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How AI will change the ways financial advisers manage your money

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How AI will change the ways financial advisers manage your money

Innovation in financial advice is sometimes met with this feeling of existential anxiety from financial advisers who worry that new technology will negatively affect their jobs — or at the very least, reduce their value. We’ve experienced this hype cycle repeatedly in financial advice, as fledgling technologies tend to create anxiety for advisers by automating or modifying legacy processes and services they historically managed.

While the concerns around job security are understandable, advisers can’t let that unease cloud the good that technology has brought to the advice industry — especially the ways it’s enhanced how advisers serve their clients. Technology has helped lower advisers’ costs and overhead by delivering efficiencies, including streamlining client onboarding and portfolio construction. And it has fundamentally improved their ability to deliver a more personalized experience for clients — cementing the durable value of coaching and guidance from human advisers. 

Fast forward to today, and the technology driving headlines is generative AI. This rapidly evolving technology has the promise and potential to change the ways we interact with nearly everything, including financial advice. As GenAI becomes prevalent in technology solutions across the industry, advisers would be well-served to consider its meaningful benefits and the accompanying risks, instead of viewing it as a fad or threat.  

Evaluating GenAI’s potential for advisers

There are many ways GenAI can provide value, but for advisers, most notable are the ways in which the technology can help streamline and augment administrative tasks. Here are three time-scaling benefits GenAI can provide advisers so they can prioritize more valuable tasks to help their clients reach their goals:

1. Content generation: GenAI can lend a hand with content generation for the routine communications that advisers often spend their time agonizing over — helping deliver personalized communications like standard client check-ins, meeting reminders and market updates.

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2. Knowledge management: Another of GenAI’s core use cases for advisers is in synthesizing and distilling a lot of information quickly. For example, GenAI can summarize comparisons between products, helping advisers make educated decisions more quickly for their clients. And rather than spending hours parsing through projections, lengthy annual reports and commentary to understand the latest market conditions or outlook, advisers can use GenAI to immediately summarize key takeaways and translate those insights into value for clients. GenAI can even help to distill prior client correspondence into more easily digestible notes and prompts as advisers prepare for upcoming meetings.   

3. Code generation: Just as GenAI can help develop and draft routine content, it can also generate web-page coding, helping advisers upload content on their websites for clients more quickly. And for larger advisory firms, GenAI-assisted code generation can help advisers and their software developers expedite custom technology solutions that assist with client onboarding and back-office tasks like data analysis, trading and operations. It can also support their ability to more seamlessly integrate internal systems for CRM, trading and portfolio management. 

Evolving technology has its risks

GenAI carries several risks if left unchecked, further reinforcing the importance of having a human adviser in the loop. While the time-scaling benefits of GenAI are attractive, advisers must have a framework in place to address risks, both to protect their practice and to safeguard private client information. 

One risk, for example, is jumping into a GenAI-focused partnership without conducting sufficient due diligence. We’ve witnessed explosive growth in GenAI technology, and new tools and platforms are popping up every day that may, at face value, seem like a good fit. It’s critical that advisers develop guidelines to vet potential partners and their technology, focusing on expertise, experience, client set and information-security measures. 

Another important risk advisers will need to guard against is any lack of awareness around the parameters of the GenAI platform they’re operating in. GenAI technology can be private, but some platforms are open to the public — like ChatGPT, for example — and advisers should consider oversight measures to ensure no confidential, proprietary or client information is shared. 

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Lastly, advisers should develop processes to spot risks related to hallucinations and biases. Hallucinations can occur when AI is prompted to provide a response to a question it hasn’t been trained to answer. Instead of not answering the question, AI can hallucinate and provide an incorrect response that sounds convincing. Additionally, GenAI tools can also suffer from racial and gender biases. For example, GenAI could recommend a lower investment-risk tolerance for women regardless of their actual appetite for risk. It is crucial that advisers understand the source data behind the AI they’re using, and have plans in place to check against unexpected hallucinations and biases that may perpetuate prejudices or stereotypes.  

With GenAI, advisers can more effectively manage their time — their most scarce and valuable asset — and devote more energy to creating personalized experiences and building deeper relationships with clients. Vanguard research shows that relationship-oriented services are a key differentiator in delivering value for clients, and that value increases as advisers establish emotional trust. Advisers who welcome technology and incorporate it judiciously have the potential to deliver better results for clients. 

Lauren Wilkinson is a principal at Vanguard and chief information officer for the firm’s Financial Advisor Services (FAS) division.

More: Saving too little? Spending too much? How to know if your money worries are rational (or not).

Also read: A rude awakening: Lack of financial literacy hurts the young. What about older people?

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State treasurers push CFPB on third-party financial data access rule

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State treasurers push CFPB on third-party financial data access rule

A dozen state financial officers are writing to the Consumer Financial Protection Bureau (CFPB) to uphold consumers’ right to share financial data with authorized third parties as the agency weighs a rule that could restrict their ability to do so, according to a letter exclusively reviewed by FOX Business.

The CFPB is considering revising a regulation under section 1033 of the Dodd-Frank Act, which would revise the definition of a “representative” who makes a request on behalf of the consumer, as well as how to assess fees to cover costs incurred by a covered person responding to a customer request.

Twelve state financial officers — including nine treasurers, two auditors and one controller — wrote in favor of the rule recognizing consumer-authorized third parties as “representatives” while preserving existing authorization and conduct requirements.

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They wrote that Section 1033 gives consumers a right to access their financial information upon request and that the rule includes agents, trustees or representatives acting on their behalf, including those who aren’t fiduciaries, upon the consumer’s authorization, which is the “touchstone” of the process that needs to be preserved.

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A dozen state financial officers are arguing for the CFPB to preserve the ability of consumers to authorize non-fiduciary representatives to access their data. (Anna Moneymaker/Getty Images)

“Preserving this interpretation promotes competition and innovation (including for real-time payments, budgeting tools, alternative credit assessment, AI, and crypto) and it reduces the risks of debanking and market concentration,” the financial officers wrote.

“In contrast, narrowing ‘representative’ would harm consumers by reducing choice and entrenching incumbents — outcomes counter to Section 1033’s competitive purpose,” they explained.

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The group of state financial officers wrote that the CFPB should affirm the text of the rule by clarifying that a consumer-authorized third-party qualifies as a representative acting on their behalf.

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Consumer Financial Protection Bureau

The CFPB’s proposed rule is revising regulations under the Dodd-Frank Act. (Samuel Corum/Bloomberg via Getty Images)

They also wrote the definition of “representative” shouldn’t be limited to fiduciary relationships as it’s not required by the text and would “unduly restrict consumer choice.”

“Consumers should be able to exercise their Section 1033 rights directly or through an authorized representative of their choosing. A text-faithful interpretation of ‘representative’ sustains competition and innovation and reduces risks of debanking and market concentration,” the state financial officers explained.

State financial officers who signed onto the letter include Kansas Treasurer Steven Johnson, Kentucky Treasurer Mark Metcalf, Mississippi Treasurer David McRae, Nebraska Auditor Mike Foley, Nebraska Treasurer Tom Briese, Nevada Controller Andy Matthews, North Dakota Treasurer Thomas Beadle, Ohio Treasurer Robert Sprague, South Carolina Treasurer Curtis Loftis, Utah Auditor Tina Cannon, Utah Treasurer Marlo Oaks and Wyoming Treasurer Curt Meier.

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ANTI-WOKE GROUPS IN US AND FRANCE JOIN FORCES TO COMBAT DEBANKING AND CORPORATE IDEOLOGICAL POLICIES

Wall Street American Flags

The state financial officers want to ensure consumers can authorize a third party to look at their financial data. (Yuki Iwamura/AFP via Getty Images)

The public comment period for the CFPB’s rule closed on Tuesday night and the rule attracted nearly 14,000 comments.

Sen. Cynthia Lummis, R-Wyo., sent a letter to the CFPB in support of open banking policies as the agency considers the rule, while consumer groups have also weighed in.

Major financial institutions are attempting to consolidate their power and maintain monopolistic control over consumer data,” Will Hild, executive director of Consumers’ Research, said in a statement. 

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“If these major banks are allowed to continue to control access to consumer data, they will have even greater leverage to punish Americans for their beliefs and to coerce compliance with their radical left-wing ideology.”

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Equipment finance leaders urge collaboration as uncertainty persists

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Equipment finance leaders urge collaboration as uncertainty persists

Facing excess inventory, elevated equipment prices and tightening credit, leaders in the equipment finance industry say collaboration among dealers, lenders and OEMs is key to finding stability amid economic uncertainty. 

As dealer and finance operations expand nationwide, lenders must collaborate with trusted dealers to inspect and deliver quality equipment that meets customer needs, Jody Ray, vice president and relationship manager at BMO Bank North America, said during Equipment Finance News’ webinar today. 

“That collaboration piece is crucial and critical,” he said. “In my world, we trust our dealers and we depend on them, almost solely, to be the eyes and ears, sometimes, of the lending team and the asset management team.”

Watch the webinar here

Dealers are the critical link between lenders and customers, providing visibility into real-world equipment conditions that influence credit and valuation decisions that would otherwise need third-party assistance for equipment monitoring, John Gougeon, president and chief executive of UniFi Equipment Finance, said during EFN’s High-priced used equipment inventory: The no-man’s land of equipment finance webinar. 

“Nothing beats having eyes on the equipment,” he said. “We have some internal hurdles that may be deal size, cost of equipment or age of equipment that require us to seek third-party support or an inspection.” 

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Lenders, in turn, are rethinking financing structures, using flexible floorplans and tailored repayment schedules to protect dealer liquidity and accelerate inventory turnover. 

“At the same time, we also have to look at the retail side of that when they sell the piece of equipment, and we’re looking at the customer of the dealership’s ability to purchase the piece of equipment,” he said. “As the lender, we’ll need to make sure that we’re on top of every piece of the transaction, so that we can make that a smooth and seamless transaction for our customer, the dealer and their customer purchasing the equipment.” 

Excess inventory collaborations 

Collaboration can also enable dealers, lenders and OEMs to address excess inventory, especially with strategic programs, Kevin Pate, director of fleet and heavy-duty equipment at Shoppa’s Material Handling, said during the webinar. More than 70% of ag equipment dealers and nearly 30% of truck dealers reported excess late-model inventory in the second quarter, according to IronAdvisor Insights. 

The collaboration “would be something in the vein of a shared remarketing program, whether it’s a joint site where dealers that work with that vendor are able to load their assets, maybe not just what’s with that company,” he said. “You’d be looking for OEM-supported programs, similar to a certified pre-owned program, where they come with finance options, extended warranty options and things like that.” 

Check out our exclusive industry data here. 

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The impact of fintech on lending

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Technology – especially AI – is disrupting the world of finance (see overviews in Duffie et al. 2022, Foucault et al. 2025, and Vives 2019). Lending is no exception: machine learning and large datasets are successfully used for credit assessment. Fintech has enabled efficiency gains, such as improved loan screening, monitoring, and processing, and has fostered financial inclusion among underserved populations and in less developed countries.

At the same time, it raises concerns about financial stability, privacy, and discrimination. Digital technologies enable improved customer segmentation, which not only facilitates personalised services but also allows for finer price discrimination. The empirical evidence on fintech’s impact is mixed regarding loan pricing, substitutability or complementarity of fintech and bank credit, loan default, and data sharing.

Empirical studies differ on whether default or delinquency rates are higher for fintech-originated loans than for bank-originated loans. While some report higher default rates (Di Maggio and Yao 2021), others report lower (Fuster et al. 2019), and still others find no significant difference (Buchak et al. 2018). Similarly, open banking initiatives increase the likelihood that SMEs form new lending relationships with non-bank lenders and reduce their interest payments. Still, they do not necessarily improve financial inclusion (Babina et al. 2024). However, in Germany (Nam 2023) and India (Alok et al. 2024), open banking has improved credit access on both extensive and intensive margins without increasing risk. In the US, California’s Consumer Privacy Act strengthened fintechs’ screening capabilities relative to banks and enabled more personalised mortgage pricing, ultimately reducing loan rates and improving financial inclusion (Doerr et al. 2023).

An analytical framework

In Vives and Ye (2025a, 2025b), my co-author and I present an analytical framework that incorporates key differences between fintech firms and incumbent banks, explains the mixed empirical findings in the literature, and delivers a welfare analysis. The framework introduces a taxonomy of how fintech affects frictions in the lending market. We find that fintech’s impact on competition and welfare hinges on its effect on the differentiation between financial intermediaries and the efficiency gap between them. Primary factors influencing market performance include the level of bank concentration, the intensity of competition among fintechs, the potential for price discrimination, the size of the unbanked population, and the convenience offered by fintechs.

We consider a spatial oligopolistic competition model in which lenders (banks and fintechs) compete to provide loans to entrepreneurs. The framework captures key differences between fintechs and banks. For example, banks have more financial data and soft information (with relationship lending) than fintechs, but the latter have better information-processing technology and conversion of soft into hard information (with the digital footprint) and lower distance friction with borrowers. This distance can be physical or in terms of expertise; greater distance between a lender and borrower increases the cost of monitoring (or screening).

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Furthermore, banks have lower funding costs, and fintechs have higher convenience benefits. Fintechs also have greater price flexibility for technological and regulatory reasons, which gives them a competitive advantage. In the extreme, banks are differentiated by expertise (location), but fintechs are not; fintechs can price discriminate, whereas banks cannot. In our model, endogenous entrepreneur participation occurs at each location, and entrepreneurial projects require monitoring (screening) to enhance project returns (Vives and Ye 2025b) or to mitigate a moral hazard problem faced by entrepreneurs (Vives and Ye 2025a).

The type of fintech advancement matters

A key insight from Vives and Ye (2025a) is that we should distinguish between general advances in fintech that reduce the distance between lenders and borrowers and those that do not. General improvements in information collection and processing, such as enhanced data storage, computing power, or desktop software, do not necessarily reduce distance friction. Technologies that lower the effective distance between lenders and borrowers include improved internet connectivity, video conferencing, remote learning tools, AI, and advanced search engines, which enable lenders to expand their domain expertise and serve distant borrowers more effectively. Big data, together with machine learning, can improve both types of capabilities.

If fintech does reduce the distance friction, lenders’ differentiation will decrease and competition intensity will increase, decreasing their profits and monitoring incentives. The effect is more pronounced when the entrepreneurs’ moral hazard problem is more severe. The impact on entrepreneurs’ investment and total welfare is hump-shaped. Those effects are not present when fintech progress does not affect the distance between lenders and borrowers.

Bank and fintech competition

In Vives and Ye (2025b), we assume that banks are differentiated by expertise (located in a circle) but fintechs are not (located in the virtual middle). We find that (1) fintech entry can be blockaded, remain as a potential threat, or materialise depending on fintechs’ monitoring efficiency, (2) fintech lending can substitute or complement bank lending depending on whether pre-entry banks competed or not, and (3) fintech entry and loan volume is higher when bank concentration is higher.

Furthermore, if banks cannot price discriminate, a fintech with no advantage in terms of monitoring efficiency or funding costs can enter the lending market. If banks and fintechs have similar funding costs, for entrepreneurs with similar characteristics, banks’ loan rates and monitoring are higher than those of fintechs (and fintech borrowers are more likely to default). The latter result will change if fintechs have significantly higher funding costs than banks. If fintechs have a significant advantage in convenience, they will likely charge higher prices, while banks will conduct more thorough monitoring. Therefore, differences in funding costs, convenience benefits, and abilities to price discriminate may explain the variety of empirical results on loan defaults by banks and fintechs.

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Fintech entry may decrease entrepreneurs’ investment if competition within fintechs is not sufficiently intense. An intermediate level of competition intensity among fintechs is needed to ensure a welfare increase following fintech entry, to balance the incentives of borrowers and lenders.

However, if banks can also discriminate, fintechs need an advantage in monitoring (or funding costs, although this is less probable) to penetrate the market. Finally, the threat or actual entry of fintechs can induce bank exit or restructuring, potentially reducing the intensity of lending competition and investment, but generating a welfare-improving option value effect.

Policy implications

We can derive some policy implications from the analysis. We know that price discrimination is a competitive weapon, but it will not necessarily be welfare optimal unless it extends the market. This is so also in our modelling. Socially optimal loan rates strike a balance between the incentives of entrepreneurs and intermediaries to exert effort, thereby mitigating moral hazard, encouraging entrepreneur participation in the market, and enhancing lenders’ monitoring or screening effort.

However, this balance typically cannot be obtained from lender competition with location-based discrimination. For example, with endogenous entrepreneur participation at any location, a bank should charge (from a welfare perspective) higher rates for distant locations (since monitoring is more costly and distant locations generate less surplus). In contrast, price-discriminating banks will do the opposite in equilibrium to meet the competition. However, allowing banks to discriminate when fintechs price discriminate improves welfare when there is little inter-fintech competition.

Regarding data sharing, we find that a policy (e.g. open banking) that benefits fintechs must be complemented by an appropriate degree of inter-fintech competition. Otherwise, the policy may backfire, and a leading fintech may gain a monopoly position in a market segment. Differences in the degree of competition may explain the differences in the empirical results in the impact of open banking.

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In summary, levelling the playing field (in terms of lenders’ ability to price discriminate and access to information) is a good policy aimed at achieving a degree of competition that induces a division of rents, thereby balancing the incentives of different market participants to maximise welfare. This degree of competition must be sufficient to prevent monopoly positions in market segments, while also ensuring that both lenders and borrowers have enough stake in the game.

References

Alok, S, P Ghosh, N Kulkarni, and M Puri (2024), “Open banking and digital payments: Implications for credit access”, working paper.

Babina, T, S A Bahaj, G Buchak, F De Marco, A K Foulis, W Gornall, F Mazzola, and T Yu (2024), “Customer data access and fintech entry: Early evidence from open banking”, working paper.

Buchak, G, G Matvos, T Piskorski, and A Seru (2018), “Fintech, regulatory arbitrage, and the rise of shadow banks”, Journal of Financial Economics 130: 453–83.

Di Maggio, M, and V Yao (2021), “FinTech borrowers: Lax screening or cream skimming?”, The Review of Financial Studies 34: 4565–618.

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Doerr, S, L Gambacorta, L Guiso, and M Sanchez del Villar (2023), “Privacy regulation and fintech lending”, working paper.

Duffie, D, T Foucault, L Veldkamp, and X Vives (2022), Technology and finance, The Future of Banking 4, CEPR Press.

Foucault, T, L Gambacorta, W Jiang and X Vives (2025), Artificial intelligence in finance, The Future of Banking 7, CEPR Press.

Fuster, A, M Plosser, P Schnabl, and J Vickery (2019), “The role of technology in mortgage lending”, The Review of Financial Studies 32: 1854–99.

Nam, R J (2023), “Open Banking and Customer Data Sharing: Implications for Fintech Borrowers”, SAFE Working Paper No. 364.

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Vives, X (2019), “Digital disruption in banking”, Annual Review of Financial Economics 11: 243–72.

Vives, X, and Z Ye (2025a), “Information technology and lender competition”, Journal of Financial Economics 163: 103957.

Vives, X, and Z Ye (2025b), “Fintech entry, lending market competition, and welfare”, Journal of Financial Economics 168: 104040.

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