15+ AI Applications, Use Cases & Examples in Finance (2024)

Artificial intelligence (AI) is transforming the finance industry by automating processes, extracting insights from data, and enhancing decision making. According to a 2020 Business Insider report, 75% of respondents at banks with over $100 billion in assets are implementing AI technologies.[1] McKinsey estimates that banking and financial services companies can generate over $250 billion in value by applying AI across their organizations.[2]

While AI adoption in finance still faces challenges like historical bias in data and model explainability, the potential benefits far outweigh the costs. AI can help financial institutions operate more efficiently, reduce costs and errors, and provide better services to customers.

To understand where AI can have the most impact, it‘s important to analyze key processes and identify automation opportunities. Process mining techniques can uncover bottlenecks and pain points in financial workflows that are ripe for AI automation.[3] I have over 10 years of experience in data extraction and understand the vital role it plays in feeding data to AI systems in finance.

Here are 15+ real-world examples of AI transforming finance across lending, investments, operations, insurance, compliance, customer service and more.


Lending is one area seeing rapid AI adoption. A 2020 study found 87% of lenders were piloting or moving AI solutions to production. [4] AI is applied across both retail and commercial lending to accelerate underwriting, improve risk analysis, and provide a better customer experience.

[h3]Retail Lending Operations[/h3] AI-powered document capture can extract data from credit applications and associated financial documents like pay stubs and bank statements. This automates the applicant evaluation process so lenders can assess retail lending deals faster with less manual work.

One auto lender saw a 5x increase in loan applications processed per day after implementing an intelligent document processing solution.[5] [h3]Commercial Lending Operations[/h3] Lenders use AI to analyze financial documents from business loan applicants including annual reports, tax returns and cash flow statements. Extracting insights from this unstructured data leads to more accurate credit risk assessments.

An AI solution from Moody’s analyzed commercial loan documents in seconds compared to hours for humans. This accelerated underwriting for a large bank by 70%.[6]

[h3]Retail Credit Scoring[/h3] AI credit scoring models evaluate applicants faster and more accurately than traditional methods. Credit card issuer Discover saw a 10x increase in speed and better visibility into borrower behaviors by applying AI for credit decisioning.[7]

Retail credit scoring models consider thousands of data points across identity, income, assets, payment history and other alternative data. AI allows more adaptive risk modeling.

[h3]Commercial Credit Scoring[/h3] By rapidly analyzing financial documents, news, and qualitative data, AI systems can generate credit risk scores and insights to support commercial lending decisions. This leads to reduced costs and better outcomes.

One model analyzes over 2 million data points to predict probability of default and loss given default on commercial loans.[8] [h2]Investments[/h2] [h3]Robo-Advisory[/h3] Robo-advisors powered by AI provide personalized investment recommendations and portfolio management for retail investors. They analyze a client‘s risk appetite and other data to generate suitable portfolios across stocks, bonds and other assets.

Robo-advisors like Betterment and Wealthfront manage over $330 billion in assets and are expected to reach $5 trillion by 2025.[9] Their algorithms can optimize portfolios in real-time based on market changes.

[h2]Operations[/h2] [h3]Debt Collection[/h3] The most common consumer complaint in debt collection is continued attempts to collect non-owed debt.[10] AI helps collectors comply with regulations by determining which accounts to pursue and when to stop.

One bank saw a 76% decrease in delinquency rates by optimizing collection strategies with AI.[11] Models incorporate Bureau data, payment history, demographics, channel preferences and other insights to engage customers.

[h3]Procure-to-Pay[/h3] AI invoice capture and payment reminder systems accelerate procure-to-pay cycles in commercial banking. This reduces costs and errors while improving cash flow and invoice recovery rates.

According to an Ardent Partners study, over 70% of businesses saw increases in on-time payments after implementing AI for collections. [12] [h3]Account Reconciliation[/h3] AI extracts data from bank statements and complex account spreadsheets for automated reconciliation. This eliminates manual processes and improves accuracy.

The time taken for account reconciliation at large banks has been reduced from weeks to days using AI tools from vendors like HighRadius.[13] [h2]Insurance[/h2] [h3]Insurance Pricing[/h3] AI evaluates a customer‘s unique risk profile to price policies competitively while still maintaining profitability. This leads to improved customer satisfaction and retention.

Geico is one of many insurers leveraging AI to segment customers and optimize pricing strategies. They have seen increased clickthrough and conversion rates after personalizing premium costs based on behavior.[14] [h3]Claims Processing[/h3] AI expedites claims by automatically validating documents, identifying fraud, estimating repair costs through image recognition and more. One vendor claims AI can accelerate claims by 10x.[15]

USAA saw a 30% improvement in customer satisfaction by using AI to settle claims in seconds rather than weeks.[16] AI is transforming claims through automation across insurers.

[h2]Audit & Compliance[/h2] [h3]Fraud Detection[/h3] Banks lose billions from fraud each year, with less than 25% of funds recovered in most cases.[17] By detecting anomalies and suspicious activities, AI helps financial institutions mitigate fraud and comply with regulations.

One bank was able to reduce false positives in fraud alerts by 50% using an AI solution, saving thousands of hours of manual review.[18] [h3]Regulatory Compliance[/h3] AI tools can rapidly scan troves of legal and regulatory documents to check policies for adherence. This saves compliance teams significant time and money.

For instance, an NLP tool from Credit Suisse analyzes 22,000 new regulations annually to check adherence, delivering $50-100 million in annual savings.[19] [h3]Travel & Expense Management[/h3] AI validates expense reports and detects non-compliant spend. This prevents policy violations and reduces the effort required for report approvals.

By combining computer vision and NLP, organizations have cut T&E review times by over 75% while improving spend policy compliance.[20] [h2]Customer Service[/h2] [h3]KYC Processes[/h3] AI streamlines know-your-customer (KYC) processes by validating identities and finding risks in client data. This improves efficiency, reduces mistakes and enhances compliance.

JPMorgan Chase decreased KYC review times from months to hours by using AI for document analysis and validation.[21] [h3]Responding to Customer Requests [/h3] Chatbots and virtual assistants use NLP to address common customer inquiries instantly. This reduces call volumes so agents can handle more complex issues.

Capital One‘s Eno chatbot handles over 12 million customer requests per month, allowing humans to focus on high-value activities.[22] [h3]Identification of Upsell & Cross-sell Opportunities[/h3] By analyzing customer data, AI helps financial institutions uncover unmet needs and offer relevant products to increase satisfaction and revenue.

One global bank increased annual revenue generated from email cross-selling by 4x using AI-based next-best-action recommendations.[23] [h3]Customer Churn Prediction[/h3] AI analyzes past churn patterns to identify at-risk customers early. Banks can then take proactive measures to retain customers and prevent attrition.

Chase was able to double its accuracy in predicting mortgage churn using AI, allowing them to save millions in acquisition costs.[24] [h2]Other Use Cases[/h2]

Beyond the examples above, AI is transforming other areas of financial services:

  • Algorithmic trading based on pattern recognition and predictive models
  • Recommending investment strategies and optimizing portfolios
  • Automating middle and back office functions such as settlement
  • Detecting credit card fraud during transactions
  • Analyzing earnings calls and financial reports to generate insights
  • Improving market forecasting and risk analysis with alternative data
  • Personalizing marketing through predictive analytics and sentiment analysis
  • Enhancing financial research and analysis with natural language generation

The applications of AI in finance are rapidly evolving. As technology like machine learning, computer vision and natural language processing continue to advance, more processes can be augmented and automated to drive value.

[h2]The Future of AI in Finance[/h2]

While AI adoption is accelerating, there are still challenges to overcome. Historical biases in data can lead to problematic predictions if not addressed properly. Interpretability continues to be an issue, especially for complex deep learning models. Stringent model governance and ethics practices are required to build trust and mitigate risks.

However, the opportunities enabled by AI are tremendous. AI can enhance almost every aspect of the financial services value chain, from customer acquisition to product delivery and portfolio management.

To maximize the benefits of AI, financial institutions need to take an enterprise-wide view and identify the most impactful business applications. With the right strategy and governance, AI can help drive competitive advantage for years to come. Those who lag in adoption risk falling behind digitally progressive institutions and fintech disruptors.

As someone with deep expertise in data extraction, I understand the importance of high-quality data. For AI systems to work well, they need to be constantly fed new, clean data from applications like process mining, document capture and transaction monitoring. The raw material of AI is data – so firms must invest in automation and connectivity to fuel AI engines.

With enabling technologies reaching maturity and proven value delivered in many financial domains, AI is at an inflection point for broad adoption. The examples here illustrate the tangible and immense potential of AI in finance. While thoughtful strategy and planning is required, leading institutions are already realizing major benefits – I expect the pace of innovation to dramatically accelerate going forward.