiduciary duty, supervision, recordkeeping, disclosure, and liability attach to the registered entity, not the software. “The model did it” is not a defense, she argues, because the firm remains responsible for every output that reaches a client. As financial institutions increasingly incorporate AI into customer-facing services, the distinction underscores why governance frameworks must focus not only on what AI can do, but also on who is accountable when something goes wrong.
For fintech firms, this means an AI financial adviser cannot simply be treated as a clever product feature. If it recommends products, influences lending outcomes, handles personal data, or nudges users toward financial decisions, firms will need clear governance and accountability measures.
AI may make financial guidance more accessible, but access alone is not enough. The next phase will depend on whether consumers understand when they are getting education, advice, marketing, or an automated decision.
In finance, the most important question may not be whether AI can give an answer, but who stands behind that answer when money is at risk.
