Sunday, May 5th, 2024
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Shasat Research Desk | May 2024 | Enterprise AI · Generative AI · Financial Services · RegTech |
8 min read |
As Artificial Intelligence reshapes the global economy, a new and fundamentally more powerful class of AI has emerged. Generative AI — the technology behind large language models, image generation systems, and autonomous reasoning tools — does not simply analyse data or follow rules. It creates. It writes, reasons, synthesises, and produces outputs that did not exist before. For financial institutions, this distinction has direct consequences for risk management, regulatory compliance, investment strategy, and the future of work.
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$1.3T
Projected GenAI
Market by 2032 |
40%
Finance Tasks
Automatable by GenAI |
3×
Faster Compliance
Checks vs Manual |
60%
Firms Actively
Piloting GenAI in 2024 |
For most of its history, artificial intelligence operated within carefully defined parameters. A credit-scoring model learned from historical loan data to predict default risk. A fraud-detection system identified anomalous transactions by comparing them against known patterns. A regulatory reporting tool extracted structured data from defined fields and formatted it for submission.
These are genuine achievements. Traditional AI systems have saved financial institutions billions and materially improved risk management across the industry. But they share a fundamental constraint: they can only work with what they have been explicitly trained to do. Present them with a scenario outside their training distribution and they fail — or produce a confidently wrong answer.
Generative AI breaks this constraint entirely. Instead of learning a set of rules, it learns the underlying statistical structure of vast amounts of data and uses that understanding to produce entirely new outputs — generalising from what it has learned to address problems it has never encountered before.
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Neural Network Architecture
At the core of every Generative AI system is a deep neural network — typically a transformer — trained on billions of data points. Rather than being told what to look for, these networks learn statistical relationships across the entire training corpus, producing outputs that are contextually coherent and technically grounded across domains they were never explicitly programmed for. |
Probabilistic Output Generation
Unlike deterministic algorithms, Generative AI models sample from a probability distribution to generate responses — giving the technology its characteristic fluency and adaptability. Modern financial deployments use retrieval-augmented generation (RAG) to ground outputs in verified data sources, substantially reducing hallucination risk in regulated environments. |
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“Where traditional AI answers questions, Generative AI creates answers that did not previously exist — and this distinction has profound consequences for how financial institutions manage risk, monitor compliance, and develop competitive strategy.” Shasat Market Intelligence · May 2024
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Generative AI is moving rapidly from pilot to production across three core domains in financial services — each presenting distinct use cases, measurable efficiency gains, and governance considerations that require active management.
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Finance & Investment
Quantitative strategy generation, portfolio optimisation, earnings analysis, and investment research synthesis at a speed no human team could match. |
Operations
Intelligent automation of knowledge-intensive tasks, AI-powered customer service, production scheduling, and operational decision support across sectors. |
RegTech
Regulatory text analysis, compliance obligation mapping, automated reporting, and continuous horizon scanning across IFRS, Basel IV, GDPR, and ESG frameworks. |
The finance sector has moved faster than almost any other industry to adopt Generative AI. The combination of vast data volumes, complex analytical requirements, and high-value decisions makes it an almost perfect fit for what the technology does best. Hedge funds and asset managers use Generative AI to synthesise macro data, earnings transcripts, and alternative data into actionable investment theses at a speed no analyst team could replicate.
Citadel, one of the world’s largest hedge funds, has deployed Generative AI to identify alpha signals across vast, heterogeneous datasets — developing quantitative trading strategies that outperform traditional factor-based approaches in several key market segments.
Financial institutions are also deploying Generative AI for proactive risk management. By simultaneously analysing historical loss events, market stress scenarios, and macroeconomic indicators, these systems identify emerging risk concentrations well before traditional early-warning systems would flag them — giving risk managers time to act rather than simply respond.
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Case Study — Manufacturing
Siemens employs Generative AI to optimise production schedules in real time across global facilities, proactively identifying bottlenecks and reducing unplanned downtime — delivering measurable improvements in throughput and asset utilisation. In manufacturing, production optimisation algorithms analyse real-time sensor data and historical performance metrics simultaneously, enabling interventions that traditional rule-based systems would miss entirely. |
Case Study — Banking Customer Service
Bank of America’s virtual assistant Erica, powered by Generative AI, has handled over one billion client interactions — delivering personalised financial guidance and streamlining everyday banking at a scale no human team could support. Intuit’s deployment of GenOS across Credit Karma, QuickBooks, and TurboTax further demonstrates how Generative AI is being embedded into the core of everyday financial platforms used by millions globally. |
Regulatory technology represents one of the highest-impact applications of Generative AI for financial institutions. The volume and complexity of requirements — spanning IFRS 17, IFRS 9, Basel IV, GDPR, MiFID II, and rapidly evolving ESG disclosure frameworks — make manual compliance monitoring both costly and inherently error-prone.
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“Generative AI execution in financial services now requires detailed coordination between finance, technology, legal, and compliance teams. Understanding governance requirements — from model validation to explainability and audit trails — has become a core execution risk, not a procedural formality.” Shasat Market Intelligence · May 2024
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JPMorgan Chase uses Generative AI to analyse regulatory texts across jurisdictions, map compliance obligations to internal policies, and automate compliance checks — significantly reducing the time required to assess the impact of new regulatory requirements and ensuring consistent standards across the organisation.
The shift is from reactive compliance to continuous, proactive horizon scanning. Generative AI monitors regulatory publications in real time, drafts impact assessments for human review, and maintains an up-to-date compliance picture across every relevant jurisdiction — transforming the compliance function from a cost centre into a genuine strategic asset.
Several structural forces are accelerating Generative AI adoption across financial institutions, moving it from innovation agenda to operational imperative.
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Data Volumes
The exponential growth of unstructured data — earnings calls, regulatory filings, market commentary, client communications — has created demand for AI systems capable of synthesising information at scale. |
Regulatory Complexity
The expanding body of financial regulation across IFRS, Basel, ESG, and AML frameworks creates compliance burdens that manual processes cannot efficiently absorb. Generative AI offers a scalable solution. |
Competitive Pressure
Early movers in Generative AI adoption are demonstrating measurable advantages in cost efficiency, analytical depth, and speed of decision-making — creating urgency for peers to accelerate their own programmes. |
The most important thing to understand about Generative AI in financial services is what it is not. It is not a replacement for human expertise, professional judgement, or ethical oversight. It is a tool — an extraordinarily powerful one — that expands what skilled people can achieve.
For financial institutions navigating IFRS 17, IFRS 9, Basel IV, and evolving ESG disclosure requirements simultaneously, Generative AI offers a compelling lever: the ability to dramatically expand analytical capacity without a proportional increase in headcount — freeing expert resource for the strategic work that creates real competitive advantage.
Those organisations that begin building the internal literacy, governance infrastructure, and technology foundations now will be significantly better positioned as the technology continues to mature. Those that wait risk a compounding capability gap that becomes progressively harder to close.
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Shasat Consulting — Enterprise AI Practice
Shasat’s Enterprise AI & Intelligent Automation practice helps financial institutions design compliant, auditable, and commercially impactful AI strategies — from governance framework design through to model validation and implementation. |
