Regulatory Technology in Banking

Let’s talk about the part of banking no one brags about, regulatory and compliance. It’s slow. It’s manual. It’s endless. And it’s about to become one of the biggest #AI goldmines in #finance. Please trust when I tell you, Bank of America, Citi, HSBC, UBS, Wells Fargo, TD, Capital One, and others are all over this.   Banks are drowning in rule changes: #SEC, #OCC, #FINRA, #FCA, #EBA, you name it. Each update triggers weeks of human review: “Does this apply to us? Do we need to rewrite a policy?”   That manual work is expensive and risky. Every missed rule = a fine, a headline, a reputation hit, a delayed product launch.   Generative and agentic AI systems are absolutely a perfect fit for such circumstances. Latterly a sweet spot. I am seeing logic that can already parse new regulations, connect them to internal policies, and even draft first versions of updates.   The ROI comes in two forms: • Cost savings: Less manual review and faster audits, which saves compliance headcount hours. • Risk reduction: Harder to quantify, but catching regulatory changes quickly avoids fines and reputational hits.   It’s one of the few AI use cases in banking already showing measurable ROI without touching sensitive customer data.   If you want to spend your #AI dollars wisely today for the needs banks will pay for tomorrow here’s where innovation teams, startups, and ISVs should step up. And BTW, not just banks. This is true for the entire #BFSI sector.   1️⃣ Regulatory forecasting, or "predictive risk" as the industry calls it. AI that reads draft regulations and predicts business impact before they’re enforced. 2️⃣ Continuous monitoring. Systems that link policies to live IT and data environments catching compliance drift automatically. On the fly. 3️⃣ Cross-border models that compare and reconcile overlapping rules across the US, UK, and EU eliminating millions in redundant policy work.   This is where the enterprise AI market in finance and insurance is heading from reading rules to running compliance as code.   Everyone will be writing and talking about AI copilots for bankers. While the real ROI right now is copilots for compliance replacing hours of manual review with real intelligence and traceability.   If you’re building or adopting AI in the GRC space, this is your moment. You can be a start up or in-house innovation team inside a BFSI incumbent. Doesn’t matter. Low glamour. High payoff. Real adoption.   👉 I productionize AI in financial institutions — strategy to adoption, minus the hype.

Navigating India's Digital #Banking Future: Reserve Bank of India (RBI) 's New Authorization Directions. The Reserve Bank of India (RBI) has just unveiled its comprehensive "Reserve Bank of India (Digital Banking Channels Authorisation) Directions, 2025". This significant draft, effective from its final issuance date, aims to streamline and strengthen the regulatory framework for digital banking services across India. This isn't just an update; it's a foundational shift for all commercial and cooperative banks operating in India! Let's dive into what these directions mean for the banking landscape: 1. Defining the Digital Frontier: The RBI clearly distinguishes between various digital banking channels: • Digital Banking Channels themselves encompass services offered via websites (internet banking), mobile phones (mobile banking), or other digital channels, involving significant process automation and cross-institutional capabilities. • Internet Banking allows customers to manage accounts and access services online. • Mobile Banking facilitates banking through mobile applications, USSD, and SMS. Crucially, the directions differentiate between two levels of digital service based on functionality: • View Only Banking Facility: This is for non-transactional services that do not alter a customer's assets or liabilities. Think balance inquiries, statement downloads, or viewing. While loans and fund transfers cannot be directly provided, banks can offer downloadable forms for such facilities. • Transactional Banking Facility: This is the full suite, allowing all fund-based or non-fund-based banking services. This distinction is key to understanding the varying compliance requirements. 2. Dual Pathways for Authorization – A Tailored Approach: The RBI has established two distinct eligibility criteria, reflecting the risk profiles of the services offered: • For "View Only" Banking Facility:     ◦ Banks must have fully implemented Core Banking Solution (CBS).     ◦ Their public-facing IT infrastructure must be enabled to handle Internet Protocol Version 6 (IPv6) traffic.     ◦ Upon launching, banks must inform the concerned RBI regional office within thirty days and submit a ‘Gap Assessment and Internal Controls Adequacy’ (GAICA) report. This demonstrates a lighter, but still structured, oversight for lower-risk services. • For "Transactional" Banking Facility:     ◦ This requires prior approval from the Reserve Bank.     ◦ Applications must be submitted via the PRAVAAH portal with a board resolution and supporting documents.     ◦ The criteria are significantly more stringent, emphasizing robust financial health and technological readiness:         ▪ Full CBS and IPv6 enablement of IT infrastructure.         ▪ Compliance with minimum regulatory Capital to Risk-Weighted Assets Ratio (CRAR).         ▪ Net worth of at least the minimum regulatory requirement or ₹50 crore, whichever is higher, as of March 31st of the preceding financial year.

How Banks Ensure Regulatory Compliance: Conducting Treasury Activities Regulatory compliance is a cornerstone of modern banking, ensuring financial institutions operate within legal frameworks. For banks, particularly in treasury activities, maintaining compliance is crucial to uphold trust, manage risk, and avoid significant penalties. Here is how banks ensure regulatory compliance in their treasury operations: Understanding Regulatory Requirements: Banks must have a comprehensive understanding of relevant regulations, including international directives and national rules. These cover capital adequacy, liquidity management, and risk assessment. Robust Internal Controls: Implementing robust internal controls is essential. Compliance departments monitor and enforce adherence to regulatory standards through regular audits and reviews of treasury activities. Effective Risk Management: Banks use risk management frameworks to identify, assess, and mitigate risks in their treasury operations. This includes market risk, credit risk, and operational risk, maintaining a conservative approach. Training and Education: Continuous training ensures staff are aware of regulatory changes and understand their roles in compliance. Specialised training for treasury staff focuses on specific compliance requirements. Technology and Automation: Advanced software solutions monitor transactions, manage data, and generate compliance reports. These tools detect potential compliance issues in real-time for prompt corrective actions. Regular Reporting and Documentation: Accurate and timely reporting to regulatory bodies is essential. Comprehensive documentation of all treasury activities ensures transparency and provides a clear audit trail. Engagement with Regulators: Proactive engagement with regulators keeps banks informed about upcoming regulatory changes and provides guidance on compliance matters, addressing issues before they escalate. Scenario Analysis and Stress Testing: Conducting scenario analysis and stress testing helps ensure compliance under various market conditions. Banks assess the impact on their treasury activities to ensure they can withstand adverse conditions. Ensuring regulatory compliance in treasury activities is a multi-faceted process requiring understanding regulations, implementing robust controls, managing risks, continuous education, leveraging technology, accurate reporting, engaging with regulators, and conducting scenario analysis. By prioritising compliance, banks navigate the complexities of the regulatory landscape, contributing to the stability and integrity of the financial system.

Deploying LLMs in Banking? Don’t Let the Machine Just Vibe! In regulated industries like banking, a customer saying “I’m sure there’s a good reason…” may sound polite—but it could be hiding a real complaint. Miss it, and you're not just risking poor service—you’re risking regulatory trouble. That’s the problem I tackle in this paper: “Knowledge Graph as Guardrails: Achieving Conceptually Sound LLM Complaint Classification Without Fine-Tuning” https://lnkd.in/ez8RWhcS Instead of relying on zero-shot LLMs to make judgment calls, we use knowledge graphs as external decision frameworks to ensure: - Structured, component-level analysis of customer statements - Regulatory logic applied through explicit, auditable rules - Transparent, explainable decisions—not black-box outputs - Built-in triggers for human review when confidence is low This makes the use of LLMs conceptually sound—exactly what SR 11-7 demands in high-risk applications. If you're building or validating GenAI in banking, this framework bridges the gap between raw model power and regulatory-grade accountability.

In July, the European Banking Authority (EBA) released an Opinion on #ML #TF risks that, among other things, highlights a misalignment between #innovation, #technology, and #accountability. As I break from interview prep and research (yes dear reader, I aim to rejoin institutional bliss), I offer a few thoughts. Over 1/2 of serious #compliance failures involve improper use of #RegTech tools.  “…the widespread use by financial institutions of RegTech products by a small number of providers, and off-the-shelf solutions that are not fit for purpose, exacerbate vulnerabilities...”.   Yet, to attribute compliance failures solely to the FIs is misplaced. RegTechs share some of that responsibility, but legal frameworks do not recognize that. Relationship Status: It’s Complicated. FIs and FinTechs prioritize speed, scale, and revenue. To move fast, they partner with RegTechs. Buying is easier than building, especially when few firms have the internal expertise to develop, test, and maintain compliance tools. RegTechs are incentivized by market share to deliver audit-passing functionality, optimized for #efficiency, not necessarily #effectiveness. Most platforms are built around the "what", without embedding the "why" behind these processes, which ultimately shapes the "how" the tech should be built. Regulatory logic is sparingly integrated into prod design. Products can become checkbox solutions, built to meet outdated standards. As companies scale, firms tend to buy from “proven” vendors. Once implemented, it takes a catastrophic failure to justify replacement. Unable to rip and replace, firms layer new tools on top of legacy tech for lift. Legal and reputational liability lay with the FI, but when things go wrong, it passes that pain to its respective vendors. Prod improvements become revenue and reputational triage in a market where bad (and good) news travels fast. Let’s be clear - RegTechs are essential. The volume and velocity of finance mean that the automation and scalability are critical. At their best, service providers bring deep tech innovation and operational agility that internal teams cannot replicate. The challenge isn’t their involvement, it’s ensuring they are integrated as strategic partners, with a clear understanding of both the regulatory purpose and operational reality behind the workflows they support. The EBA Opinion *applies everywhere* 📌 An alignment of accountability and oversight in vendor-client relationships. 📌  Best practices where regulators audit not just the result, but how the tech reached the decision, the controls around its logic, and its resilience. 📌  Tech design grounded in legal and regulatory logic. Let’s not confuse digital infrastructure with regulatory capacity. https://lnkd.in/evfhGdmH

Privacy-preserving cryptography will matter to banks only when it improves control, not just confidentiality. The prevailing assumption is that technologies like Fully Homomorphic Encryption, Zero-Knowledge proofs, and Multi-Party Computation allow data to remain private while still being usable, making them naturally suited for banking. That assumption is technically correct but institutionally incomplete. Banks do not adopt technology because it is elegant. They adopt it when it strengthens governance, reduces risk, or improves capital efficiency. The deeper mechanics are structural. These technologies allow computation on encrypted data, verification without disclosure, and shared processing without exposing raw information. That changes how sensitive data can move across institutions, regulators, and counterparties. Three areas become relevant. First, regulatory reporting. Banks can prove compliance conditions without exposing underlying customer data, reducing friction between privacy obligations and supervisory requirements. Second, interbank collaboration. Fraud detection, credit assessment, and risk sharing can be coordinated across institutions without centralizing sensitive data. Third, custody and transaction validation. Multi-party authorization frameworks can replace single-point control without weakening accountability. The second-order effect is operational. If implemented correctly, these technologies reduce the trade-off between data privacy and system transparency. If implemented poorly, they introduce complexity that obscures accountability rather than strengthening it. Banks are not constrained by lack of cryptographic tools. They are constrained by the need to integrate those tools into governance, auditability, and legal enforceability. For boards and executives, the question is not whether privacy-preserving technology is useful. It is whether it can be embedded into existing control frameworks in a way that enhances accountability while protecting sensitive data.

Navigating the AI Maze in Banking: Are You Prepared for the Compliance Surprises? 🤯 At Future Branches, I'll talk about how Artificial Intelligence is rapidly transforming how banks and credit unions operate – from streamlining loan processing to enhancing fraud detection and personalizing customer experiences. The potential is HUGE. But as we race to innovate, are we fully anticipating the regulatory and compliance curveballs AI can throw? I'm seeing (and hearing about!) financial institutions running into unexpected hurdles. It's not just about implementing cool tech; it's about ensuring that these sophisticated systems align with a complex web of existing (and evolving!) regulations. Think about: Data Privacy & Governance: How are new AI uses squaring with GDPR, CCPA, and other data protection mandates, especially when data appetite for AI models is vast? Bias & Fairness: Are your AI models inadvertently creating discriminatory outcomes in lending or customer service, and how do you prove they aren't? This is a big one for fair lending laws! Third-Party Vendor Risk: If your AI solution comes from a vendor, how are you managing the compliance pass-through and oversight? Keeping Up with Shifting Sands: Guidance on AI is still developing. What seems compliant today might need adjustments tomorrow. These aren't just IT problems; they are institution-wide strategic challenges with significant legal and reputational implications. I'm curious to hear your experiences and thoughts: ➡️ What's the BIGGEST regulatory or compliance surprise your institution (or one you know) has encountered when implementing AI? ➡️ How are you proactively tackling these potential "unknown unknowns" in AI compliance? ➡️ What kind of guidance do you think is most needed from regulators in this space? Let's discuss and learn from each other! 👇 #ArtificialIntelligence #AIinBanking #Fintech #CreditUnions #RegulatoryCompliance #RiskManagement #FinancialServices #Innovation #Compliance #TechRegulation

I was recently speaking with group of industry peers, and our conversation took an interesting turn. They asked, “How can compliance be more than just a regulatory checkbox?” That sparked a discussion on outcome-based use cases—a shift that transforms compliance from a cost center into a business enabler. Traditionally, banks have approached compliance with a focus on meeting regulatory requirements—KYC, AML, stress testing. But what if we redefined success to focus on measurable business outcomes? Key pillars : - Clear Outcomes – Move beyond process completion to achieve tangible results (e.g., 30% faster onboarding). - Advanced Technology – Leverage AI/ML and predictive analytics for real-time insights and automation. - Feedback Loops – Continuously refine based on real-world results and evolving regulations. The benefits? Greater customer trust, operational efficiency, and regulatory confidence—all while making compliance a strategic value driver. This shift is happening now, and it’s changing the game. Interesting days ahead! #ComplianceInnovation #OutcomeDriven #RegTech #RiskManagement #DigitalTransformation #AIinBanking #BankingCompliance #OperationalExcellence #DataDrivenCompliance

The Enterprise AI Map: Day 24/30 AI governance in financial services has requirements no other industry faces. Financial services is not just another vertical for AI governance. It is the most complex regulatory environment in the world, and AI adds layers of risk that existing compliance frameworks were not designed to handle. Model risk management. US banks are subject to SR 11-7 (OCC/Fed) and UK firms to SS1/23 (PRA). These frameworks require model validation, ongoing monitoring, and documentation for any model used in business decisions. When employees use AI tools to analyze data, generate forecasts, or inform lending decisions, those interactions may trigger model risk management requirements that nobody is tracking. Fair lending implications. If AI is used anywhere in the credit decisioning process, even informally, it creates fair lending risk. An analyst who uses an AI tool to "pre-screen" loan applications before formal review has introduced an ungoverned model into a regulated process. Without visibility into AI usage, compliance teams cannot identify these risks. Data handling under GLBA and PCI. Financial data has specific handling requirements. When an employee pastes account numbers, transaction data, or customer financial records into an AI tool, the data handling provisions of GLBA and PCI-DSS may be triggered. Most AI tools are not designed to meet these standards. Third-party risk management. Financial regulators (OCC, FDIC, FRB) require rigorous third-party risk management. Every AI vendor is a third party. Every AI tool that touches financial data needs to be assessed against the same standards as any other vendor in the ecosystem. Audit expectations. Financial regulators expect continuous monitoring, not point-in-time reviews. When examiners ask about AI governance, they want to see real-time dashboards, automated alerting, and evidence of ongoing compliance. A quarterly spreadsheet review does not meet the standard. The financial services enterprises we work with at Oximy have the most sophisticated requirements, and they are also the ones moving fastest on governance because the regulatory consequence of not moving is existential. #EnterpriseAIMap #AIGovernance #FinancialServices #Banking