Generative AI Use Cases in Banking

Generative AI is redefining how banks operate — from automating customer service to streamlining underwriting. Below are practical examples showing how leading financial institutions are applying GenAI for real business value.

1 đź“‹ Summary of Use Cases

Use Case Area Examples Benefits Underlying Models
Fraud Detection Mastercard Decision Intelligence Pro Real-time anomaly detection, improved fraud response EBM, Graph Models, VAE
Credit Approval and Underwriting JPM COiN, Zest AI LuLu Stragey Faster decisions, consistent underwriting LLM, Tabular GANs
Customer Engagement Erica (BoA), Cora+ (NatWest), Fargo, Eliza (BNYM) Automated support, 24/7 service LLM
Personalized Insights BBVA ChatGPT, Morgan Stanley Debrief Tailored financial advice, better CX LLM (GPT-4), RAG
Generative BI Microsoft Copilot, ThoughtSpot Sage Instant dashboards, conversational BI LLM, NLQ, NLG
Document Generation SARs, compliance memos, offer letters Auto-drafted docs, reduced manual effort LLM, IDP, Diffusion
Document Summarization JPM AI Suite, Morgan Stanley AskResearchGPT Faster reading, internal reporting, decision support LLM
Synthetic Data Generation Internal testing, privacy-safe modeling Augmented training data, preserves confidentiality VAE, Diffusion, GAN

2 🔍 Detailed Use Cases

2.1 Fraud Detection

  • Problem: Detecting rare, evolving fraud patterns is difficult with traditional rule-based systems.
  • Solution: Energy-Based Models (EBMs) and VAE-based anomaly detection are used to identify outlier transactions in real time.[1]
  • Impact: +20% detection accuracy and –85% false positives (Mastercard).[2]

2.2 Credit Approval and Loan Underwriting

  • Problem: Credit underwriting requires reviewing financial statements, legal documents, and industry data under time pressure — a process that is slow, manual, and error-prone.
  • Solution: Generative AI accelerates underwriting by analyzing credit memos, extracting key risk metrics, and summarizing legal clauses, enabling faster and more consistent loan decisions.
  • Impact: JPMorgan COiN saved over 360,000 hours by automating legal reviews; Zest AI reduced underwriting time from days to minutes and increased approvals by up to 25% with no added risk.[3][4]

2.3 Customer Engagement

  • Problem: Call centers and support channels face high volumes of routine queries, long wait times, and rising operational costs.
  • Solution: Generative AI powers conversational agents that provide real-time, personalized support across multiple languages and channels, improving CX while reducing human workload.
  • Impact: Erica has handled 2B+ interactions with an average 44s resolution time; Cora+ reduced escalations by 50% and improved customer satisfaction by 150%. [5][6]

2.4 Personalized Insights

  • Problem: Financial advisors often struggle to provide timely, personalized advice due to information overload and manual follow-up tasks.
  • Solution: Generative AI automates note-taking, analyzes client profiles and market trends, and generates tailored investment recommendations.
  • Impact: Morgan Stanley’s AI @ Morgan Stanley Debrief saves ~30 minutes per meeting, freeing up 10–15 hours/week per advisor for higher-value interactions. [7][8]

2.5 Generative BI

  • Problem: Business leaders often struggle to extract timely insights from complex, multi-source data environments, relying on static dashboards or analysts.
  • Solution: Generative AI combines LLMs with backend analytical pipelines to deliver real-time, conversational insights tailored to executive needs.
  • Impact: Forrester reports a 379% ROI and 20% analyst efficiency gain from Power BI with Copilot; users save up to 2.4 hours/week through automated tasks. [9][10]

2.6 Document Generation

  • Problem: Writing compliance memos, suspicious activity reports (SARs), and internal summaries requires significant manual effort and is prone to inconsistencies.
  • Solution: Generative AI models—particularly LLMs and intelligent document processing (IDP) systems—automate document classification, data extraction, summarization, and drafting, while supporting compliance standards.
  • Impact: AI-powered IDP platforms have reduced review time by up to 90%, improved processing accuracy, and enabled faster turnaround for compliance documents. [11][12][13]

2.7 Document Summarization

  • Problem: Manual summarization of research and compliance documents is time-consuming and error-prone.
  • Solution: GenAI tools powered by LLMs extract, synthesize, and summarize large volumes of internal content at scale.
  • Impact: Morgan Stanley’s AskResearchGPT summarizes insights from 70,000+ proprietary reports; EY’s DI Platform saves 90% review time, cuts costs by 80%, and improves accuracy by 25%.[14][15][16]

2.8 Synthetic Data Generation

  • Problem: Real data can’t be shared freely due to privacy, regulatory, and operational constraints.
  • Solution: Use VAE, Diffusion, or GAN-based models to generate synthetic datasets that preserve statistical properties while protecting sensitive information.
  • Impact: JPMorgan uses synthetic data to develop and test AI models in secure environments; industry surveys highlight growing adoption of GANs and Diffusion for financial data simulation.[17][18]

3 References

[1] NVIDIA - How Is AI Used in Fraud Detection? https://blogs.nvidia.com/blog/ai-fraud-detection-rapids-triton-tensorrt-nemo/

[2] Mastercard supercharges consumer protection with Gen AI. https://www.mastercard.com/news/press/2024/february/mastercard-supercharges-consumer-protection-with-gen-ai/

[3] Zest AI Launches LuLu Strategy Module to Expand Generative AI to Financial Institutions https://www.zest.ai/company/announcements/zest-ai-launches-lulu-strategy-module-to-expand-generative-ai-to-financial-institutions/

[4] Bloomberg. (2017). JPMorgan Software Does in Seconds What Took Lawyers 360,000 Hours. https://www.bloomberg.com/news/articles/2017-02-28/jpmorgan-marshals-an-army-of-developers-to-automate-high-finance?embedded-checkout=true

[5] Erica at Bank of America https://info.bankofamerica.com/en/digital-banking/erica

[6] NatWest Cora+ https://www.natwestgroup.com/news-and-insights/news-room/press-releases/data-and-technology/2024/jun/natwest-launches-cora-plus-the-latest-generative-ai-upgrade-to-t.html

[7] Morgan Stanley. (2024). AI @ Morgan Stanley Debrief Launch. https://www.morganstanley.com/press-releases/ai-at-morgan-stanley-debrief-launch

[8] The Wall Street Journal. (2024). Inside OpenAI’s Deal With BBVA. https://www.wsj.com/articles/six-months-thousands-of-gpts-and-some-big-unknowns-inside-openais-deal-with-bbva-5d6f1c03

[9] Microsoft. (2024). Forrester Total Economic Impact™ study: Microsoft Fabric delivers 379% ROI over three years. https://www.microsoft.com/en-us/microsoft-fabric/blog/2024/06/03/forrester-total-economic-impact-study-microsoft-fabric-delivers-379-roi-over-three-years/

[10] Forrester. (2024). New Technology: The Projected Total Economic Impact™ Of Microsoft Copilot For Microsoft 365. https://tei.forrester.com/go/Microsoft/365Copilot/?lang=en-us

[11] U.S. Department of the Treasury. (2024). Artificial Intelligence in Financial Services. https://home.treasury.gov/system/files/136/Artificial-Intelligence-in-Financial-Services.pdf

[12] EY. (2024). Document Intelligence Platform. https://www.ey.com/en_gl/alliances/microsoft/document-intelligence-platform

[13] McKinsey & Company. (2024). How Generative AI Can Help Banks Manage Risk and Compliance. https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/how-generative-ai-can-help-banks-manage-risk-and-compliance

[14] Morgan Stanley. (2024). AskResearchGPT helps advisors access 70,000+ research reports. https://www.morganstanley.com/press-releases/morgan-stanley-research-announces-askresearchgpt

[15] JPMorgan Chase. (2024). LLM Suite for operations and reporting automation. https://www.cnbc.com/2024/08/09/jpmorgan-chase-ai-artificial-intelligence-assistant-chatgpt-openai.html

[16] EY. (2024). Document Intelligence Platform: Transforming document processing in financial services. https://www.ey.com/en_gl/alliances/microsoft/document-intelligence-platform

[17] J.P. Morgan. (2024). Synthetic data enables privacy-preserving model development. https://www.jpmorgan.com/technology/artificial-intelligence/initiatives/synthetic-data

[18] X. Wang et al. (2024). A Survey on Financial Synthetic Data Generation with GANs and Diffusion. https://arxiv.org/pdf/2410.18897v1

4 Further Reading

[1] Boston Consulting Group (BCG). (2023). A Generative AI Roadmap for Financial Institutions. https://www.bcg.com/publications/2023/a-genai-roadmap-for-fis

[2] McKinsey & Company. (2023). How generative AI can help banks manage risk and compliance. https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/how-generative-ai-can-help-banks-manage-risk-and-compliance

[3] McKinsey & Company. (2023). Capturing the full value of generative AI in banking. https://www.mckinsey.com/industries/financial-services/our-insights/capturing-the-full-value-of-generative-ai-in-banking

[4] Gartner. (2023). Emerging Tech Impact Radar: Artificial Intelligence in Banking. https://www.gartner.com/en/documents/4558699

[5] CTO Magazine. (2023). JPMorgan Chase Accelerates AI Adoption. https://ctomagazine.com/jp-morgan-chase-accelerates-ai-adoption/

[6] Bain & Company. (2023). Generative AI in Banking – Interactive. https://www.bain.com/insights/generative-ai-banking-interactive/

[7] PwC Middle East. (2023). Leveraging Generative AI in Banking. https://www.pwc.com/m1/en/publications/leveraging-generative-ai-in-banking.html

[8] KPMG. (2023). Unleashing Potential: Exploring Generative AI’s Role in Banking. https://kpmg.com/xx/en/our-insights/ai-and-technology/unleashing-potential-exploring-generative-ai-role-in-banking.html

[9] Mastercard. (2023). Signals: Generative AI is Transforming Banking. https://innovationinsights.mastercard.com/signals-generative-ai-transforming-banking/p/1

[10] Mastercard. (2023). Generative Banking: How Financial Institutions Are Embracing the New AI. https://newsroom.mastercard.com/news/perspectives/2023/generative-banking-how-financial-institutions-are-embracing-the-new-ai/

[11] Accenture. (2023). Generative AI in Banking. https://www.accenture.com/us-en/insights/banking/generative-ai-banking

[12] Accenture. (2023). 3 Ways Generative AI Will Transform Banking. https://bankingblog.accenture.com/3-ways-generative-ai-will-transform-banking

[13] Deloitte. (2023). Generative AI in Financial Services: Google & Deloitte Alliance. https://www.deloitte.com/global/en/alliances/google/blogs/generative-ai-in-financial-services.html

[14] KPMG US. (2024). How Generative AI Can Help Banks Accelerate Digital Transformation. https://kpmg.com/kpmg-us/content/dam/kpmg/pdf/2024/generative-ai-help-bank-accelerate-digital-transformation.pdf

[15] Bain & Company. (2023). How Bank CIOs Can Build a Solid Foundation for Generative AI. https://www.bain.com/insights/how-bank-cios-can-build-a-solid-foundation-for-generative-ai/

[16] PwC US. (2023). Generative AI in Financial Services – Salesforce Partnership. https://www.pwc.com/us/en/technology/alliances/library/salesforce-generative-ai-banking-financial-services.html

[17] IBM. (2023). Generative AI in Banking. https://www.ibm.com/think/topics/generative-ai-banking