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AI in banking is used for more than chatbots: banks and financial-technology companies apply it to customer service, identity checks, fraud detection, compliance, credit analysis and operational forecasting. The 23 examples described by Built In in an article updated August 12, 2026, range from customer-facing assistants to internal tools that flag suspicious activity or analyze financial data. They illustrate different uses, not independently verified product performance or a current feature comparison.
What are the main ways banks use AI?
Banking AI appears across three broad areas: services customers use directly, systems that help staff assess risk and meet compliance obligations, and tools for financial analysis and operations. The word “AI” does not identify one particular technology. A conversational assistant, a machine-learning fraud model and an analytics tool can all be described as AI while doing very different work.
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The examples below are those reported by Built In; company capabilities are attributed to that article rather than presented as independently tested or confirmed current offerings. The Office of the Comptroller of the Currency (OCC) separately identifies fraud prevention, cybersecurity, Bank Secrecy Act and anti-money-laundering monitoring, and customer due diligence as banking AI use cases. Its Spring 2025 risk perspective also warns that AI can bring model, cyber and compliance risks, whether developed in-house or supplied by a third party.
How is AI used in customer service and digital banking?
Customer-facing systems can answer routine questions, guide customers through common tasks or support a bank’s digital channels. Their usefulness depends not only on what they can answer, but also on whether they handle mistakes and route a customer to effective human help.
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Ally, Capital One and Kasisto: digital assistants
Built In describes Ally’s machine-learning chatbot as helping with questions, transfers, payments and summaries. It also names Capital One’s Eno assistant. These examples show the everyday service role often associated with banking AI: assisting with common account interactions rather than replacing every kind of customer support.
Kasisto’s KAI platform is described as a way for banks to build their own digital assistants. That is a different role from a bank’s branded assistant: a platform provider supplies technology that a financial institution can use to create a customer-facing experience.
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The article reports that HSBC used Pepper as a branch greeter, while HooYu is associated with remote identity onboarding. Discover is described as using Google Cloud contact-center tools. These examples place AI-enabled or automated tools at different points in a customer relationship: an in-person branch interaction, the identity-check process for a remote customer, and contact-center operations.
SoFi Coach and Bankrate: finance prompts and editorial workflows
Built In describes SoFi Coach as offering budgeting help and other personal-finance prompts. It also names Bankrate’s use of AI in editorial and product workflows. The latter is an example of AI supporting work around financial information and products, not itself a bank account assistant.
These uses should not be treated as interchangeable. A budgeting prompt, an identity check and a contact-center tool may rely on different data, integrations and review processes. A customer comparing services should look at the specific task and the consequences of an incorrect answer, rather than infer capabilities from an “AI-powered” label.
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How do banks use AI to detect fraud and manage compliance?
Financial institutions use AI-related systems to help identify suspicious patterns, check identity, monitor transactions and detect cyber threats. These are consequential internal uses: a false alert can create friction for a legitimate customer, while a missed signal can leave a risk unaddressed.
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Built In names Socure for identity and know-your-customer analysis, and Vectra AI for cyber-threat detection. Identity analysis can support customer due diligence, while threat detection is aimed at protecting systems and information. The article presents these as use-case examples, not as evidence of measured accuracy or effectiveness.
Compliance, anti-money-laundering monitoring and transaction fraud
The article associates FIS and C3 AI with compliance and credit-analysis workflows, and Symphony AyasdiAI with anti-money-laundering alerts and transaction monitoring. It also describes DataVisor as working on transaction and application fraud, Airwallex on fraud protection, and Feedzai on payment-risk monitoring.
These systems can help staff prioritize cases, but an alert is not the same as a confirmed violation. Banks still need processes to investigate, review and act on alerts, and to manage the risk of errors. The OCC’s Spring 2025 perspective is relevant here: AI used for fraud prevention, cybersecurity, suspicious-activity monitoring or customer due diligence can itself create model, cybersecurity and compliance risks.
How is AI used in lending, credit risk and financial analysis?
AI-related tools can support analysis used in lending and other financial decisions, as well as forecasting and investment research. When a system affects a consumer’s credit decision, the stakes are different from those of an internal research aid: the institution must be able to give the customer the required reasons for an adverse decision.
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Credit and lending examples
Built In names ZestFinance for credit modeling, and describes Upstart as offering personal, auto and home-equity lending products. It also lists Gynger for technology financing and EDGE for credit-risk analysis. Feedzai appears in the article’s credit and lending use cases as well as its payment-risk examples.
These descriptions do not establish that a particular model expands access to credit, produces fairer outcomes or makes better decisions. Such claims require evidence about the model, the population and the outcomes measured. A vendor’s stated purpose or a list of use cases is not a substitute for an independent evaluation.
Forecasting, market scenarios and investment research
The article describes PNC’s PINACLE as supporting cash forecasting and Kensho as a financial-data analysis example. It names Kepler for investment-banking research and Simudyne for market-scenario simulation. These examples concern analysis and planning rather than direct customer support. Their value depends on the quality and relevance of the data, how results are interpreted, and whether people understand a model’s limitations.
What should customers and bank leaders examine when evaluating these examples?
The right questions depend on the system’s job. A customer-service bot and an underwriting model do not carry the same consequences, and neither should be evaluated only by its label or advertised capability.
- What task does it perform? Distinguish a routine balance inquiry from identity verification, suspicious-activity monitoring or credit analysis.
- Who uses the output? Identify whether the tool speaks directly to customers, assists employees, or feeds into an institutional decision.
- What happens when it is wrong? Consider the customer impact of an incorrect answer, a false fraud alert, an identity-check failure or a flawed analysis.
- What data and integrations does it depend on? The examples involve different workflows, so ask what information is used and how the tool fits into the bank’s existing process.
- How are outputs checked and governed? Ask how staff review important results, how performance is monitored and how model, cybersecurity and compliance risks are managed.
- Can a customer reach a person or challenge an outcome? For customer support, a reliable escalation route matters. For credit decisions, customers need specific reasons when an application is denied or otherwise receives an adverse action.
What are the main risks of AI in banking?
The risks vary by use. A chatbot can misunderstand a customer; a model can produce an inaccurate or poorly explained result; a vendor-supplied system can introduce security or compliance concerns. Bank leaders need controls suited to the system’s role, not a blanket assumption that automation is either harmless or inherently unreliable.
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Chatbots can fail on complex problems
The Consumer Financial Protection Bureau (CFPB) says banks use chatbots for tasks such as balance and transaction lookups and bill payment, but warns that bots may fail on complex problems, provide inaccurate information or leave customers without a route to effective human help. The CFPB estimated that approximately 37% of the U.S. population had interacted with a bank chatbot in 2022; this is a U.S. estimate about interaction in that year, reported by the CFPB in 2023, not a current adoption rate.
For customers, that context makes the escalation path part of the service itself. A bot may be convenient for a routine request, but a bank should not leave customers stuck when an issue is urgent, complicated or not resolved by the automated exchange.
Credit decisions still require specific explanations
Using a complex algorithm does not remove a creditor’s obligation to explain an adverse credit action. In Consumer Financial Protection Circular 2022-03, the CFPB states: “Creditors who use complex algorithms, including artificial intelligence or machine learning, in any aspect of their credit decisions must still provide a notice that discloses the specific principal reasons for taking an adverse action.” The explanation must identify the specific principal reasons; algorithmic opacity is not an excuse for a vague notice.
Models and suppliers create governance obligations
The OCC’s Spring 2025 risk perspective cautions that AI systems may introduce model, cyber and compliance risks, including when a bank obtains a system from a third party rather than building it internally. The practical implication is that a bank must understand and oversee the system in its actual workflow. A vendor description alone cannot establish that a tool is appropriate, secure or effective for a particular bank or customer population.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do these 23 examples show—and what do they not show?
Taken together, the examples show that AI in banking is not one product category. The list spans customer-facing assistants, identity and fraud tools, compliance monitoring, lending and credit analysis, forecasting, contact centers and research. Some entries describe products or platforms; others describe a bank’s reported use of a tool or a company’s stated use case.
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The list is a map of applications, not a current buying guide, a ranking or proof of performance. Built In’s August 12, 2026 update is the source for the named examples; the CFPB and OCC materials provide independent context about chatbot limitations, credit explanations and institutional risk. Product capabilities and relationships can change, and the examples do not establish that each named system is currently offered or deployed in the same way.
Frequently Asked Questions
Does a bank have to tell me if it used AI to deny my loan?
A general requirement to label a decision as AI-made is not established by the CFPB guidance discussed here. The CFPB does say that a creditor using AI in a credit decision must give the specific principal reasons for an adverse action.
Can I opt out of automated decisions at my bank?
The examples and regulatory guidance discussed here do not establish a universal opt-out right or one policy that applies to every bank and use. Ask your bank how a specific decision is made and what review or appeal process is available.
Are the banking AI examples listed here all generative AI?
No such conclusion follows from the examples: they include machine-learning chatbots, identity and risk analysis, forecasting and other tools. A company’s use of the broad term “AI” does not by itself show that a system generates text or uses a generative model.
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