Generative AI in Finance:
Transforming Services, Accelerating
Decisions, and Enhancing Trust
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. Beyond task automation, GenAI enables contextual reasoning, semantic search, natural language generation, and data synthesis – all of which are capabilities that are essential for transforming finance into a more intelligent, scalable, and resilient industry.
Industry leaders like JPMorgan Chase, Wells Fargo, Goldman Sachs, and Citi are already deploying GenAI solutions for 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.
This guide explores how financial organizations can translate the potential of generative AI and LLMs into real, measurable business impact across operations, compliance, risk, and client experience.
The finance industry is extremely broad and diverse, and under constant pressure to innovate. Finance leaders have to balance efficiency, compliance, and growth, while operating in a highly regulated environment. Many core processes remain manual and fragmented, or reliant on legacy systems, making it difficult to meet rising expectations from customers, regulators, and internal stakeholders. Generative AI presents an opportunity to rethink how work gets done across the enterprise.
Below are the key challenges driving urgency for transformation, and why GenAI is becoming part of the solution.
While many of the finance sub-sectors share the same structural issues, some of them face their own challenges.
- Margin compression from digital-first banks and fintechs
- Consumer demand for 24/7 digital services, end-to-end interaction
- 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 and structuring
- Pressure to automate and augment research workflows
- Demand for personalized portfolio building at scale
- High operational cost of client onboarding, compliance, and reporting
- Rising expectations for real-time financial planning and reporting tools
- Resource-heavy manual underwriting and claims processing
- Gaps in real-time risk analysis and regulatory documentation
- Need for tailored policy generation and customer engagement
- Growing real-time transaction volumes and fraud exposure
- Need to differentiate by adopting GenAI intelligent customer interfaces
- Need for API-first architectures that enable faster GenAI adoption
- Manual workflows slow audits and increase compliance risk
- Expanding regulatory demands require more transparent reporting
- Siloed systems and limited resources make it hard to scale
Generative AI offers tools that can help adapt to the realities of each sub-sector, enabling organizations to find the balance between growth and profitability.
Generative AI goes further than traditional AI/ML, structured data analysis, and rule-based tasks automation through RPA. It provides:
Unlike traditional models that focus on narrow predictions or classifications, generative AI can power intelligent conversational interfaces, generate compliant reports, assist with coding legacy systems, or detect fraud patterns in ways that were previously out of reach. This makes it a perfect technology for tackling many of finance’s core use cases, from manual document work and client engagement, to compliance management and risk analysis.
Morgan Stanley rolled out a generative AI assistant to enable its wealth advisors to ask complex questions and get instant answers from over 100,000 internal research reports. The tool can also draft meeting notes, identify action items, and prepare follow-up emails after client meetings.
By integrating GenAI into daily workflows, Morgan Stanley augmented its employees, improving productivity and delivering fast, personalized service to high-net-worth clients.
Generative AI is already being put to work by leading finance organizations, with practical use cases that are delivering real results across the industry.
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.
Business Impact
- 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
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.
Business Impact
- 60% reduction in compliance documentation prep time
- Greater accuracy and consistency across submissions
- Faster RFI and regulatory response turnaround
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.
Business Impact
- Up to 2x faster detection of compromised accounts
- 20-30% reduction in false positives
- More efficient investigation and automated reporting
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.
Business Impact
- Expanded scenario library and faster model testing
- 30-40% faster CCAR/ICAAP reporting
- Improved narrative quality and audit trail
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.
Business Impact
- 40-60% faster project modernization
- Lower reliance on third-party vendors
- Reduced tech debt and mainframe costs
Generative AI is finding new roles across the finance industry, with fresh applications emerging alongside growing investment and adoption.
These emerging applications show how versatile generative AI can be across the finance industry. From trading desks to compliance teams, organizations are finding new ways to apply GenAI & LLMs to everyday work. They reflect a broader shift in how the industry is investing in AI to stay ready for the future.
Generative AI spending by financial organizations is projected to exceed $85B by 2030, up from about $5B in 2023.
#1
Prioritize high-impact use cases that address cost, compliance, or client / employee satisfaction with measurable KPIs.
#1
Prioritize high-impact use cases that address cost, compliance, or client / employee satisfaction with measurable KPIs.
#2
Implement model inventories, bias detection, human oversight (HITL), and regulatory alignment from day one (as part of AI Center of Excellence).
#2
Implement model inventories, bias detection, human oversight (HITL), and regulatory alignment from day one (as part of AI Center of Excellence).
#3
Customize GenAI models and LLMs using financial documents, customer dialogues, compliance records, etc. for relevance, consistency, and accuracy.
#3
Customize GenAI models and LLMs using financial documents, customer dialogues, compliance records, etc. for relevance, consistency, and accuracy.
#4
Figure out ways of reinventing legacy for adopting GenAI. Leverage secure cloud platforms and data pipelines as prerequisites for scaling GenAI.
#4
Figure out ways of reinventing legacy for adopting GenAI. Leverage secure cloud platforms and data pipelines as prerequisites for scaling GenAI.
#5
From data scientists to compliance officers, train staff in prompt engineering, model output validation, and other GenAI best practices.
#5
From data scientists to compliance officers, train staff in prompt engineering, model output validation, and other GenAI best practices.
#6
Run 8-12 week prototypes in high-value domains (compliance, document processing, customer service), validate ROI, and scale across business units.
#6
Run 8-12 week prototypes in high-value domains (compliance, document processing, customer service), validate ROI, and scale across business units.
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 100x more risk reports, leading to better decisions and insights.
Established new decision standards by digitizing expert knowledge, to accelerate growth.
Adopting GenAI is a strategic journey. Provectus supports financial organizations through every phase of adoption, from initial use case discovery to solution deployment at the enterprise scale.
