Generative AI in Finance: Transforming Services, Accelerating Decisions, and Enhancing Trust
How GenAI and LLMs are pushing the next wave of transformation in finance — from contextual reasoning and natural-language interaction to document intelligence, fraud prevention, and legacy modernization.
Download the full guideFinance at a Crossroads
The finance industry is undergoing a structural shift. Traditional AI/ML and data analytics have already delivered productivity gains, but recent innovations in generative AI (GenAI) and large language models (LLMs) are pushing the next wave of transformation.
GenAI enables contextual reasoning, semantic search, natural language generation, and data synthesis — transforming finance into a more intelligent, scalable, and resilient industry. JPMorgan Chase, Wells Fargo, Goldman Sachs, and Citi are deploying solutions across customer interaction, document processing, report generation, coding, fraud detection, and advisory services.
McKinsey estimates the impact of generative AI on banking alone could reach up to $340 billion annually.
Challenges Driving GenAI Transformation in Finance
| Challenge | Pressure Point | How GenAI Helps |
|---|---|---|
| Operational Inefficiency | Teams spend more time gathering data than making decisions. Manual KYC, loan reviews, and compliance tasks create bottlenecks. | Automates document parsing, data extraction, and report generation to reduce workload and turnaround time. |
| Regulatory Complexity | Firms must demonstrate control, traceability, and auditability across evolving frameworks like CCAR, Basel III, ESG, etc. | Drafts filings, summarizes policy changes, and generates audit trails to help scale faster, more consistent compliance. |
| Fraud Sophistication | Real-time transactions and synthetic identities overwhelm traditional fraud detection systems. SOC teams struggle with false positives. | Augments fraud detection models, simulates fraud patterns, and drafts SARs, reducing false alerts and response time. |
| Legacy Infrastructure | Aging systems are hard to maintain and slow to adapt. Accumulation of tech debt limits scalability and innovation. | Assists in handling and translating complex legacy code, automating documentation, and reducing time-to-modernization. |
| Customer Expectations | Users expect personalized, instant support 24/7. Traditional service models cannot scale effectively and are not accurate enough. | Powers natural-language chatbots and personalized recommendations for high-quality interactions in real time. |
Teams spend more time gathering data than making decisions. Manual KYC, loan reviews, and compliance tasks create bottlenecks.
Automates document parsing, data extraction, and report generation to reduce workload and turnaround time.
Firms must demonstrate control, traceability, and auditability across evolving frameworks like CCAR, Basel III, ESG, etc.
Drafts filings, summarizes policy changes, and generates audit trails to help scale faster, more consistent compliance.
Real-time transactions and synthetic identities overwhelm traditional fraud detection systems. SOC teams struggle with false positives.
Augments fraud detection models, simulates fraud patterns, and drafts SARs, reducing false alerts and response time.
Aging systems are hard to maintain and slow to adapt. Accumulation of tech debt limits scalability and innovation.
Assists in handling and translating complex legacy code, automating documentation, and reducing time-to-modernization.
Users expect personalized, instant support 24/7. Traditional service models cannot scale effectively and are not accurate enough.
Powers natural-language chatbots and personalized recommendations for high-quality interactions in real time.
Sector-Specific Pressures
Margin compression from digital-first banks and fintechs; consumer demand for 24/7 digital services; complex, cost-heavy risk and compliance obligations.
Increasing data complexity in market research and deal due diligence; need for faster AI-powered M&A advisory; pressure to automate research workflows.
Demand for personalized portfolio building at scale; high operational cost of onboarding and compliance; rising expectations for real-time planning tools.
Resource-heavy manual underwriting and claims; gaps in real-time risk analysis; need for tailored policy generation.
Growing real-time transaction volumes and fraud exposure; need to differentiate through GenAI interfaces; API-first architecture requirements.
Manual workflows slow audits; expanding regulatory demands; siloed systems limit scalability.
Strategic Advantage of Generative AI
LLMs can ingest and process complex financial texts (like 10-Ks or regulations) to answer questions in natural language.
Advisors, clients, and compliance teams can engage with GenAI assistants as with intelligent conversational interfaces.
From raw data to narrative summaries, GenAI can generate a range of reports, risk memos, and diverse responses.
Trained or fine-tuned models work better across risk, operations, IT, customer support, finance, etc.
Major Use Cases of Generative AI in Finance
Replace rule-based chatbots with GenAI & LLM-powered conversational interfaces that understand and respond to nuanced customer inquiries in natural language. Such GenAI solutions can also help in knowledge work — from retrieving, processing, and organizing data and information from knowledge bases, to suggesting personalized recommendations for finance products.
- 30-50% increase in self-service and issue resolution
- 20-40% time savings for advisors and knowledge workers
- Enhanced client and employee satisfaction and retention due to user-friendly processes
Wells Fargo's 'Fargo' managed over 245M secure interactions in 2024, giving clients instant personalized answers while ensuring data privacy through in-house LLM orchestration.
Automate the processing, summarization, and generation of diverse regulatory filings, KYC reports, loan memos, or 10-K commentaries using custom GenAI & LLMs trained on internal and external documentation, in-house operational standards, and industry regulations.
- 60% reduction in compliance documentation prep time
- Greater accuracy and consistency across submissions
- Faster RFI and regulatory response turnaround
Morgan Stanley's 'AskResearchGPT' was deployed to help financial advisors query over 100,000 internal research reports and get compliant, client-ready summaries in seconds.
Streamline the analysis and synthesis of realistic fraud scenarios using GenAI and custom large language models. Train advanced ML detectors with synthetic data, analyze transactions for unusual narratives, and automatically generate SAR drafts for review.
- Up to 2× faster detection of compromised accounts
- 20-30% reduction in false positives
- More efficient investigation and automated reporting
Mastercard uses custom GenAI solutions to analyze transaction data at scale, doubling the speed of fraud detection while cutting false alerts by 200%.
Draft plausible market downturn or credit-risk scenarios, to feed them into stress testing and capital planning frameworks. Streamline the preparation of regulatory documentation, at scale.
- Expanded scenario library and faster model testing
- 30-40% faster CCAR/ICAAP reporting
- Improved narrative quality and audit trail
A US Regional Bank leverages GenAI to pre-draft CCAR narratives based on structured model output, saving over 1,000 analyst hours per cycle.
Leverage GenAI tools to translate COBOL, Fortran, Assembly, RPG, VB6 or PL/I into modern languages, scale unit test generation, and create documentation using code-aware LLMs.
- 40-60% faster project modernization
- Lower reliance on third-party vendors
- Reduced tech debt and mainframe costs
Goldman Sachs reports that GenAI & LLM-powered tools now assist in writing about 40% of their code for legacy application refactoring projects.
Emerging Application Areas
GenAI and domain-specific LLMs outperform traditional AI/ML in sentiment analysis, question answering, and entity recognition for capital markets.
GenAI can generate various assets, including board reports, investor memos, and ESG compliance summaries, to free up FP&A teams.
GenAI can help track changing rules, summarize and share new guidance with in-house teams, and assess policy impact automatically.
GenAI, in combination with traditional AI/ML, can help reconcile invoices, predict liquidity needs, and automate generation of SWIFT messages.
Domain LLMs (e.g. BloombergGPT) can mine news, transcripts, and alternative data, helping to find trade signals and generate trading ideas.
GenAI investment in finance is accelerating
Strategic Recommendations for Financial Executives
Prioritize high-impact use cases that address cost, compliance, or client / employee satisfaction with measurable KPIs.
Implement model inventories, bias detection, human oversight (HITL), and regulatory alignment from day one (as part of AI Center of Excellence).
Customize GenAI models and LLMs using financial documents, customer dialogues, compliance records, etc. for relevance, consistency, and accuracy.
Figure out ways of reinventing legacy for adopting GenAI. Leverage secure cloud platforms and data pipelines as prerequisites for scaling GenAI.
From data scientists to compliance officers, train staff in prompt engineering, model output validation, and other GenAI best practices.
Run 8-12 week prototypes in high-value domains (compliance, document processing, customer service), validate ROI, and scale across business units.
Convex
Convex is a global specialty insurance company operating across Europe, the UK, and the US.
Convex' underwriters review hundreds of pages of complex risk reports under tight broker deadlines, a process that is manual and time-consuming, and limits the number of risks assessed and decisions made each day.
By implementing generative AI, Convex transformed its underwriting workflow. The GenAI solution, AI Underwriter, automatically summarizes risk reports, extracting key details, sentiments, and metrics in minutes. Underwriters now spend less time reviewing documents and more time making decisions. GenAI enabled Convex to scale underwriting capacity, improve decision speed, and support its growth in premium segments without compromising accuracy.
- Reduced the time needed to process a 100-page report to about ten minutes.
- Enabled underwriters to analyze 100× more risk reports, leading to better decisions and insights.
- Established new decision standards by digitizing expert knowledge, to accelerate growth.
How to Get Started with Generative AI
Provectus offers a clear, proven path forward with a structured program available through AWS Marketplace. Our subject matter experts and technical team guide your organization through every step.
Assess your GenAI readiness across data, talent, compliance, and workflows. Identify use cases that align with your goals and pain points.
Build and test a GenAI solution in areas like customer service, document intelligence, or compliance. Measure impact, refine, and prepare for scale.