7 Top AI Use Cases in the Financial Industry

AI and related technologies are helping financial institutions accelerate their digital transformation, automate workflows, reduce cost and error while ensuring data security and compliance.

Over 75% of banks with $100 billion in assets are already using AI, and a report from McKinsey estimates that banks and other financial service companies can generate more than $250 billion in value by applying AI technologies to their financial processes.

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Discover the various use cases of AI in the financial sector and the benefits they can bring your organisation.

Use Cases of AI in the Financial Industry 

AI is used in the financial industry in different areas, from lending and investment to audit and insurance.

AI in Lending

AI is used in lending for retail and commercial lending operations and credit scoring.

Financial institutions are automating credit applicant evaluation processes with AI. Instead of manually reviewing financial documents like payslips or invoices, AI is capturing data from documents automatically and managing lending operations with fewer human touchpoints.

In B2B lending, AI can extract information from annual reports and cash flow statements, which the financial institution can use to conduct credit evaluations more accurately and provide faster service.

Predictive models are used to evaluate credit scores and reduce compliance and regulatory costs. Discover Financial Services accelerated its credit assessment process ten times and got a more accurate view of borrowers by using AI technologies in evaluating credit applicants.


AI chatbots and virtual assistants can monitor personal finances and provide insights based on target savings or spending amounts.

Robo-advisors use customer information about investment experience and risk tolerance to give financial advice to help investors manage their portfolio optimally and recommend a personalised investment portfolio containing shares, bonds and other asset types.


Debt collection and “attempts to collect debt not owed” are persistent problems, and some companies are using AI to solve this problem. Brighterion, a MasterCard company, effectively implemented AI to reduce delinquency rates by 76%.

AI-based invoice capture is helping companies automate their invoice systems that remind customers to pay and help businesses speed up the procure-to-pay process, reduce manual errors and improve loan recovery ratios.

In commercial banking, AI is used in account reconciliation processes to speed up the process significantly and eliminate errors from manual processes.


AI can assess customers’ risk profiles and identify the optimal prices to recommend the best insurance plan. This helps streamline workflow and reduce cost and improve the accuracy of insurance pricing.

As AI can analyse large volumes of data quickly, it is also used in claims processing, which includes review, investigation, adjustment, remittance or denial. Further, AI is also able to detect fraudulent claims and check if claims are compliant with regulations.

For example, Tractable’s AI system can recognise accident images and estimate repair costs in real-time, which the company can accelerate claims processing ten times.

Audit and Compliance

Cyber attacks and data breaches are two of the top threats financial institutions face. But AI is helping financial institutions detect fraudulent actions and maintain system security. Using AI for fraud detection can also improve general regulatory compliance and lower workload and operational costs by limiting exposure to fraudulent documents.

Financial institutions are using Natural Language Processing (NLP) technologies to scan legal and regulatory documents to streamline their compliance functions. AI can browse thousands of documents rapidly to check non-compliant issues without any manual intervention.

Customer Service

Banks and financial services firms are using AI, natural language processing and data extraction models for KYC processes. AI can help banks detect anomalous patterns and identify areas of risk in the KYC process without human intervention.

Conversational AI systems are used to support customers and fulfill requests, to the point human intervention is needed. Thus, customer requests are addressed faster, employee workload is reduced and more attention is given to complex customer requests.

AI is also helping banks discover unaddressed customer needs and providing more upsell and cross-sell opportunities.

Banks are using AI to analyse customer behaviour to predict customer churn rates. By identifying these customers, they are taking the necessary actions to prevent churn.


AI’s ability to analyse large amounts of complex data in real-time is proving significant in algorithmic trading. An AI trading service, Kavout, estimates that they can generate approximately 4.84% with their AI-powered trading models.

As the use cases of AI in the financial industry keep growing, managers and finance executives have a wealth of opportunities to implement this technology in their organisations to gain a competitive advantage.

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