🛟 The AI Deregulation Playbook: Reward and Risk

Ride the US zeitgeist but watch for overseas constraints and insider AI fraud. You're navigating a goldmine within a minefield.

Author

Abbie Widin
March 18, 2025

👋 Welcome, thank you for joining us here :)

The AI Check In is your weekly power play to navigate AI governance and strategy in banking and finance.

What to expect this edition:

  •  🛟 Need to Know: The AI deregulation playbook for speed and profit

  • 🥷 Deep Dive: AI's transformative impact on banking profitability

  • ⚔️ Instruments of Mastery: SAS Viya

  • 📈 Trends to Watch: AI-powered insider threats: fraud that outruns detection

Rewrite the rules. Outmaneuver competitors. Stay two steps ahead of regulators.

Let the games begin.

🛟 Need to Know: The AI Deregulation Playbook

AI deregulation is shifting risk management from compliance checklists to executive strategy. With fewer federal guardrails, banks must now self-police AI risks while aggressively investing in AI-driven profitability. Whilst this might look like freedom, it's really a test of your institution's strategic discipline.

🏛 AI Governance Without a Net

The loosening of federal oversight is a double-edged sword: banks gain speed but absorb more liability.

Leading institutions like JPMorgan and Citi aren't celebrating. Instead, they're implementing Ethical AI Committees to audit algorithmic bias and prevent regulatory blowback before it happens.

Meanwhile, global regulators have moved on. The Bank of England now explores stress tests for AI supply chains—modeling cloud outages, data breaches, and chip shortages. U.S. banks that mistake deregulation for permissiveness risk being caught flat-footed when international standards become the de facto requirement.

📊 Strategic AI Investments: Where the Smart Money is Going

Banks are redirecting compliance savings into high-return AI battlegrounds:

  • Cloud-Native AI – Morgan Stanley's uses EC2 from AWS to manage its equities risk system, processing 3 billion points each day.

  • Generative AI Co-Pilots – JPMorgan's IndexGPT now scans large numbers of SEC filings daily, accelerating deal sourcing and leaving competitors struggling with outdated due diligence.

  • AI Talent Cultivation - The Evident AI Index evaluates 50 of the largest banks in North America, Europe, and Asia against 90 individual indicators, including Talent as a critical pillar of AI capability.

💰 The Cost of Getting It Wrong

The financial risks of mismanaging AI are escalating, with each misstep now a direct threat to your balance sheet:

· Goldman Sachs, Citi and JPMorgan have flagged AI hallucinations as a serious financial risk. Unchecked AI decision-making in high-frequency trading isn't just inefficient, it's potentially catastrophic.

· 80% of bank cybersecurity executives say they cannot keep pace with AI-powered threats—a vulnerability your competitors will eventually exploit, especially using external AI vendors without deep moats.

🥊 Your Move:

  1. Adopt Agile AI Governance – Update AI policies every 90 days to match emerging risks—deepfake fraud, quantum vulnerabilities, and synthetic data exploits

  2. Balance Cost & Security in AI Sourcing – Dual-source AI infrastructure: offset lower-cost AI (Alibaba Cloud) with secure, compliant providers (AWS GovCloud)

  3. Embed AI Risk Pricing into Financial Models – Factor AI model failure probabilities into loan covenants, risk-weighted assets, and trading desk P&L

🥷 Deep Dive: AI's Transformative Impact on Banking Profitability

AI is now a financial force multiplier. For U.S. bankers and CFOs, AI adoption is about profit, margin expansion and power. The institutions that wield AI intelligently will dominate the market; those that treat it as a compliance checkbox will become acquisition targets.

💰 Operational Efficiency: The Silent Profit Engine

The first wave of AI profitability is ruthless efficiency.

Leading banks are using AI to slash operational costs while their competitors continue to bleed resources on manual processes:

The Financial Impact: Banks and financial institutions deploying intelligent automation are slashing operational costs by up to 30%, creating an expanding profitability gap that AI laggards cannot overcome. Efficiency is now asymmetric. For those that can’t keep up, it's existential.

Risk Management: AI as Your Defensive Arsenal

With regulators circling, banks need AI-driven risk analytics that don't just identify threats but neutralize them before they materialize:

  • Predictive Risk Analytics – AI models processing real-time financial data can identify credit defaults, market volatility, or cybersecurity breaches before human analysts notice the first warning signs.

  • Regulatory Compliance Automation – While your competitors scramble to interpret new regulations, AI monitoring systems flag non-compliant transactions before they trigger audits or penalties.

Vendor Risk: The Hidden Vulnerability

  • Not all AI partners are built for banking-grade security and compliance. Choosing the wrong AI vendor is potentially catastrophic. The metrics that matter:

  • Model Explainability – If your vendor can't explain every AI decision to auditors, you don't have an AI solution—you have a regulatory time bomb.

  • Data Sovereignty – With geopolitical AI tensions escalating, vendors without clear U.S. regulatory alignment expose you to foreign jurisdiction risks that can't be mitigated.

  • Operational Resilience – AI models dependent on embargoed foreign chips or vulnerable cloud infrastructure aren't assets—they're supply chain liabilities.

📊 Revenue Expansion: AI as Your Growth Engine

The most sophisticated banks have moved beyond defensive AI to offensive strategies that create entirely new revenue streams:

  • Hyper-Personalized Lending Models – AI risk assessments enable targeted credit expansion without proportional risk increase—the holy grail of banking profitability.

  • AI-Driven Wealth Management – Models delivering personalized investment strategies are capturing higher-margin advisory fees while reducing portfolio management costs.

  • Dynamic Liquidity Optimization – AI models that adjust capital reserves in real-time allow forward-thinking CFOs to deploy previously idle capital toward profitable opportunities.

The Profitability Impact: Banks with advanced AI lending models are increasing approval rates while maintaining risk controls, effectively mining new revenue from previously overlooked customer segments. This isn't incremental growth—it's market capture.

🏛 The 2025 Finance Leaders War Room: Strategic AI Priorities

To maximize AI's profit potential while neutralizing risks, tomorrow's banking leaders are focusing on three critical priorities:

  • Data Infrastructure as Foundation – Clean, high-quality data isn't just a technical requirement—it's the cornerstone of AI-driven profitability. Without it, even the most sophisticated algorithms become expensive distractions

  • AI Talent Cultivation – External consultants can implement AI, but they can't build institutional knowledge. Banks that develop internal AI expertise gain both strategic autonomy and competitive intelligence

  • Ethical AI Governance – Forward-thinking institutions recognize that governance isn't about restriction—it's about sustainable growth. Frameworks that prevent bias while enabling AI-driven efficiencies create long-term competitive advantages

The Power Play

The AI profitability gap between leaders and laggards is widening daily. This isn't just about technology adoption—it's about strategic leverage that will define the financial sector's power structure for the next decade.

Banks with mature AI strategies are already outperforming competitors in key metrics:

  • Lower efficiency ratios

  • Higher return on assets

  • Improved net interest margins

  • Reduced risk-weighted assets

🥊 Your Move:

  1. Build AI Infrastructure That Scales – Ensure your AI investments align with revenue strategy, not just compliance mandates—the latter just keeps you in the game, the former helps you win it.

  2. Forge a Data-First Culture – AI is only as powerful as the data feeding it. Centralized, structured data isn't a technical nice-to-have, it's a strategic imperative.

  3. Prioritize AI Security as Competitive Defense – AI models are now prime attack vectors. Proactive cybersecurity is less about protection, it's about staying operational while competitors recover from breaches.

⚔️ Instruments of Mastery - SAS Viya

SAS Viya serves as the AI backbone for banks managing risk, compliance, and capital amid deregulation and escalating fraud threats. For CFOs, SAS Viya functions as an ROE multiplier.

📊 AI-Powered Risk & Compliance

  • Dynamic Capital Allocation – AutoML releases 12-15% of trapped capital for higher-yield investments while remaining compliant with Basel III and Basel IV.

  • Regulatory Agility – AI-driven compliance reduces audit prep by 40%, ensuring real-time oversight.

  • Fraud Prevention – ML detects 94% of synthetic identity fraud pre-approval, cutting losses by $8M monthly.

For Example: National Bank of Greece achieved 22% faster risk reporting using SAS Viya's federated learning for cross-border transactions while maintaining GDPR compliance.

📈 AI as a Profit Engine

  • Credit Scoring: Alternative data boosted loan approvals by 18%.

  • Portfolio Stress-Testing: AI simulations cut capital buffer inefficiencies by 1.4%.

  • Customer Retention: NLP insights preserved $9M annually in prevented churn.

For Example: Goldman Sachs reduced model drift in trading algorithms by 63%, preventing $140M in losses.

🥊 Your Move:

  • Automate Risk Adjustments – Free capital before market shifts

  • Embed AI in Compliance – Preempt audit risks with AI oversight

  • Use AI for Trading & Liquidity – Optimize capital flow in real time

📈 Trends to Watch: AI-Powered Insider Threats: Fraud That Outruns Detection

While AI can be used to stop fraud, it is also driving it out of sight. The next risk wave is internal AI manipulation.

  • Rogue employees are using AI to manipulate financial models, distorting trading, lending, and risk assessments.

  • AI-generated transaction masking is bypassing traditional fraud detection, making bad trades look legitimate.

  • AI-driven market manipulation – Hedge funds and rogue traders weaponizing AI to trigger flash crashes, fake trading signals, and misinformation-based market disruptions.

🥊 Your Move:

  • AI-Driven Behavioral Monitoring: Flag unusual employee patterns before they escalate into financial damage.

  • Zero-Trust AI Governance: Restrict AI decision-making access to prevent internal manipulation of trading models.

  • Real-Time AI Risk Audits: Deploy continuous monitoring to catch fraudulent AI use before it impacts your P&L.

🔮 Next Week

Forget ESG-driven AI governance, instead financial institutions are prioritizing AI investments that drive efficiency, cut costs, and enhance risk management banking and finance.

With global Net Zero off the table, I’ll talk you through how to pivot your AI initiatives fast away from ESG towards efficiency and profitability.

Yours,

Disclaimer

This newsletter is for informational and educational purposes only and should not be considered financial, legal, or investment advice. Some content may include satire or strategic perspectives, which are not intended as actionable guidance. Readers should always consult a qualified professional before making decisions based on the material presented.