15+ Generative AI Use Cases That Are Transforming Finance in 2023

Hi there! As an AI consultant specialized in the finance domain, I‘m often asked – how can banks and financial institutions leverage generative AI today? Which real-world business applications is it best suited for?

In this comprehensive guide, I‘ll walk you through 15+ use cases where generative AI can deliver immense value to financial organizations of all types and sizes.

Whether you are a banker, insurer, auditor, regulator, or investor, understanding these cutting-edge applications is key to harnessing AI‘s productivity and efficiency boosting powers.

I‘ll support each use case with real-world examples and data so you can appreciate the tangible benefits. My goal is to showcase just how profoundly these technologies can transform finance when applied correctly.

Sound exciting? Let‘s dive right in!

An Introduction to Generative AI

First, what exactly is generative AI?

Generative AI refers to a class of machine learning models that can generate brand new data based on patterns learned from existing information. The most popular form is natural language generation, where AI models can create human-like text tailored to the input prompts.

According to MIT, generative models can expand data by a phenomenal 100x factor or more. This enables limitless possibilities for creating customized, interactive content.

Generative AI represents the cutting edge of artificial intelligence today. Let‘s explore how it can shape the future of financial services.

Front Office Use Cases

The front office directly interacts with customers and drives revenue through areas like sales, trading, advisory, and product management. Let‘s see how generative AI can assist front office teams.

1. Conversational Banking Assistants

Have you ever wished for a financial assistant you could consult 24/7? One that could understand your questions, summarize complex information, and offer personalized advice instantly?

That‘s exactly what conversational AI enables.

Powered by natural language processing (NLP), conversational assistants can have natural, contextual discussions with users. Generative AI takes it further by producing nuanced responses that mimic human conversations.

HSBC‘s chatbot, Olivia, leverages generative AI to provide intuitive customer support, responding to over 100,000 requests monthly. Olivia can:

  • Answer commonly asked questions
  • Suggest suitable products
  • Connect users to live agents when needed

According to Business Insider, conversational AI can improve customer satisfaction by 20% and reduce service costs by 70%. That‘s a massive impact!

By deploying user-friendly virtual assistants, banks can provide efficient, personalized services 24/7. The outcome? Happier customers.

2. Hyper-Personalized Recommendations

Have you tried shopping online and receiving suggestions that are totally irrelevant to you? That‘s because most recommendation engines lack contextual understanding.

In contrast, generative AI grasps not just customer data but the underlying meaning. It can assess financial needs within each user‘s unique context.

For example, AWS machine learning services generate tailored recommendations on:

  • Account opening
  • Loan eligibility
  • Investible amounts
  • Insurance coverage

By serving hyper-personalized suggestions, banks can boost customer satisfaction and cross-sell rates. A study by BCG found AI-driven personalization can improve sales conversion by 10-15%.

3. Accelerated Customer Onboarding

According to PYMNTS, 60% of customers abandon online applications due to their length and complexity. Slow onboarding results in massive lost business.

With AI capabilities like optical character recognition (OCR) and natural language understanding (NLU), generative models can extract key data from applicant documents.

Chatbots can then engage users in friendly conversational flows to fill any missing details quickly. This greatly accelerates and smoothens onboarding.

UK neobank Atom automated 90% of its customer onboarding process using AI. This helped expand its customer base by 4X within months.

Through smart automation, financial institutions can dramatically reduce customer drop-offs during onboarding.

4. Sentiment Analysis for Brand Tracking

Customer sentiment plays a huge role in adoption of financial services. Analyzing emotions and feedback at scale can give banks valuable market insights.

Using NLP, generative AI is highly accurate at classifying sentiments from customer conversations, social media, surveys and more. It can detect the slightest nuances in language and their emotive implications.

JPMorgan Chase employs AI across over 100 million customer interactions to gauge satisfaction levels. The ability to constantly monitor sentiment helps them rapidly address concerns.

With granular insight into attitudes and perceptions, financial companies can refine products, defuse rumors, and strengthen their brand reputation.

5. AI-Generated Portfolio Reports

Does your advisor spend more time creating reports than analyzing your portfolio? Manual reporting is inefficient, but AI can help.

By feeding portfolio and market data into generative models, personalized reports highlighting risks, returns and trends can be auto-created in seconds.

For instance, Nomura‘s AI analyst generates equity reports in 30 seconds, encapsulating the core findings in easy-to-digest summaries.

Through automated reporting, advisors save hours of manual labor and can have more value-adding client interactions.

6. Predictive Modeling and Forecasting

Accurately predicting market movements allows banks to minimize risks and capitalize on opportunities. But uncertain variables make reliable forecasts difficult.

This is where AI simulation shines. Generative models can rapidly analyze millions of potential scenarios, by modeling combinations of signals like past data, news events, sentiments, fundamentals etc.

The output is probabilities for outcomes like default rates, equity values, GDP growth etc. so clients can make smart decisions amidst the uncertainty.

For example, JP Morgan deploys AI across asset management and trading to continuously evaluate probabilities and optimize strategies. This provides an analytical edge.

In short, generative modeling brings robust predictive capabilities for trading, lending and beyond.

Back Office Applications

Now let‘s explore how generative AI can optimize back office functions like operations, compliance, and accounting.

7. Automated Document Processing

On average, employees spend over 20% of their time on document-related tasks according to McKinsey. In finance, this includes reviewing agreements, extracting data, approving invoices and more.

With machine reading comprehension capabilities, AI systems can fully analyze documents and extract key details rapidly. This helpsauto-populate databases, route workflows and verify information.

For instance, JP Morgan uses Contract Intelligence (COiN) to review legal documents and extract important clauses, reducing review time from 360,000 hours to just 12,000 hours annually. That‘s a 97% saving!

By deploying AI for document processing, financial institutions see fewer errors, lower costs, and happier employees.

8. Fraud Detection

Financial fraud causes massive losses – over $42 billion in credit card fraud alone as per The Nilson Report. Yet most incidents go undetected until it’s too late.

Here’s where generative AI’s capabilities are counterintuitively beneficial. AI models can actually mimic and generate dummy fraudulent transactions and scenarios.

These synthesized examples, combined with actual data, can train machine learning algorithms to accurately recognize indicatory patterns and anomalies. So genuine fraud gets flagged sooner before major damage is done.

According to McKinsey, AI-driven fraud prediction is up to 8% more accurate than conventional rule-based monitoring. Those percentages translate to millions in actual savings.

9. Regulatory Technology (RegTech)

Financial institutions operate in highly regulated environments. Tracking compliance across departments is extremely challenging.

This is where RegTech powered by AI comes in. It can rapidly extract required data from documents and databases to generate compliance reports, risk disclosures and regulatory filings.

For instance, Credit Suisse uses NLP algorithms to search and structure content from 20,000 regulatory documents. This ensures accurate and timely compliance.

According to Accenture, RegTech AI can reduce compliance costs by upto 90% while lowering risks. That‘s invaluable for maintaining compliance amidst ever-evolving regulations.

10. Legacy System Maintenance

Here’s an interesting fact – 43% of banking systems are over 15 years old, built using outdated technologies like COBOL according to Accenture. Maintaining these systems is painful.

This is where the versatility of generative coding shines. AI models can be trained to write and comprehend software code in any language, even obscure ones like COBOL.

Banks can use AI to cost-effectively maintain legacy systems without expensive developer resources. AI adds agility by enabling rapid changes through auto-generated code.

According to Microsoft, AI lowers legacy system maintenance costs by upto 30%. That‘s savings banks can allocate to innovation investments.

11. Cloud Migration and Modernization

Many banks still rely on legacy on-premise infrastructure causing reliability and scaling issues. Migrating to the cloud is urgent but complex.

This typically involves re-platforming applications into cloud-compatible code like Python. AI drastically accelerates this by converting legacy codebases into modern languages that engineers can then refine.

Capital One migrated thousands of applications to the cloud using AI conversion tools. AI also continuously optimizes the cloud deployments autonomously for maximum efficiency.

Per IDC, AI boosts application migration productivity by over 50%, making cloud modernization smooth and speedy.

Common Finance Applications

Beyond the categories above, here are some common ways generative AI is transforming finance functions.

12. Accounting Process Automation

Accounting teams are mired in manual processes like generating journal entries, reconciling ledgers, rolling up P&Ls etc. These are ideal for AI automation.

Through techniques like optical character recognition (OCR) and linguistic analysis, AI accounting tools can extract salient data from documents and ledgers to auto-populate accounting systems and generate compliant financial statements.

According to a Deloitte audit of accounting AI software, accuracy levels exceeded 90% for core processes like accounts payable and receivable. Such tools reduce accounting labor needs by up to 70% as per IDG.

13. Financial Question Answering

In investment research or account management, easy access to reliable information is critical. Yet finding answers often requires searching across siloed data and tools.

AI knowledge models can rapidly synthesize details from massive financial datasets and documents to directly answer natural language queries.

For example, Goldman Sachs‘ AI platform MARIA scours multiple data sources both internally and externally to response to analyst questions posed in human readable form.

According to Juniper, question answering AI can improve business productivity by over 50% when deployed enterprise-wide.

14. Investment Research Generation

Does your investment research team spend more time formatting data than analyzing it? AI-powered automation can fix that.

By inputting data and prompts, AI algorithms can generate complete equity reports including financial models, forecast assumptions, valuations, peer comparisons, sentiment signals, risk factors and more.

BNP Paribas has used AI to reduce equity report production from 4 hours to just 30 minutes. Authors can spend the freed-up time honing investment strategies.

In effect, AI converts raw research data into actionable insights rapidly and consistently.

15. Credit Risk Modeling

Assessing borrower default likelihood is crucial for banks in lending decisions. Building credit risk models involves complex data analysis.

AI can rapidly analyze thousands of credit variables and combinations to highlight the strongest predictive signals for default. This allows development of highly accurate credit scoring models.

According to S&P Global, AI-driven credit scoring reduces losses by up to 20% compared to traditional models. More accurate risk analysis enables prudent lending.

16. Customer Churn Prediction

Losing customers to competition erodes market share and hurts financial growth. So predicting the likelihood of churn is essential.

By assessing numerous parameters related to customer loyalty like usage, engagement, support queries etc., AI models can identify high churn probability accounts months in advance, enabling proactive retention.

Capital One uses machine learning across millions of customer interactions to predict churn risk and tailor retention offers. They‘ve seen response rates to retention offers double.

Early churn warnings allow banks to be proactive in sustaining long-term relationships.

17. Trade Settlement Reconciliation

Reconciling buy and sell trade orders is a complex, error-prone manual process. It causes failed settlements and risks.

Now AI engines can automatically match cash and security legs across millions of daily transactions with over 99% accuracy according to Capgemini.

Deutsche Bank reconciles trillions in trades through AI which enhances efficiency, controls risks and prevents settlement failures before they occur.

18. Generating Synthetic Data

To train robust AI models, large volumes of high-quality data are required. However, regulations like GDPR restrict use of customer data.

Generative networks can produce synthetic customer data that preserves the statistical properties of real data without exposing personal information.

JPMorgan Chase employs a specialized AI model called Coin to create privacy-preserving synthetic data for risk modeling. This unlocks access to abundant representative data.

Synthetic data is a game changer that accelerates analytics while upholding data privacy regulations.

Realizing the Benefits

We‘ve covered a variety of ways generative AI can optimize finance. But how do companies actually implement these use cases? Here are two options:

  1. Leverage pretrained models – Platforms like Anthropic’s Claude, Cohere, and Google’s Performer offer generative AI that can be readily customized. No in-house AI skills needed.
  2. Train proprietary models – For sensitive data, train customized AI models using internal documents and data. This requires data science expertise but delivers more tailored performance. FB, Tesla, NASA take this route.

Key success factors are aligning AI to clear business objectives, integrating it into workflows, and monitoring for model drift. Partnering with experienced AI vendors can smooth execution.

The time for generative AI in finance is now. With prudent adoption, financial institutions can drive unprecedented productivity, grow customer connections, and strengthen competitive positioning for long-term success.

I hope this guide provided you a helpful overview of the transformative potential of generative AI in finance. Please feel free to get in touch if you need any guidance on leveraging these technologies within your organization. I‘m always glad to help fellow finance professionals navigate the AI world!

Similar Posts