Scaling Generative AI in Finance

From Unmanageable Risk to Controllable Asset

Confidential

The Compliance Paradox

Compliance and Risk teams are caught between a rock and a hard place.

THE BOARD MANDATE

Adopt AI Now

Pressure to leverage GenAI for efficiency ($4.4T potential value) means saying "no" indefinitely is not an option.

THE REGULATORY FEAR

Deeply Scared of Risk

Teams are terrified of hallucinations, compliance breaches, and lack of control. (95% of pilots fail as a result).

In the absence of a true governance layer, you cannot safely enable AI.

Source: MIT Technology Review; Forbes; McKinsey & Company

The Governance Gap: Why Guardrails Fail

Today's "solutions" were not built for high-stakes, regulated industries.

RAG & Fine-Tuning

Brittle, static, and resource-intensive. Fails to adapt to dynamic policies and introduces new hallucinations.

Prompt Engineering

Unscalable, unreliable, and easily bypassed. Every new policy requires complex, manual rework.

“LLM-as-Judge" Evals

Opaque and inconsistent. You can't govern one black box with another. This is a passive check, not active control.

The Legacy Trap: Built for a Different Era

Your current compliance stack wasn't designed for Generative AI.

“Thousands of Regexes"

A brittle, keyword-based system that can't understand context. Policy updates take 1-2 weeks, creating critical risk windows.

Massive TCO

This legacy stack consumes 20-40% of TCO just in engineering maintenance.

Incompatible with GenAI

Cannot govern nuanced, generative outputs. Bolting on new AI creates more complexity, not more control.

Your architecture is being overwhelmed by
millions of messages per day.

Regulatory Change at Scale

Historical events expose the fragility of legacy compliance: The LIBOR Sunset.

THE HISTORICAL REALITY (Manual & Slow)

Massive Undertaking

When LIBOR was sunset, tens of thousands of legal documents (ISDAs, contracts) required immediate review and remediation.

Crippling Costs and Time

The process took months to years and cost the industry millions of dollars in legal and operational overhead.

Extended Risk Exposure

Delays in updating brittle, hardcoded systems created extended periods of regulatory risk.

THE CTGT APPROACH (Governed & Instant)

Instant Policy Ingestion

Ingest the new regulatory guidelines (e.g., SOFR standards) in minutes, not months.

Real-Time Compliance

The CTGT engine immediately enforces the new rules on all new contracts and communications, stopping non-compliant activity before it happens.

Zero Engineering Lift

The policy engine adapts without rewriting underlying application code or managing complex rule conflicts.

CTGT

The Enterprise AI Policy Engine

Competitors focus on detection after the fact.

We actively prevent risk in real-time.

CTGT empowers compliance teams to safely adopt AI by enforcing controls before a breach occurs.

Opening the Black Box

Our proprietary Feature-Level Intervention technology gives you real-time control over any model.

CURRENT STATE (Jerry-Rigged Approaches)

AI has been "bolted on" without true governance. Teams rely on brittle prompts or slow, post-hoc checks. These treat LLMs as a black box and fail to stop nuanced risk.

CTGT (Active Governance Layer)

The necessary layer for enterprise AI. Intervenes at the model's "feature level" to actively enforce policy before the user ever sees the output.

Proof Point: Unlocking a Censored Model

Using our technology, we identified the specific features causing censorship in the DeepSeek model. At runtime, we down-sampled the probability of those features, removing the bias without retraining the model.

The AI Governance Fabric

1. INGEST

Your Policies (FINRA, SEC, SOPs)

Your Data (Trading, Client, Market)

→

2. ENFORCE

CTGT Policy Engine

Real-time, model-agnostic remediation and active enforcement.

→

3. AUDIT

Defensible, Immutable Audit Trail

Full traceability for every decision.

This is the Triangulated Intelligence platform for high-stakes enterprise.

The Speed-to-Policy Revolution

Typical New Vendor Procurement Cycle

1.5 YEARS

Updating a Legacy (Regex) Rule

2 WK

Deploying a New Policy with CTGT

30 MIN

Eliminate risk windows and bypass 18-month procurement cycles. Upload a doc. Go live.

Deep Dive: Speed-to-Policy in Action

Now Insurance (Inc. 5000, Lloyd's Portfolio Co.)

THE CHALLENGE

Scaling AI-powered medical malpractice underwriting. Needed to rapidly deploy LLMs while adhering to complex regulations (e.g., HIPAA) where the margin for error is zero.

THE BOTTLENECK

Traditional governance implementation and vendor procurement typically takes months. Basic guardrails were insufficient for complex underwriting compliance.

THE CTGT IMPACT

1.5 Weeks

To stand up the CTGT governance layer.

Enabled the safe transition of LLMs from internal pilots to high-stakes production environments.

“It didn't just identify that there was a hallucination, it also showed that the hallucination stemmed from our own prompt... To me, that was a gamechanger.”

– Jonathan Sims, Head of Data & Analytics

The AI Firewall: Model-Agnostic & Future-Proof

↓

CTGT AI FIREWALL

Your Policies. Your Data. Your Control.

↓

Wealth Mgmt

Research

Global Markets

Your governance layer outlives any single LLM. No vendor lock-in.

Proof: Unmatched Accuracy & Reliability

Factual Grounding (RAG)

Ensuring answers are based *only* on provided documents (HaluEval-QA).

Baseline Model 92.6%

CTGT-Governed Model 96.5%

Mitigating Falsehoods

Stopping the model from repeating common misconceptions (TruthfulQA).

Baseline Model 21.3%

CTGT-Governed Model 70.6%

3.3x

Improvement in Truthfulness

Proof: Advanced Contextual Reasoning

Our engine moves beyond keywords to understand intent. This is what stops subtle, high-stakes errors.

Logical Inversion

Understands that if "Lerwick is SE of Tórshavn," then "Tórshavn is NW of Lerwick" to answer correctly. The baseline model fails.

Error Correction

Correctly identifies a user typo ("David Of me") and maps it to the correct entity in the source document ("David Icke") to find the fact.

Multi-Step Resolution

Traces pronouns across complex sentences. Correctly identifies "he" as "Kevin Jackson" (from the prior sentence) to answer the query.

This is the reliability required for financial compliance.

Global Financial Services Leader

(Alpha Partner)

THE CHALLENGE

Post-hoc compliance review for 10M+ daily messages on a legacy stack (some dating to the 90s) of "thousands of regexes" and classic ML.

THE SOLUTION

CTGT's Policy Engine scoped for the Wealth Management division, ingesting FINRA, SEC, and internal SOPs to govern communications.

THE RESULTS

Est. 20-40%

Reduction in Engineering TCO*

99.9%+

Policy Adherence Achieved

*Understanding the TCO Reduction (Productivity Multiplier):

Legacy Maintenance: Reduced cost and engineering effort required to maintain brittle, legacy systems (e.g., COBOL, complex Regex).
Eliminated Risk Windows: Removed the downtime, exposure costs, and merge conflicts associated with 1-2 week manual policy updates.
Engineering Velocity: Focused resources on innovation rather than maintaining outdated rule-based systems (10x productivity boost).

The CTGT Fabric

A single, independent platform to secure, control, and audit all AI activity across the enterprise.

Your Path Forward: The Phased Pilot

A low-risk, high-impact path to enterprise-wide AI governance. Weeks, not months.

PHASE 1: PILOT

(Single Legal Entity)

Scope initial use case (e.g., WM Compliance)
Benchmark accuracy & TCO savings
Prove on-premise/VPC deployment

→

PHASE 2: EXPAND

(Cross-Division)

Roll out to adjacent units
(e.g., Research, Global Markets)
Add new policy sets & use cases

→

PHASE 3: SCALE

(Enterprise "AI Firewall")

Become the central, model-agnostic governance layer for all AI
Achieve "Triangulated Intelligence"

CTGT

Let's build with confidence.

Cyril Gorlla

Founder & CEO