Source: https://www.clarista.io/whitepapers/ai-for-regional-banks-phased-guide White Paper · Regional and Community Banking # The Relationship Advantage: How Regional Banks Can Use AI to Out-Serve the National Banks A phased guide for regional and community bank leaders on using AI to know clients better, serve them sooner, stop fraud and run oversight at lower cost, while keeping ownership of their software and data. By Suvrat Bansal, Founder and CEO, Clarista; former Chief Data Officer, UBS Asset Management · October 25, 2026 ## Key Takeaways - AI from your core provider reaches every bank on the platform at the same time. It keeps you level with your peers. It does not set you apart. - Building software no longer needs an IT department. Code that took a team months can now be written with AI in days, then reviewed, secured and put into production in weeks. - Own two things: the software built for your bank, and your data, linked by customer and household. - Regional banks already win on trust. AI lets your bankers act before the client asks, at a business sale, a succession or an inheritance. - Start with one process. Four phases take a bank from governed data to proactive advice, with evidence an examiner can follow at every step. TermWhat it means in this paper Core provider | The company that runs your bank's system of record, such as Fiserv, FIS or Jack Henry | Household view | Every account, loan, entity and person in one family or business relationship, linked together | Signal | A rule written from your data that tells a banker something needs attention, and why | Agentic AI | AI that carries out a series of steps toward a goal, using only the tools and data you allow | ## The Advantage Large Banks Paid Billions For Is Now Within Your Reach JPMorgan Chase expects to spend $19.8 billion on technology in 2026 (AI News). Most regional banks have no team that builds software, so for years new capability has arrived on their core provider's schedule. At a New York Bankers Association conference this fall, where Clarista joined a panel on AI in customer decisions, that was the common answer to every AI question: we are waiting for our platform. That limit is going away. Software that once took a team of developers months to build can now be written with AI in days. Reviewing it, securing it and putting it into production takes weeks, not years. A bank can now own software built for its own clients, policies and data without building an IT department. Once cost and headcount stop deciding what you can build, the question changes. What would your bankers do if they knew each morning which families and businesses needed a call, and why? What if every fraud and AML alert arrived with the customer's full history, so your analysts spent their time deciding instead of gathering? The largest banks spent billions to get here. You can start with one process. ## Two Things Every Bank Must Own Now that a bank can build, the question is what it must own. The answer is two things: the software that runs its own way of working, and its data. ### Owning Your Software: Built, Secured and Maintained for Your Bank Alone Regional banks have relied on their platforms for nearly every new capability: online banking, mobile, payments, and now AI. That made sense when software was expensive to build. It also means every bank on a platform gets the same features, on the same timeline, designed for the average of thousands of institutions. Taking AI from one more platform deepens that dependence. The alternative is software built as a unit for your bank alone. You can own it outright or rent it as a service. Either way it is designed around your processes, secured to your standards and maintained for you, and the code and data stay in your bank's name. If the relationship with the builder ends, the software stays with you. AI from your platformAI built for your bank Who sets the roadmap | The provider, across thousands of clients | You | Whose rules it follows | The provider's defaults | Your policies, credit standards and risk appetite | What data it sees | Data inside that platform | Data across your systems, linked by customer and household | What it does for your position | The same features as every peer | Your way of serving clients, made faster | On exit | Leaves with the provider | Code and data stay with you | ### Owning Your Data: The Asset Behind Personalization, Efficiency and Scale A regional bank's data is its most underused asset. It sits in the core, the loan origination system, treasury management, trust, CRM and document folders, each behind a vendor's terms. Regulators have noticed. In November 2025 the OCC asked community banks about obstacles with their core providers, including their ability to access and use their own data (OCC Bulletin 2025-39). In comment letters, banks described API access sold as an extra charge and waits of 12 to 18 months for basic data extracts (PYMNTS). On September 11, 2026, the Federal Reserve, FDIC and OCC noted that "a significant percentage of the core provider market is represented by just a few large providers," and named "excessive limitations on the ability of unaffiliated service providers to integrate with the core platform" as a contract practice they will weigh (Joint Statement). What owned data makes possible: - Personalization. A banker sees the whole family and business relationship, not one account at a time. - Efficiency. An exam request, a committee pack or an alert investigation draws from one linked source instead of five systems. - Scale. Each new AI use starts from data that is already linked, checked and permissioned, so the second use costs far less than the first. Owning your data does not mean replacing the core. It means a copy of your data, linked by customer and household, checked on every load and governed by role, kept in an environment in your bank's name beside the systems that produce it. Our papers [Your Data Architecture Is About to Break](https://www.clarista.io/whitepapers/how-ai-adoption-is-sending-data-teams-back-to-the-drawing-board-on-enterprise-data-architecture) and [Wealth Data Aggregation](https://www.clarista.io/whitepapers/wealth-data-aggregation) explain what that takes and what it costs. ## What Platform AI Will Improve, and What Stays Yours to Solve Platform AI will make the work inside the platform faster. The processes that set a regional bank apart run across systems and depend on the bank's own policies and judgment, so they stay unsolved unless the bank builds for them. Core providers are moving. In May 2026 Fiserv announced a collaboration with OpenAI covering agents for the workflows that consume the most operational capacity, faster core conversions, banking-specific AI in its products and stronger cybersecurity (Fiserv). That is real progress, and it reaches every Fiserv bank at once. It works on the data and screens inside the platform, and it is built for the average client. The processes below were never solved by the platforms, and adding AI to the platform leaves them where they are. Client relationships ProcessWhere the work happens today One view of a family or household across personal, private, commercial and business banking | Bankers' memory and spreadsheets; the core sees accounts, not families | Business owner moments: a sale, a succession, a liquidity event, a new entity | News, conversations and CRM notes | Pricing and profitability across the full relationship | Spreadsheets, if done at all | Referrals between lines, such as commercial to private banking or trust | Personal networks inside the bank | Cash flow guidance for business clients | Generic treasury reports | Next-generation and elder care transitions | Handled only when the family calls | Why a banker made or declined a recommendation | Lost when the banker leaves | Regulatory and risk oversight ProcessWhere the work happens today Exam preparation and request lists | Weeks of collecting from many systems | Board, ALCO, credit and risk committee packs | Compiled by hand each cycle | Covenant tracking, annual reviews and commercial real estate concentration | The loan system plus spreadsheets | BSA/AML investigations and case narratives | Alerts come from a tool; the investigation crosses systems | Fraud and scams across check, ACH, wire, P2P and card | Separate vendor tools with no view of the customer's usual behavior | Vendor and third-party oversight, including AI vendors | Questionnaires and file folders | Complaints, UDAAP and fair lending reviews | Manual sampling | Mapping regulatory change to policies and procedures | Compliance staff, by hand | ### Choosing What to Take, Buy or Build Take from your core providerBuy off the shelfBuild for your bank (own or rent) Best for | Work inside the core's own screens | Tasks that are the same at every bank | Work that carries your relationships, policies and judgment | Examples | Servicing inside the core, its security services, its chat | Meeting notes, document text capture, sanctions list screening | Household view, proactive signals, exam readiness, alert investigation across systems, committee packs | What you control | Little; the provider's roadmap | Configuration | The design, the rules and the data | ## Where Regional Banks Stand Today Nearly every regional bank now uses AI, but few have scaled it, and very few use it to serve clients personally. Wolf & Co, 2026 AI Adoption and Maturity in Banking · 20 respondents at 17 banks, January to February 2026 - Use is wide, scale is rare. All 17 banks in the Wolf & Co survey use AI, but 30% limit it to ad hoc or vendor-embedded tools, and one bank has a scaled, governed program (Wolf & Co). The sample is small, so read it as direction, not a census. - Fraud gets the budget. Among 257 lending professionals at community banks and credit unions, fraud detection is the top AI investment priority at 66%, while 13% use AI to personalize outreach or offers (MeridianLink). - AI is the top issue. Executives at banks under $10 billion named AI the issue most likely to matter in 2026, and 50% called it the top technology trend (ABA Banking Journal). - The barriers are data and people. Banks cite security and data leakage (55%), limited internal expertise (50%), data quality (40%) and vendor AI risk (40%) (Wolf & Co). The relationship model itself is working. Community banks raised their share of domestic deposits from 14.8% before 2020 to 16.6% by mid-2025, while the largest banks had not won back deposits lost in the downturn (IntraFi). Clients are staying with banks that know them. That is where AI investment has the most to add, and where it is spending the least. ## Your Advantage Is Real, but Mostly Reactive Regional bankers often know their clients better than a national bank can. What they lack is a way to act on that knowledge before the client asks. A regional banker often serves the founder of a local company, the company itself, the family's real estate, its trust and, in time, the children and the aging parents. That reach across personal, private, commercial and business banking is something a national bank's segmented model struggles to match. Clients increasingly want that advice. In J.D. Power's 2025 study, 46% of retail bank customers recalled receiving financial advice from their bank, up from 34% in 2021, and 26% said they were very interested in it, up from 19% (ABA Banking Journal). The trouble is that the relationship lives in people, while the data lives in separate systems. Illustrative family relationship at a regional bank The core sees six sets of accounts; the banker sees one family. Because no system links them, service waits for the client to call. AI can see what the banker sees only when the data is linked by family and business, which is why data ownership comes first in this guide. ## Six Areas Where AI Pays Off AI pays off for a regional bank in three ways: it grows relationships, protects clients and the bank, and lets the same team serve more clients. Risk and compliance matter, but they are two of six areas, not the whole agenda. Clarista framework · three goals, six areas, one foundation Each area already has early evidence at banks of your size. The results below are as reported by the banks or their vendors and have not been independently verified (AI for Banking). AreaWhat AI doesEarly evidence Know the whole relationship | Links accounts, loans, entities and people into one family or business view | Horicon Bank uses AI customer intelligence across business lines | Advise before they ask | Flags a moment that matters and tells the banker why | Synovus deployed personalized financial insights at scale with Personetics | Stop fraud and scams | Reads checks, payments and behavior together | First Community Bank and Trust cut manual check review time 65% | Tighter oversight | Gathers the facts behind an alert, an exam request or a vendor review | F&M Bank estimates 5 minutes of investigator time saved per AML alert | Serve faster | Answers staff and client questions from your own policies | BankUnited's staff assistant answers in under 10 seconds | Run lean operations | Reads documents and drafts reviews and packs | A regional community bank reports loan processing 50% faster | The pressure on the Protect column is rising. Americans reported $20.9 billion in internet crime losses to the FBI in 2025, up 26%. People over 60 lost $7.7 billion, up 59%, and 22,364 complaints mentioned AI (FBI IC3). FinCEN has issued an alert on deepfake fraud against financial institutions (ICBA) and found 15,417 reports of mail theft-related check fraud, worth more than $688 million, in six months of 2023 (ACAMS). ## Serving Multi-Generational Families and Business Owners The biggest moments in a family's financial life often pass through a regional bank's accounts first, yet the bank usually hears about them last. The stakes are rising. Cerulli estimates $124 trillion will transfer through 2048, $105 trillion of it to heirs, and more than half from high-net-worth households that make up 2% of all households (Funds Society, citing Cerulli). The bank that is in the room when the money moves is best placed to keep the family. Our paper [The $100T Wealth Transfer Has Started](https://www.clarista.io/whitepapers/the-new-wealth-demand-and-supply-paradox) looks at the same shift from the advisor's side. The table shows moments a regional bank can see coming. Each signal is a rule written from data the bank already holds, so it can be explained to a banker and to an examiner. MomentSignal from your dataProactive conversationWho decides Business sale or liquidity event | A large incoming wire to the operating account; the credit line paid off; a new entity opened | Options for the proceeds; an introduction to private banking and trust | Relationship manager with a private banker | Succession | Founder's ownership on file; a next-generation family member added as a signer | Succession and estate planning with the trust team | Relationship manager | Inheritance | An estate account opened; trust distributions begin | Meet the heirs before the money moves | Private banker | Next-generation milestones | First payroll deposits; a mortgage inquiry | A first mortgage, credit and savings, introduced through the family | Retail banker | Business cash strain | Operating balances below their usual range; a covenant test date approaching | A credit line review before a missed payment | Commercial lender | Elder protection | A new payee, a new authorized user, sudden large withdrawals | A check-in call and a fraud review | Branch manager with the BSA team | ### Capturing Why, Not Only What Bank systems record what happened: a loan approved, a referral made. They rarely record why, or why a banker chose not to act. That reasoning is the bank's institutional knowledge, and it walks out the door when a banker retires. How a proactive signal works · 4 steps and a feedback loop When bankers record their action and its reason, the bank learns which signals matter for which clients, and new bankers inherit the reasoning along with the book. Our papers [The Thinking System](https://www.clarista.io/whitepapers/the-thinking-system-institutional-knowledge-ai) and [Building the Thinking System](https://www.clarista.io/whitepapers/building-the-thinking-system-ai-maturity-model) explain how this works in wealth management; the same approach applies to relationship banking. ## The Phased Guide A regional bank can reach proactive advice in four phases, each starting only when the one before has produced evidence a board and an examiner can follow. Clarista phased guide · 4 phases, 3 gates The order matters. Internal assistance comes before client-facing work because staff review every output, which builds trust and a record. Fraud comes before advice because it protects clients and the bank's balance sheet, and it uses the same linked data. Phases can overlap once their gates are met. PhaseWhat you needWhat you deliverWhat you can show an examiner 1. Govern and Connect | An executive owner; an AI use policy; your core and vendor contracts | An inventory of AI uses with a risk tier for each; data rights reviewed against the September 2026 joint statement; core, loan, trust and CRM data linked by customer and household | The inventory, the tiering, data lineage and who can see what | 2. Assist Your People | One process and the staff who run it today | Answers to staff questions from your own policies; alert, document and exam preparation; committee packs from live data | A human review of every output, and a log of every question, answer and data access | 3. Protect Your Customers | Fraud and BSA leaders; alert history | Check, wire, P2P, scam and deepfake signals in one customer view; elder exploitation flags | Alert outcomes, false positives, analyst decisions and SAR support | 4. Advise Proactively | Relationship managers and private bankers who will act on signals | Signals for life and business moments; the banker's action and reason recorded | Each signal's rule, the data it used, and the decision taken | For each phase, the software is built for your bank and the data stays in your bank's name. How to take an AI-built application from a working concept to governed production is covered in [You Built the App. Now Comes the Hard Part](https://www.clarista.io/whitepapers/ai-built-apps-to-production-financial-services). When to use an autonomous agent rather than an application is covered in [AI Agent Governance: Agents vs AI-Built Apps](https://www.clarista.io/whitepapers/ai-agent-governance-financial-services). ## What Examiners and Boards Will Ask There is still no AI-specific rule for banks, but in 2026 regulators set out what they will look at: an inventory of AI uses, oversight of vendor AI, and the bank's leverage over its core provider. DateIssued byWhat it saysWhat it means for your AI program Sept 16, 2026 | CSBS, for state examiners | AI supervisory framework covering governance, an inventory of AI uses, vendor AI, and generative and agentic AI, with an optional risk tiering worksheet | Keep an inventory with a risk tier for each use, including AI inside vendor products | Sept 11, 2026 | Federal Reserve, FDIC, OCC | Joint statement on core service providers, naming limits on third-party integration, undefined deconversion fees and back-billing | Review your core contract for data access and integration rights before you build | Sept 11, 2026 | Federal Reserve, FDIC, OCC | Proposed third-party risk management guidance, tailored to the bank's size and risk, out for comment | Oversight of AI vendors should match the risk of each use | Apr 17, 2026 | Federal Reserve, OCC, FDIC | Revised model risk management guidance (SR 26-2), principles-based and covering vendor models; generative and agentic AI excluded, with separate guidance promised | Govern generative AI under your existing risk practices until that guidance arrives | Nov 2024 | FinCEN | Alert on deepfake fraud schemes, with red flags and a SAR key term, FIN-2024-DEEPFAKEFRAUD | Build deepfake red flags into onboarding and account-change checks | ### Questions Your Board Should Ask - Which AI is in use today, including AI inside our vendors' products? - What data does each use see, and who can see its results? - Who reviews an AI output before it reaches a client, a credit file or a filing? - Can we show how each result was reached, from the data to the decision? - What happens to our software and data if a vendor relationship ends? - Which uses have we chosen to wait on, and why? The same evidence answers both audiences. Our papers [What Examiners Are Actually Asking About AI](https://www.clarista.io/whitepapers/sec-finra-2026-ai-examination-priorities) and [The Door AI Walks Through](https://www.clarista.io/whitepapers/permission-aware-data-access-ai-agents) show how to keep that evidence as a by-product of the work, including who saw what data through AI. ## How to Know It Is Working Judge each phase by what changes for your staff and clients, measured from records the work already creates rather than from a separate survey. PhaseWhat to trackWhere the number comes from 1. Govern and Connect | Share of accounts linked to a household; data checks passed on each load; AI uses inventoried and tiered | The data platform's own logs and the AI inventory | 2. Assist Your People | Hours returned to staff; days to assemble an exam response or committee pack | Task timestamps before and after | 3. Protect Your Customers | Time to clear an alert; fraud losses stopped; false positives per analyst | Case management and loss records | 4. Advise Proactively | Proactive conversations per banker; share of signals acted on, with reasons; families kept through a business sale or an inheritance | The signal record and the core | The last measure matters most and takes longest to show. A family that stays with the bank through a generational handover is the clearest proof that the relationship advantage held. ## Your First 90 Days In 90 days a regional bank can finish Phase 1 groundwork and put one working process in front of its staff. - Name an executive owner for AI and agree a one-page AI use policy - List every AI use in the bank today, including AI inside vendor products, and give each a risk tier - Review your core contract for data access, integration rights, deconversion fees and back-billing - Pick one process from the tables in this paper that staff find slow and that crosses systems - Build a working concept on synthetic data or one report you already export, outside production - Have the staff who run that process today review every output, and keep the log - Take the concept, the log and a production plan to your board and your risk committee Clarista builds that first concept for regional and community banks at no cost: a live demo on your data or on synthetic data in about two weeks, a walkthrough of how it was built, and a plain view of what production would take. The software and data stay in your bank's name. Contact Clarista to choose a process. ## Frequently Asked Questions ### How are regional and community banks using AI in 2026? Mostly for internal work. In a 2026 survey of banks with $750 million to $25 billion in assets, 95% were exploring employee productivity tools and 20% marketing and personalization; one bank in 17 had a scaled, governed program. Fraud detection is the top AI investment priority for community lenders, at 66%. ### Should we wait for our core provider to deliver AI? Take what your core provider delivers, because it will improve work inside the platform. It reaches every bank on that platform at the same time, so it keeps you level with your peers. The processes that set you apart run across systems and need software built for your bank. ### Do we need an IT department to build our own AI software? A bank needs an owner for each process and staff who will review outputs, not a development team. AI now writes most of the code in days, and a partner can review, secure, host and maintain it in your bank's name. ### What does it mean to own our data? It means a copy of your data, linked by customer and household, checked on every load and governed by role, kept in an environment in your bank's name. The core stays your system of record. ### How can a regional bank compete with national banks on personalization? By acting on what its bankers already know. Link each family's personal, private, commercial and business relationships, write signals from that data for the moments that matter, and let bankers decide and record why. ### How does AI help with fraud at a community bank? It reads checks, payments and customer behavior together, so a scam, an altered check or a deepfake request stands out against the customer's usual pattern. Reported losses to the FBI reached $20.9 billion in 2025, and $7.7 billion of that fell on people over 60. ### What do examiners expect from a bank's AI program? An inventory of AI uses with a risk tier for each, oversight of vendor AI, human review of outputs, and a record of how each result was reached. The CSBS framework of September 2026 and the revised model risk guidance of April 2026 set out these expectations. ### Where should a regional bank start with AI? With one slow process that crosses systems, built as a concept on synthetic data or an existing report, reviewed by the staff who run it today. Phase 1 groundwork, the AI inventory and the data rights review, can run alongside it. ## References Regulators and government - Board of Governors of the Federal Reserve System, FDIC and OCC, Joint Statement on Community Banks' Engagement with Core Service Providers, September 11, 2026 - Federal Reserve, SR 26-7, October 5, 2026 - Federal Reserve, Agencies seek comment on proposed third-party risk management guidance, September 11, 2026 - OCC, Bulletin 2025-39: Request for Information on Community Banks' Engagement with Core Service Providers, November 24, 2025 - FBI Internet Crime Complaint Center, 2025 IC3 Annual Report Industry research and reporting - Wolf & Company, Survey Results: 2026 AI Adoption and Maturity in Banking, May 1, 2026 - MeridianLink, 2026 AI Priorities Benchmark Report, October 8, 2026 - ABA Banking Journal, Survey: AI, cybersecurity top priorities to community banks in 2026, January 28, 2026 - ABA Banking Journal, Survey: More bank customers are seeking financial advice, June 3, 2025 - IntraFi, How Deposit Flows Are Reshaping the U.S. Banking Landscape - Funds Society, $124 trillion will change generations by 2048, citing Cerulli Associates - Fiserv, Fiserv forms strategic collaboration with OpenAI, May 14, 2026 - PYMNTS, FinTechs and Community Banks Tell OCC to Open the Data Pipes, February 3, 2026 - Consumer Finance Monitor, CSBS releases artificial intelligence supervisory framework for state examiners, September 18, 2026 - RiskTemplate, OCC, Fed and FDIC replaced SR 11-7, May 2, 2026 - ICBA Independent Banker, Taking the Proper Steps to Unmask Deepfake Fraud, May 1, 2025 - ACAMS, FinCEN lists check fraud methods; alteration tops the list, September 2024 - AI News, JPMorgan expands AI investment, March 5, 2026 - AI for Banking, AI in Community and Regional Banking: Case Studies Related Clarista white papers - [The Thinking System: Why AI Cannot Learn Your Firm's Institutional Knowledge](https://www.clarista.io/whitepapers/the-thinking-system-institutional-knowledge-ai) - [Building the Thinking System: Four Layers, and the Order You Build Them In](https://www.clarista.io/whitepapers/building-the-thinking-system-ai-maturity-model) - [The $100T Wealth Transfer Has Started](https://www.clarista.io/whitepapers/the-new-wealth-demand-and-supply-paradox) - [The SMB Lending Land Grab](https://www.clarista.io/whitepapers/winning-the-next-generation-of-smb-lending) - [What Examiners Are Actually Asking About AI](https://www.clarista.io/whitepapers/sec-finra-2026-ai-examination-priorities) - [The Door AI Walks Through: Permission-Aware Data Access for AI Agents](https://www.clarista.io/whitepapers/permission-aware-data-access-ai-agents) - [You Built the App. Now Comes the Hard Part](https://www.clarista.io/whitepapers/ai-built-apps-to-production-financial-services) - [AI Agent Governance: Agents vs AI-Built Apps in Financial Services](https://www.clarista.io/whitepapers/ai-agent-governance-financial-services) - [Your Data Architecture Is About to Break](https://www.clarista.io/whitepapers/how-ai-adoption-is-sending-data-teams-back-to-the-drawing-board-on-enterprise-data-architecture) - [Wealth Data Aggregation: What It Takes, What It Costs and What It Makes Possible](https://www.clarista.io/whitepapers/wealth-data-aggregation)