How Banks can Ensure Reliable AI Solutions

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

Working with AI Agents in production isn’t trivial if you’re regulated. Over the past year, we’ve developed five best practices: 1. Secure integration. Not “agent over the top” integration - While its obvious to most you’d never send sensitive bank or customer information directly to a model like ChatGPT often “AI Agents” are SaaS wrappers over LLMs - This opens them to new security vulnerabilities like prompt injection attacks - Instead AI Agents should be tightly contained within an existing, audited, 3rd party approved vendor platform and only have access to data within that 2. Standard Operating Procedures (SOPs) are the best training material - They provide a baseline for backtesting and evals - If an Agent is trained on and follows that procedure you can then baseline performance against human agents and the AI Agents over time 3. Using AI Agents to power first and second lines of defense - In the first line, Agents accelerate compliance officer’s reviews, reducing manual work - In the second line, they provide a consistent review of decisions and maintain a higher consistency than human reviewers (!) 4. Putting AI Agents in a glass box makes them observable - One worry financial institutions have is explainability, under SR 11-7 models have to be explainable - The solution is to ensure every data element accessed, every click, every thinking token is made available for audit, and rationale is always presented 5. Starting in co-pilot before moving to autopilot - In co-pilot mode an Agent does foundational data gathering and creates recommendations while humans are accountable for every individual decision  - Once an institution has confidence in that agents performance they can move to auto decisioning the lower-risk alerts.

Govern to Grow: Scaling AI the Right Way    Speed or safety? In the financial sector’s AI journey, that’s a false choice. I’ve seen this trade-off surface time and again with clients over the past few years. The truth is simple: you need both.   Here is one business Use Case & a Success Story. Imagine a loan lending team eager to harness AI agents to speed up loan approvals. Their goal? Eliminate delays caused by the manual review of bank statements. But there’s another side to the story. The risk and compliance teams are understandably cautious. With tightening Model Risk Management (MRM) guidelines and growing regulatory scrutiny around AI, commercial banks are facing a critical challenge: How can we accelerate innovation without compromising control?   Here’s how we have partnered with Dataiku to help our clients answer this very question!   The lending team used modular AI agents built with Dataiku’s Agent tools to design a fast, consistent verification process: 1. Ingestion Agents securely downloaded statements 2. Preprocessing Agents extracted key variables 3. Normalization Agents standardized data for analysis 4. Verification Agent made eligibility decisions and triggered downstream actions   The results? - Loan decisions in under 24 hours - <30 min for statement verification - 95%+ data accuracy - 5x more applications processed daily   The real breakthrough came when the compliance team leveraged our solution powered by Dataiku’s Govern Node to achieve full-spectrum governance validation. The framework aligned seamlessly with five key risk domains: strategic, operational, compliance, reputational, and financial, ensuring robust oversight without slowing innovation.   What stood out was the structure: 1. Executive Summary of model purpose, stakeholders, deployment status 2. Technical Screen showing usage restrictions, dependencies, and data lineage 3. Governance Dashboard tracking validation dates, issue logs, monitoring frequency, and action plans   What used to feel like a tug-of-war between innovation and oversight became a shared system that supported both. Not just finance, across sectors, we’re seeing this shift: governance is no longer a roadblock to innovation, it’s an enabler. Would love to hear your experiences. Florian Douetteau Elizabeth (Taye) Mohler (she/her) Will Nowak Brian Power Jonny Orton

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

New #Fintech Snark Tank post: 𝗪𝗵𝗲𝗻 𝗔𝗜 𝗚𝗼𝗲𝘀 𝗢𝗳𝗳 𝗧𝗵𝗲 𝗥𝗮𝗶𝗹𝘀: 𝗟𝗲𝘀𝘀𝗼𝗻𝘀 𝗙𝗿𝗼𝗺 𝗧𝗵𝗲 𝗚𝗿𝗼𝗸 𝗗𝗲𝗯𝗮𝗰𝗹𝗲 I'm guessing that, by now, most of you have heard that Elon Musk’s AI chatbot, Grok, went disturbingly off the rails. What began as a mission to create an alternative to “woke” AI assistants turned into a case study in how LLMs can spiral into hateful, violent, and unlawful behavior. 𝙈𝙮 𝙩𝙖𝙠𝙚: The Grok debacle is more than just a PR blunder. It's a wake-up call to nearly every industry, in particular banking and financial services. Here’s what banks and credit unions should do now: ▶️ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗮𝗻 𝗔𝗜 𝗿𝗶𝘀𝗸 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘁𝗲𝗮𝗺. Nearly every bank and credit union I’ve spoken to in the past 18 months has developed an “AI policy” and has--or is looking to--establish an “AI governance board.” Not good enough. The issue is much more operational. Financial institutions need feet on the ground to: 1) review model behaviors and outputs; 2) coordinate compliance, technology, risk, and legal departments; and 3) manage ethical, legal, and reputational risks. ▶️ 𝗔𝘂𝗱𝗶𝘁 𝗔𝗜 𝘃𝗲𝗻𝗱𝗼𝗿𝘀. Ask AI providers: 1) What data was the model trained on? 2) What are its safeguards for bias, toxicity, hallucination? 3) How are model outputs tested and monitored in real-time? Refuse “black box” answers. Require documentation of evaluation metrics and alignment strategies. ▶️ 𝗧𝗿𝗲𝗮𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝗹𝗶𝗸𝗲 𝗽𝗼𝗹𝗶𝗰𝗶𝗲𝘀. Every system prompt should be reviewed like a policy manual. Instruct models not just on how to behave—but also on what to avoid. Prompts should include escalation rules, prohibited responses, and fallback protocols for risky queries. A lot more analysis and recommendations in the article. Please give it a read. The link is in the comments. #ElonMusk #Grok #xAI #GenAI #GenerativeAI