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

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

As seen in the image above, interest in artificial intelligence (AI) in finance is increasing, like in other industries. According to a 2020 Business Insider report, 75% of respondents at banks with over $100 billion in assets are implementing AI technologies. 1 McKinsey shares that banking and other financial service companies can generate more than $250 billion in value by applying AI technologies in their financial processes.2

However, there is still a long way for AI models to be widely used in financial services. For example, historical bias can be an issue in automated credit scoring. AI models could take into account variables like gender, race, or profession which may have been used historically in credit applications. Banks need to monitor models to avoid such situations.

Although the integration of AI into finance needs further development, the benefits definitely outweigh the potential costs. AI technologies will help banks and other financial institutions accelerate their processes with reduced cost and error while ensuring data security and compliance.

To understand which processes to automate with AI, process understanding is key. Process mining helps finance businesses identify their process issues and ensure compliance.


IBM Process Mining enables financial organizations to measure their process performance and modify those that do not comply with best practices and reference models. Thus, IBM’s process mining and the digital twin of an organization (DTO) capabilities help finance companies and banks transform their processes by identifying candidate activities for automation and simulating the ROI of such implementations.


Retail Lending Operations

Thanks to document capture technologies, financial institutions can automate their credit applicant evaluation processes. Instead of reviewing financial documents like payslips or invoices manually, which is a tiring task, AI algorithms can handle this operation, capture data from documents automatically, and manage lending operations with less human intervention. This will enable banks and financial institutions to conclude credit applications faster and with fewer errors.

Commercial Lending Operations

Similarly, financial companies can capture relevant data from borrower companies’ financial documents, like annual reports and cash flow statements. With the extracted data, credit evaluation can be handled much accurately, and banks can provide faster services for lending operations.

Retail Credit Scoring

Financial companies can leverage AI to evaluate credit applications faster and more accurately. AI tools leverage predictive models to assess applicants’ credit scores and enable reduced compliance and regulatory costs on top of better decision-making. For example, Discover Financial Services has accelerated its credit assessment processes by ten times and achieve a more accurate view of borrowers by using AI technologies in evaluating credit applicants. For more on credit scoring, feel free to read our article on the topic or access an interactive list of leading vendors in the space.

Commercial Credit Scoring

AI can analyze relevant financial information and provide insights about financials by leveraging techniques like machine learning and natural language processing. Instead of conducting numerous calculations in spreadsheets or financial documents, AI can rapidly handle large volumes of documents and deliver insights without missing an important point. This can enable better commercial loan decisions.



Companies can offer AI chatbots and virtual assistants to monitor personal finances. These assistants can provide insights based on target savings or spending amounts. Besides giving insights on personal finances, robo-advisors can give financial advice to help investors manage their portfolio optimally and recommend a personalized investment portfolio containing shares, bonds, and other asset types. To do that, robo-advisors use customers’ information about their investment experience and risk appetite.


Debt Collection

The Consumer Financial Protection Bureau (CFPB) shares that “Continued attempts to collect debt not owed” is the most common complaint by 39% in the US in 2017.3
Banks and other financial institutions can use AI to solve this issue and provide a compliant and efficient debt collection process. According to the CEO of Brighterion, a MasterCard company, effective use of AI can help reduce delinquency rates by 76%.


Companies can introduce AI-based invoice capture technologies to automate their invoice systems and use accessible billing services that remind their customers to pay. These will enable businesses to accelerate their processes, reduce any manual errors and costs, and improve loan recovery ratios. Feel free to read our in-depth source-to-pay automation guide to learn more.

Account Reconciliation in Commercial Banking

Companies can leverage AI to extract data from bank statements and compare them in complex spreadsheets. By using AI, account reconciliation processes can be accelerated significantly, and errors that can cause significant disruption would be eliminated.


Insurance Pricing

Like credit applications, AI can assess customers’ risk profile and identify the optimal prices to quote with the right insurance plan. This would decrease the workflow in business operations and reduce costs while improving customer satisfaction.

Claims Processing

Claims processing includes multiple tasks, including review, investigation, adjustment, remittance, or denial. As AI can rapidly handle large volumes of documents required for these tasks thanks to document processing technologies, it can also detect fraudulent claims and check if claims fit regulations.

As an example, some vendors developed an AI system that can recognize accident images and estimate repair costs in real-time1. As a result, it claims that insurance companies can accelerate claims processing by ten times.

Audit & Compliance

Fraud Detection

According to KPMG, the main challenge that banks face today is cyber and data breaches. More than half of the survey respondents share that they can only recover less than 25% of fraud losses, which makes fraud prevention necessary.

AI technologies advanced significantly to detect fraudulent actions and maintain system security. Using AI for fraud detection can also improve general regulatory compliance matters, lower workload, and operational costs by limiting exposure to fraudulent documents. In a case study2, DZ Bank has reduced the workload of security operations teams by 36x.

Regulatory Compliance

Complying with regulatory requirements is essential for banks and other financial institutions. AI can leverage Natural Language Processing (NLP) technologies to scan legal and regulatory documents for compliance issues. As a result, it is a scalable and cost-effective solution because AI can browse thousands of documents rapidly to check non-compliant issues without any manual intervention.

Travel & Expense Management 

Expenditure reports require travel receipt checks (like hotel reservations, flight tickets, gas station receipts, etc.) for compliance, VAT deduction regulations, and income tax laws. While this task includes compliance risks concerning fraud and payroll taxation, AI can leverage deep learning algorithms and document capture technologies to prevent non-compliant spending and reduce approval workflows.

Check out our data-driven list of data extraction tools to learn more.

Customer Service

Know Your Customers (KYC) Processes

By leveraging AI technologies like natural language processing and data extraction models, banks can find anomalous patterns and identifying areas of risk in their KYC processes without human intervention. For edge cases where human interaction is needed, the case can be forwarded for approval. The integration of AI technologies will have benefits like accelerated processing times, improved security and compliance, and reduced errors in these processes.

Responding to Customer Requests

Conversational AI systems can instantly support customers to fulfill their requests. In cases where the claim is not resolved, humans can intervene. By integrating AI into customer service, customer requests are addressed faster, the workload of call center workers would be reduced, and they can focus on more complex customer requests.

Identification of upsell & cross-sell opportunities

Banks and other financial institutions can accurately discover unaddressed customer needs, thanks to CRM systems and AI technologies. This can help increase customer satisfaction while increasing revenues for the financial institution. For example, a company can offer car insurance to its customer who is in the process of buying car. Oliver Wyman shares that using AI insights can increase annual income from email cross-sell by four times.

Customer Churn Prediction

AI models can detect patterns in customer behaviors and predict which customers have a higher potential to churn in the next term. By analyzing these behaviors, banks and other financial institutions can identify why a customer is at risk and take actions accordingly to prevent churn.



Investment companies have started to use AI to detect the patterns in the market and predict their future values. By that, AI can discover a broader range of trading opportunities where humans can’t detect. Another benefit of AI is that it can analyze large amounts of complex data faster than people, which provides time and money-saving. Kavout, an AI trading service, estimates that they can approximately generate 4.84% with their AI-powered trading models.

However, algorithmic trading still has a way to be used more widely as it is still unable to perform better than humans. According to Bloomberg, the share of hedge funds that use AI decreased by 7.3% in March 2018. It has fallen by 2.4% in the previous period.

Now that you have checked out AI applications in finance, feel free to check out other AI applications in marketingsalescustomer servicehealthcareaudit, or analytics. You can also read our other articles about AI and finance:

You can also our list of AI tools and services:

If you have more questions, do not hesitate to contact us:

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  1. Claims processing example
  2. Fraud detection case study
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Anna Shaw
Aug 25, 2020 at 13:51

The finance industry have led the way in really understanding the applications and benefits of ai and data science in terms of specific applications and use cases.

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