How Banks can Implement AI

Your AI project will succeed or fail before a single model is deployed. The critical decisions happen during vendor selection — especially in fintech where the consequences of poor implementation extend beyond wasted budgets to regulatory exposure and customer trust. Financial institutions have always excelled at vendor risk management. The difference with AI? The risks are less visible and the consequences more profound. After working on dozens of fintech AI implementations, I've identified four essential filters that determine success when internal AI capabilities are limited: 1️⃣ Integration Readiness For fintech specifically, look beyond the demo. Request documentation on how the vendor handles system integrations. The most advanced AI is worthless if it can't connect to your legacy infrastructure. 2️⃣ Interpretability and Governance Fit In financial services, "black box" AI is potentially non-compliant. Effective vendors should provide tiered explanations for different stakeholders, from technical teams to compliance officers to regulators. Ask for examples of model documentation specifically designed for financial service audits. 3️⃣ Capability Transfer Mechanics With 71% of companies reporting an AI skills gap, knowledge transfer becomes essential. Structure contracts with explicit "shadow-the-vendor" periods where your team works alongside implementation experts. The goal: independence without expertise gaps that create regulatory risks. 4️⃣ Road-Map Transparency and Exit Options Financial services move slower than technology. Ensure your vendor's development roadmap aligns with regulatory timelines and includes established processes for model updates that won't trigger new compliance reviews. Document clear exit rights that include data migration support. In regulated industries like fintech, vendor selection is your primary risk management strategy. The most successful implementations I've witnessed weren't led by AI experts, but by operational leaders who applied these filters systematically, documenting each requirement against specific regulatory and business needs. Successful AI implementation in regulated industries is fundamentally about process rigor before technical rigor. #fintech #ai #governance

Gen AI Journey at Citizens Bank from experimentation to rollout: Embracing gen AI is a must-have for every bank to stay ahead. Most importantly, it is the set of right foundational building blocks which drives the momentum for the future to scale with tangible benefits. Beth Johnson, a data geek and of course Vice Chair and Chief Experience officer at Citizens, provides precise call to action insights which is working at Citizens with the first principle on "Protect the customer" and "Protect the brand": 1.      Governance Steering Co to use AI responsibly – Bank has formed the steering co with Data & Analytics, Tech & Cyber, Legal, Risk, HR to focus on Value with managed risk with the goal to move from experimentation to rollout. Prioritized use case based on risk classification with RoI and without exposing lots of customer data. Bank has started with medium to low risk with human in the loop. Few initial use cases include for Developer persona (software development for tech upgrade from old to new), Contact Center reps (Knowledge Mgmt.), Branch personnel (Identify fake checks - Fraud). 2.      Talent & Colleague education – Constant focus to educate colleagues through industry insights, empower with tools and leverage existing analyst, intern programs in data science to develop new and pertinent models, for example Fraud models for deep fakes in check washing or reducing false positives. 3.      Platform centric approach to scale – Bank has taken a pattern centric approach to scale and safeguard customer data. For example, platform for knowledge management use case for contact center reps can be reused for similar use cases. 4.      Codified robust toll gates and right guardrails – Button up for entire journey taking a regulatory lens from test to rollout with potential scenarios / accidents and develop right guidelines and tollgates codified in the platform. Start with limited use of customer data. 5.      Data democratization through data marketplace & Journey of continuous evolution & innovation – Data patterns continue to emerge as we take a customer journey view. Bank is providing access to the data to the SMEs to identify the use cases and pattern which can further be used to solve the newer problems. This will create revenue opportunities in the space of Payments – embedded payments or broader finance, ESG and beyond. Through NGT program, Citizens has invested heavily in cloud adoption for both enterprise apps and data. As the bank is moving from experimentation to production scale, the foundation building block will make it ready to take further leap as we see new regulations such as 1033 for data democratization with 3rd party risk mitigation or Embedded Finance/ Payments again in AML/KYC space for B2B2C use cases. Private Banking, which is a key initiative at the bank, will get huge benefit as the Bank builds on both the breakthrough innovations - initial gen ai use cases and augment with Agentic AI in the future. Citizens #GenAI

🏦 𝐁𝐚𝐧𝐤 𝐂𝐄𝐎𝐬 𝐀𝐫𝐞 𝐁𝐞𝐭𝐭𝐢𝐧𝐠 𝐁𝐢𝐥𝐥𝐢𝐨𝐧𝐬 𝐨𝐧 𝐀𝐈: 𝐓𝐡𝐞𝐢𝐫 𝐒𝐮𝐫𝐩𝐫𝐢𝐬𝐢𝐧𝐠 5️⃣ 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐓𝐡𝐚𝐭 𝐀𝐫𝐞 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐅𝐢𝐧𝐚𝐧𝐜𝐞 𝐅𝐨𝐫𝐞𝐯𝐞𝐫 The recently published Euromoney “𝐀𝐈 𝐢𝐧 𝐁𝐚𝐧𝐤𝐢𝐧𝐠 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤 2025" offers unprecedented insights into how leading FIs are strategically implementing AI. 𝐬𝐨 𝐰𝐡𝐚𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐭𝐨𝐩 𝐛𝐚𝐧𝐤𝐢𝐧𝐠 𝐀𝐈 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬: 1️⃣ 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲, 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧: JPMorgan Chase has given 200,000+ employees (2/3 of staff) access to their proprietary LLM Suite platform, allowing model flexibility while maintaining security. 2️⃣ 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐢𝐧 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦s: Goldman Sachs deployed a firm-wide developer platform connecting AI models to proprietary data with appropriate safeguards, resulting in an AI assistant available to 10,000+ employees. 3️⃣ 𝐑𝐞𝐚𝐥 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐆𝐚𝐢𝐧𝐬 HSBC documented 15-30% efficiency improvements after implementing GitHub Copilot across 10,000 developers. 4️⃣ 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫-𝐅𝐚𝐜𝐢𝐧𝐠 𝐀𝐈 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 NatWest's Cora+ chatbot implementation achieved a remarkable 150% increase in customer satisfaction metrics and 50% reduction in human agent handoffs. 5️⃣ 𝐒𝐦𝐚𝐥𝐥 𝐌𝐨𝐝𝐞𝐥𝐬 𝐌𝐞𝐞𝐭𝐢𝐧𝐠 𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐍𝐞𝐞𝐝𝐬 BNP Paribas partnered with French AI firm Mistral to develop models that can run on private infrastructure for sensitive contract and transaction data. 𝐌𝐲 𝐓𝐨𝐩 🔟 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧𝐬 1. Banking AI strategy has shifted significantly from scattered use cases to “𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮-𝘣𝘢𝘴𝘦𝘥 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩𝘦𝘴 𝘸𝘪𝘵𝘩 𝘤𝘦𝘯𝘵𝘳𝘢𝘭 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦”. 2. 2/3 of JPMorgan's staff already have AI access—showing enterprise-wide commitment 3. Major banks are building abstraction layers (Goldman's developer platform, JPMorgan's LLM Suite) rather than betting on single vendors 4. UBS's exponential AI adoption curve (1M prompts in January 2025 vs 1.75M for all 2024) demonstrates momentum 5. Customer-facing implementations are moving cautiously with human oversight 6. Bank of America's Erica evolution (65% to 95% accuracy) demonstrates measured development 7. The European approach (BNP Paribas with Mistral) shows greater emphasis on data sovereignty 8. Agentic banking concepts are emerging but remain experimental 9. Human oversight frameworks will determine speed of adoption in regulated environments 10. Voice-based interactions appear to be the next frontier beyond text-based systems Most promising implementations will be combining deep domain expertise with cutting edge technical expertise And thoughtfully integrating AI into processes, culture and customer relationships. #Banking #ArtificialIntelligence #FinTech #AIStrategy #Innovation

Today we’re presenting the findings from our clients using AI agents, in production for 3 months. We can cut customer wait times stuck in a KYC/sanctions queue from 20 days to 2 minutes. This is a huge unlock for anyone onboarding customers. “Compliance Officer” is the 5th fastest growing occupation in the United States! Major banks average 307 employees just for KYC alone, yet can't hire more compliance officers fast enough. More than headcount, this costs customers and revenue. We deployed AI agents in production environments at multiple financial institutions for 3+ months and show AI Agents can meaningfully improve KPIs: - For one FI, the daily backlog was 14 hours and they couldn't keep up with it.  - So the backlog kept growing  - As did the average customer wait time stuck in a queue, to 20 days. Using Agentic AI, we were able to  - Automate majority (95%) of the cases and  - bring down daily backlog to 41 minutes (from 14 hours).  - Most importantly, avg customer wait time went down drastically to 2 minutes. Perhaps the most counterintuitive finding. Agentic AI when trained and deployed according to our framework, can be more accurate than humans. We found AI agents follow operating procedures in 100% of cases vs <95% for humans. Humans never follow SOP to the minute details and with rote work, they are more error prone. FI's rightly worry, what about hallucination? What about data privacy? Will the regulator allow it These live, production data points are all within existing regulatory frameworks (SR 11-7 compliant). Our Agentic Oversight Framework maintains complete human accountability while delivering: - Alignment to Standard Operating Procedures (SoPs) - A full audit trail of every data element accessed - A full, explained decision rationale, reviewed before every case is progressed - Continuous learning from expert reviewers - Automated drift detection and safeguards The white paper is a playbook for how financial institutions can safely implement agentic AI while fully complying with regulatory requirements. Real results. Real institutions. Real transformation. You might ask: what is AI about all of this and how's it different from ML and rules based systems. In short, rules systems are rigid but Agentic AI can adapt. All those details in the white paper:

AI Agents in Banking: The Truth No One Talks About Over the last 12 months, I’ve built and deployed 50+ custom AI agents across tier-1 banks and financial institutions. And here’s the truth—what actually works in banking is very different from what most people are selling online. Forget the flashy “$50K/month with no-code AI agents” headlines. In reality, banks aren’t buying dreams. They’re investing in precision, reliability, and measurable ROI—with strict compliance guardrails in place. The most successful AI agents I’ve built don’t try to do everything. Instead, they focus on solving one high-impact problem exceptionally well, such as: 🔹 KYC automation – Extracting and verifying documents, cutting manual review time by 60% 🔹 Fraud detection – Real-time transaction monitoring that reduces false positives by 40% 🔹 Customer service AI – Handling up to 70% of routine inquiries, boosting CSAT and reducing ops cost These agents aren’t built for show. They’re built for scale. They integrate cleanly with legacy systems, follow strict audit trails, and pass scrutiny from compliance and legal teams. Most importantly, they drive outcomes that matter—time saved, risk reduced, and customer satisfaction improved. In the world of banking, flashy doesn’t cut it. Real innovation is quiet, consistent, and measurable. If you’re working on AI for financial services, focus less on what’s trending—and more on what truly moves the needle. #AI #BankingInnovation #AIagents #Fintech #Compliance #RiskManagement #KYC #Automation #RegTech #FraudDetection #CustomerExperience