12 interesting use cases on how AI is changing the Finance world

What if Artificial intelligence (AI) could predict the outcome of a financial decision before you made it?

Suppose you’re filing an insurance claim after an unfortunate accident. The slow process leaves you waiting weeks without clarity on the outcome or compensation. However, an AI system can process your claim in minutes and predict potential costs. 

This is not futuristic. 

Gradient AI is already using AI to reshape insurance operations. The company leverages AI-powered underwriting and claims management capabilities to assess the likelihood of a bodily injury claim exceeding specific cost thresholds and flags high-risk cases for immediate review.

The shift towards AI in finance is undeniable. As you read this, financial firms are rewriting their playbooks with AI at the center of decision-making. 

• According to Gartner, AI adoption in finance departments surged to 58% in 2024, up from 37% in 2023
• Over two-thirds of financial leaders now view emerging technologies like AI as critical to the industry’s future

This article will examine how AI has reshaped the financial services landscape with 12 impactful innovations.

Table of Contents

• 12 Real-world Use Cases of AI in Finance
  • 1. Credit scoring and risk assessment
  • 2. Chatbots for 24/7 assistance
  • 3. Fraud detection and prevention
  • 4. Predictive analytics for market trends
  • 5. AI tools for personalized customer service insights
  • 6. AI-driven dynamic pricing in financial services
  • 7. Multilingual support
  • 8. Automating regulatory compliance
  • 9. Algorithmic trading
  • 10. Enhanced dispute resolution
  • 11. Proactive customer education through AI alerts
  • 12. Personalized financial advice
• Striking a balance between risk and innovation in AI for financial services
• Take the first step toward adopting AI with Hiver

12 Real-world Use Cases of AI in Finance

Dr. Manuela Veloso, Head of AI Research at JPMorgan Chase, discussed the rise of AI in finance and the impact of robotics advancements in Deloitte’s AI Ignition podcast.

She pointed out, “The challenge is to think ofAI not as a one-shot kind ofsystem but as a road … AI is like a journey, which becomes better over time.”

From automating trading strategies to providing personalized customer support services such as real-time credit approvals—let’s understand how AI is dismantling legacy models and paving the way for innovation in the financial services sector.

1. Credit scoring and risk assessment

AI is increasingly shaping the future of credit assessments by processing large datasets to build more accurate risk profiles. It goes beyond traditional credit scores, analyzing behaviors, spending patterns, and other critical factors. As reported, 54% of financial institutions planning to invest in AI lending models expect to spend at least $100,000 on AI over the next three years.

A case in point is Zest AI, which enables lenders to evaluate borrowers with limited or no credit history, ensuring more inclusive access to credit.

Zest AI technology drives lending automation with:

• AI-automated underwriting: Quick, precise credit decisions ensuring fairness for every applicant
• Fraud detection: End-to-end protection for portfolios against fraud
• Lending intelligence: Key insights to refine strategies and improve early screening

Lenders using Zest-built models have seen a 20% increase in approval rates with no added risk and up to 50% reductions in charge-offs.

2. Chatbots for 24/7 assistance

AI-powered chatbots provide round-the-clock customer support, handling queries, guiding users through transactions, and resolving issues without human intervention.

For example, Bank of America’s virtual assistant Erica, simplifies financial management for users by:

• Alerting them about duplicate charges
• Notifying users when merchant refunds are processed
• Allowing them to view and redeem rewards from a single location

Erica recently reached 2 million interactions per day. The AI virtual assistant provides fast responses, averaging 44 seconds to address client inquiries, with over 98% of cases resolved promptly. 

3. Fraud detection and prevention

Machine learning algorithms help monitor real-time transactions to detect unusual patterns like multiple failed login attempts or large, sudden withdrawals. Mastercard, for instance, uses AI to flag suspicious activity and alert customers instantly about such anomalies, reducing the risk of financial loss.

Using generative AI-based predictive technology, Mastercard has been able to:

• Deliver a 2x improvement in detecting compromised payment cards
• Achieve a 200% decrease in false positive fraud cases
• Boost the speed of merchant risk identification by 300%

4. Predictive analytics for market trends

As this Reddit user observed, much of the finance world operates on “half-broken Excel spreadsheets”. AI helps improve such workflows by consolidating data across departments, making it accessible and actionable.

Using historical data, AI can predict market trends and help firms make timely, informed investment decisions. Hedge funds like Renaissance Technologies apply machine learning to analyze vast financial datasets, identifying hidden patterns that consistently deliver returns.

Renaissance Technologies employs these insights to predict stock movements and other market trends. These predictive analytics help identify opportunities and mitigate risks, giving firms an edge in volatile markets.

5. AI tools for personalized customer service insights

For years, financial companies have been monitoring customer interactions, social media mentions, and reviews to pull up key insights into customer satisfaction and other areas for improvement.

Now, AI can do this a lot faster and at scale.

Wells Fargo uses AI to recommend products and services suited to individual customer needs. Here’s how:

• The company employs Dialog Flow, Google’s conversational AI, to interpret customer input, comparing it with data from past interactions
• Wells Fargo’s Customer Engagement Engine equips bankers with insights on the most relevant goals and discussions for each customer, enabling them to offer more targeted advice and services

6. AI-driven dynamic pricing in financial services

AI is transforming pricing strategies in financial services, replacing traditional static models with dynamic systems that adapt in real time based on data.

Financial institutions can now optimize revenue and manage risk more effectively, with AI-driven platforms offering personalized pricing tailored to each customer.

Marcus by Goldman Sachs exemplifies AI-driven dynamic pricing. The platform’s algorithms create personalized loan rates for its customers. 

Here’s how it works:

• AI evaluates individual customer profiles, including credit scores, income, and spending behavior, to offer competitive rates
• The system continually adapts to market changes, optimizing rates to balance customer acquisition with risk management

7. Multilingual support

When banks serve diverse customer groups spanning multiple countries, how can they ensure effective communication? The most obvious strategy is to communicate with customers in their local language. But a few years back, localizing customer support meant heavy investments, as you had to hire local people. That’s now changed with AI.

Take Standard Chartered as an example. They launched an AI chatbot called Stacy to help customers in Hong Kong get assistance in either English or Chinese. This is a very thoughtful move because it doesn’t alienate locals in Hong Kong who don’t understand English. Stacy enables customers to:

• Check account balances, transaction history, and payment details
• View credit card reward points and related benefits
• Leave messages during off-hours and get notified when replies are available
• Retrieve past conversations for reference whenever needed

8. Automating regulatory compliance

Thanks to AI, compliance is less of a hassle—spotting issues in real-time and analyzing rules gets done faster. Unlike manual systems, which can take days or weeks to do this and are also prone to human error, AI offers high accuracy and reduced operational costs through automation.

HSBC developed the AIGT index using AI capabilities from AWS, integrating artificial intelligence into its investment processes. 

HSBC also uses AI to review large volumes of transactions daily, identifying patterns that signal potential Anti-Money Laundering (AML) activities. This accelerates the review process while reducing the likelihood of regulatory violations.

9. Algorithmic trading

AI analyzes real-time market data to execute trades faster and more accurately than human traders, giving firms a competitive edge. Financial institutions, including hedge funds, depend on AI algorithms to spot opportunities and manage risks.

BlackRock’s Aladdin system uses AI to assess risks and optimize investments. This technology cuts costs related to trading errors while improving decision-making and profitability.

Aladdin AI copilots are improving portfolio solutions by:

-Providing immediate answers and enabling users to become experts in using Aladdin
-Unlocking new efficiencies by allowing customization to suit user needs
-Prioritizing privacy by providing permission-based access to critical insight

10. Enhanced dispute resolution

Traditional methods of resolving transaction disputes often take weeks and are resource-intensive. In fact, The Payments Report by Auremmia Research found that: 

• 35% of surveyed cardholders mistake a legitimate transaction as a fraudulent one
• 70% cardholders have disputed a charge assume unrecognized transactions are fraudulent
• 41% of debit cardholders who disputed a purchase say they are less likely to use the card again 

Keeping these factors in mind, Visa has taken a significant step forward with its global dispute processing platform—Visa Resolve Online (VROL),adding two new AI-powered features. These enhancements aim to reduce the resolution time from weeks to days while maintaining impartiality in decision-making.

Visa Resolve Online offers:

-Comprehensive data analysis to support dispute resolution
-Real-time detection and reporting of fraudulent activities
-Handling of flagged cases for quick attention
-Automated submission and management of dispute cases
-Continuous tracking and reporting for greater transparency

11. Proactive customer education through AI alerts

Your spending habits tell a story, and Monzo, a UK digital bank, uses AI to turn that story into proactive, helpful alerts.

Here’s how Monzo’s AI features help users:

• Sends alerts when it detects unusual spending patterns
• Recommends ways to manage budgets, like limiting non essential expenses
• Helps users maintain financial discipline by providing timely, data-driven suggestions

TS Anil, Monzo’s CEO, highlights how AI plays a key role in improving financial literacy:

“The power of GenAI to help customers get better educated is extraordinary. Because you can customise it in the context of the customer and you can deliver it in a way that the customer can process it and understand it.”

12. Personalized financial advice

According to ‘The Challenge of Customer-Centric Banking’ report by Genesys, 53% of banks are making significant investments in technology to enhance personalization.

AI plays a key role in meeting these expectations by analyzing customer data and offering tailored budgeting, saving, and investing solutions. Robo-advisors like Wealthfront automate financial planning, making it more accessible for individuals with limited financial knowledge or resources.

Wealthfront’s services include:

• Customized investment portfolios with access to 17 global asset types
• Automated reinvestment and portfolio rebalancing
• Tax-loss harvesting to reduce tax liability and improve after-tax returns
• Cost-efficient investment strategies tailored to individual goals and risk levels

Striking a balance between risk and innovation in AI for financial services

While 69% of financial institutions believe AI will lead to more revenue, improving customer interactions and reducing time spent on false positives—its risks cannot be overlooked. As AI continues to shape the banking sector, it offers both innovation and challenges that need careful management.

Here are a few key points to remember:

• Transparency and accountability: AI’s complexity often leads to decision-making processes that are not easily explained. Financial institutions must prioritize explainable AI, especially in sensitive areas like credit scoring and investment decisions.
• Governance frameworks: To mitigate risks, strong AI governance is necessary. This includes clear policies, regular audits, and ensuring AI models adhere to data protection regulations. Financial firms must also stay updated with regulations to prevent compliance breaches.
• Risk management: For ensuring smooth operations, ongoing AI risk assessments, performance monitoring, and quick identification of biases or security vulnerabilities are essential.

Take the first step toward adopting AI with Hiver

As we’ve seen so far, AI has made an impressive headway into the financial sector. One of its most prominent use cases is providing round-the-clock support with intuitive chatbots and virtual assistants.

To enable this, many customer support software vendors are introducing AI-powered features—automated, context-aware responses and ticket prioritization—in their products.

One such vendor is Hiver. The company has its own AI bot called Harvey that can help summarize customer conversations into concise notes, suggest responses based on context, and auto-close tickets that don’t require any more attention – all of which enables support teams in financial organizations to focus on more complex tasks and challenges.

Think of Harvey as a sidekick that helps you optimize internal operations while you focus on building strong relationships—which is the foundation of customer loyalty and trust.