TL;DR:

· The first thing AI Agents disrupt is not financial products themselves, but the "consumer inertia" that the financial industry has long relied on. Bernstein insurance renewals, low-interest deposits, brokerage idle cash, and primary credit card status could all come under pressure from automated price comparison and capital optimization.

· Bernstein's real bottleneck is not technology, but permissions, trust, and liability. Bernstein banks, brokerages, and insurance companies still control account and data access, and consumers are more willing to let AI "assist decisions" rather than operate fully autonomously.

· The Bernstein impact will not occur evenly. Bernstein businesses that rely on user stickiness and switching costs face greater pressure, but payment networks like Visa and Mastercard may benefit from rising demand for identity verification, tokenization, risk control, and dispute resolution.

· The most important thing to watch next for Bernstein is whether Agents truly gain "execution rights." Bernstein factors include the degree of consumer authorization, whether financial institutions open data interfaces, and whether operating metrics such as insurance renewals, deposit stickiness, and cash sweep begin to change.

Editor's note: Consumer-grade AI Agents are moving from "helping users answer questions" to "completing tasks for users." Bernstein noted in its latest report that after launch, Muse quickly rose to the top of the U.S. App Store, with downloads reaching about 2.8 million, and users have already begun using it to book services, cancel subscriptions, compare insurance, fill out forms, and contact customer service. At the same time, a group of financial stocks that rely on consumer stickiness and operational friction saw significant declines.

The market's most intuitive concern is whether AI Agents will directly bypass banks, insurance companies, brokerages, and credit cards. But what Bernstein is really discussing is not simply "technological replacement," but a more fundamental question: if AI can continuously compare prices, switch products, and move funds for consumers, will the profits the financial industry has built on consumer inertia, information friction, and switching costs begin to be compressed?

The report argues that insurance renewals, bank deposits, brokerage idle cash, and primary credit card status could all be affected. But this does not mean Agents will soon be able to fully take over financial decisions. Financial institutions still control accounts, data, and trading permissions, consumers may not be willing to hand their money entirely to AI, and liability and regulatory frameworks have not kept up with technological development.

Therefore, the impact of this wave of Agents on the financial industry may not be a comprehensive disruption, but a redistribution of value: the more a segment relies on consumers "not acting" to make money, the greater the potential pressure; the more it can provide infrastructure for identity verification, payment security, risk control, and data interfaces, the more important it may instead become.

The following is a compilation of the original text:

After Muse launched, financial markets quickly began trading a new question: if consumers have an AI Agent that can continuously compare prices, switch products, cancel subscriptions, and even move funds on their behalf, what will traditional financial institutions rely on to retain customers?

Bernstein statistics show that since Muse was launched, insurance, large banks and credit card issuers, regional banks, brokerages, and mortgage lenders have all experienced declines to varying degrees, with mortgage-related companies falling the most; the payments sector has been relatively limited in the impact it has suffered.

The logic behind this market reaction is not complicated.

A considerable portion of the financial industry's profits does not come because consumers make the wrong decision every time, but because consumers simply will not continuously optimize their choices.

And what AI Agents are most likely to change is precisely this.

What AI Agents touch first is the "consumer inertia" of the financial industry

Insurance is the most typical example.

Many users will directly renew their policies after they expire, rather than re-comparing prices, coverage, and products from different companies every year. As long as this renewal inertia exists, insurance companies have a certain degree of pricing room.

Banks and brokerages also have similar logic.

Consumers do not compare deposit rates at different banks every day, nor do they continuously deal with idle cash in brokerage accounts. As a result, low-yield deposits can remain in banks for a long time, and brokerages can also earn revenue from businesses such as cash sweep.

The credit card industry relies on another habit.

Consumers often use the same credit card for a long time, and this position of "using a certain card by default first" is usually called top-of-wallet. Through points, cashback, and long-term usage habits, banks turn a card into the default choice when consumers pay.

AI Agents may weaken these advantages at the same time.

If an Agent can automatically compare insurance quotes, find higher deposit yields in real time, move idle cash in brokerage accounts into higher-yield products, or compare the cashback, points, and rates of different credit cards before every purchase, then the "search-compare-switch" process that consumers previously had to complete on their own will be greatly compressed.

Bernstein therefore believes that automated cash management may make deposit migration easier, thereby pushing up bank funding costs and compressing net interest margins; brokerages' cash sweep revenue may come under pressure; and credit card issuers may lose part of the advantages brought by top-of-wallet and long-term usage habits.

What is really changing here may not necessarily be the financial products themselves, but rather the declining cost for consumers to optimize financial products.

In the past, consumers had to invest time and effort to switch insurance every year, compare interest rates across five banks, and research which credit card offers the highest cashback; if these tasks can be continuously handled by an Agent in the background, then the economic value of "users being too lazy to switch" itself may decline.

Stock price performance of different financial sectors after the launch of Muse.

But just because an Agent can do it doesn't mean it has the right to do it

If we continue to extrapolate along this logic, it is easy to reach an extreme conclusion: AI Agents will ultimately bypass banks, insurance companies, and brokerages to directly complete all financial decisions for consumers.

Bernstein believes things are not that simple.

Third-party Agents face a fundamental contradiction: without cooperation from merchants and financial institutions, it is difficult for them to truly complete complex transactions; but if open access means losing customer relationships, transaction entry points, or part of their revenue, financial institutions have no reason to cooperate unconditionally.

Banks, brokerages, and insurance companies still control several key points: account login, identity verification, data permissions, formal quotes, and whether users are eligible for a certain product.

This is also why some platforms have already begun restricting Agent access.

Bernstein mentioned that Amazon chose to block Muse, with disputes between the two sides over Agent identity recognition and the use of user login credentials; the insurance comparison platform Insurify also restricted Muse from scraping quotes, on the grounds that insurance is not simply a price comparison—beyond premiums, there is also a large amount of information such as coverage limits, deductibles, discount conditions, eligibility, and regulatory disclosures. If an Agent ultimately presents only a "lowest price," what consumers get may not be a truly comparable product.

This means that the core bottleneck for financial Agents is shifting from "whether the model can do it" to "who allows it to do it."

Data is one of the most important thresholds. Financial institutions control account authentication, account data, and data-sharing permissions, so Bernstein raised a possibility opposite to "AI platforms charging banks": will banks in the future instead charge Agents data access fees?

The report mentioned that the earlier U.S. open banking rules around Section 1033 originally sought to require banks to provide account data for free to consumers and their authorized third parties through secure APIs; the relevant rules were subsequently blocked, and JPMorgan has since begun charging data aggregators for customer data access. Bernstein believes that in the future, around Agent data access, there are likely to be more blockings, paid agreements, and situations in which financial institutions actively control what information is shown to Agents.

Even if the technology and data interfaces are already in place, consumers themselves are another constraint.

A 2026 TD Bank survey of more than 2,500 U.S. consumers showed that 55% already use AI to help manage their personal finances, but only 18% are willing to let AI make important financial decisions independently. Consumers clearly prefer a model in which "AI provides advice and humans retain the final say."

Other surveys show similar results. A survey by ACI Worldwide and YouGov showed that only 7% of U.S. and U.K. consumers are willing to let an AI assistant make purchases directly without approval; an Accenture survey showed that 32% are willing to let an Agent make purchasing decisions within set parameters, but when it comes to the actual payment step, only 12% are willing to let the Agent decide completely autonomously.

Across different scenarios, consumers' acceptance of "AI-assisted" versus "fully autonomous AI" varies, and financial investment decisions are at the lowest level of acceptance for autonomy.

Therefore, the earliest large-scale form of financial Agents may not be full autonomy, but rather: AI completes search, comparison, screening, and execution preparation, while humans retain confirmation rights at key financial decisions and payment steps.

What is even more troublesome is that once Agents truly begin executing transactions, liability issues will also arise.

Can a loan application submitted by an Agent represent valid authorization from the consumer? If an Agent uses outdated data and selects a product for a user that is technically eligible but not suitable for them, should the loss be borne by the model company, the financial institution, or the consumer? If an Agent begins proactively comparing and recommending insurance or investment products, under what circumstances would that constitute regulated financial advice?

Bernstein summarized these unresolved issues into multiple aspects, including authorization, identity verification, financial advice, licensing, data access, and attribution of liability.

Therefore, the biggest difference between financial services and ordinary e-commerce may be this: having an Agent order a meal for a user is easy, but having it allocate assets, apply for loans, or purchase insurance on the user's behalf requires solving an entire set of permission, trust, and liability mechanisms.

The impact will not occur evenly: Visa and Mastercard may actually benefit

For this very reason, the impact of AI Agents on the financial industry will not be evenly distributed. If a company primarily profits from consumer inertia, product switching costs, or long-term usage habits, the emergence of Agents may erode some of its advantages.

But for infrastructure companies responsible for solving identity verification, payment security, and transaction liability issues, the logic may be completely reversed.

Visa and Mastercard are among Bernstein's most favored examples.

Intuitively, if AI Agents can make payments autonomously, card networks might seem likely to be bypassed. But Bernstein believes that Agentic Commerce is actually a positive factor for Visa and Mastercard.

The reason is that when payments shift from "humans operating personally" to "machines operating on behalf of humans," the entire payment system needs to solve even more trust issues: Who is initiating the transaction? Who does it represent? Is it authorized? How much is it allowed to pay? And once fraud or disputes occur, how are they handled?

These all require more complex identity verification, risk control, Tokenization, and dispute resolution mechanisms.

Tokenization here refers to using specially generated digital credentials to replace real bank card numbers to complete payments. In Agent scenarios, such Tokens can also carry more information about the transaction principal, usage context, and authorization scope.

Visa has launched Visa Intelligent Commerce, and Mastercard has also launched Mastercard Agent Pay. Bernstein believes that as Agent transactions increase, such Agentic Tokens and the identity and risk management capabilities behind them may become more important.

This also explains why the impact on issuing banks and card networks may not be the same.

Issuing banks' existing top-of-wallet advantage may be weakened because Agents re-compare rewards and rates every time; but the transaction networks, identity verification, risk control, and dispute resolution provided by Visa and Mastercard may instead become more important as machine transactions increase.

The same logic also applies to PSPs such as Adyen and Stripe, namely Payment Service Providers.

The more Agents there are, the more complex the platforms, protocols, and payment interfaces merchants need to be compatible with. Bernstein believes that modern PSPs can help merchants unify the handling of different Agent channels, which may instead create new room for differentiation. Adyen has already launched Adyen Agentic, allowing businesses to avoid rebuilding a complete commerce system for every AI platform.

PayPal's position is more subtle.

If Agents already handle payments for users, the value that digital wallets previously provided in reducing Guest Checkout friction may decline; but on the other hand, digital wallets can also expand their role into trust, fraud protection, dispute resolution, offer discovery, and payment method optimization.

Therefore, Agents will not necessarily eliminate payment intermediaries. They are more likely to change the rationale for the existence of different intermediary layers.

What really matters next is: who holds the Agent's "execution authority"

Muse's rapid growth has already proven that consumer-grade Agents can gain a large number of users in a very short time. But for the financial industry, how smart the model itself is may not actually be the most important variable in the next stage.

What truly needs to be observed comes down to three questions.

First, will consumers gradually move from "letting AI give advice" to "letting AI execute directly"?

Second, how will banks, brokerages, and insurance companies handle Agents' requests for data and account access—will they restrict, charge, cooperate, or launch their own Agents?

Third, and most critically, will real operating metrics such as insurance renewal rates, bank deposit stickiness, brokerage cash sweep balances, and credit card top-of-wallet status begin to show sustained changes because of the spread of Agents?

Only when these metrics truly change will the impact of AI Agents on financial business models have moved from market expectations into the operating level.

So the real question raised by this Bernstein report is not "Will AI disrupt the financial industry?" Rather, it is: once consumers have, for the first time, an agent that can work around the clock to find them lower prices, higher yields, and better terms, which profits in the financial industry are essentially built on consumers' past reluctance to act?

At the same time, which companies truly control identity, data, payments, and liability systems will also become increasingly important. This may be what the financial industry truly needs to reprice in the Agent era.

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