If you’re in the banking industry, you know that AI is poised to transform how you operate in multiple ways. But do you know the leading use cases already being deployed across the industry — where they are helping finance organizations achieve a whole new level of business results?
As AI rapidly expands around the world, we’ve seen the number of banking use cases grow exponentially — from fraud detection to regulatory compliance support. Banking institutions are leveraging the technology because it’s making their jobs easier, faster, more reliable, more secure and more profitable. AI’s key capabilities include processing unlimited volumes of data, identifying patterns within massive amounts of data and providing insights that support informed decisions.
These six key use case categories represent multiple opportunities in which early AI adopters in the banking industry are already leveraging the technology to improve their organizations. Discover which use cases can help your organization solve your business challenges and leverage new AI-driven opportunities.
1. Fraud detection and prevention
One fifth of consumers worldwide have been victims of payment fraud since 2021, according to an ACI Worldwide report. The research also found that fraud losses within banks and financial institutions are expected to surpass $40 billion by 2027.
The use of AI in fraud detection relies on sophisticated algorithms that can be used to flag specific anomalies that suggest fraudulent activity. This enables your organization to more easily identify and preemptively intervene, preventing potential fraud before it occurs.
Here are a few ways that AI is enhancing fraud detection and prevention to better protect banks and their customers:
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- Real-time transaction monitoring: Identify unusual patterns and flagging suspicious activities.
- Biometric authentication: Enhance security measures with fingerprint, facial or voice recognition.
- Advanced analytics: Apply data analysis to discover complex fraud schemes.
- Anomaly detection: Spot fraudulent activities, such as fake loan applications or misappropriation of funds.
2. Customer experience enhancement
Here are a few ways that AI is enhancing customer experience to help banking institutions meet their customers’ demands:
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- Chatbots and virtual assistants: Provide 24x7x365 customer support and answering queries.
- Personalized recommendations: Offer tailored financial products and services.
- Sentiment analysis: Understand customer feedback and improving services.
3. Risk management
According to a McKinsey report, banks need to overhaul the way their risk teams operate. The coming decade will witness a more significant transformation in risk management than the past decade, driven by deepening regulations, evolving customer expectations and emerging risks such as cyberthreats.
Banks should act now to prepare for these shifts, leveraging AI and advanced analytics to develop new risk management techniques and services. In addition, risk functions are expected to meet these demands at a lower cost, emphasizing the need for strategic planning and investment in modern echnologies to streamline operations and enhance decision-making capabilities.
Here are a few ways that AI is transforming risk management:
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- Credit scoring: Assess creditworthiness accurately and efficiently.
- Market risk analysis: Predict market fluctuations and manage portfolios more efficiently.
- Operational risk management: Identify potential risks and implement mitigation strategies.
4.Process automation
The banking and finance industries were early adopters of robotic process automation (RPA) technologies, according to Grand View Research. Now, the adoption of RPA is poised to expand exponentially — from $861 million in 2023 to $9 billion by 2030.
Working together, AI and RPA can help banks automate the management of large volumes of unstructured data, optimize investment strategies, enhance customer experiences and support automation. This reduces repetitive administrative tasks, helps support more successful investment strategies and expands opportunities for greater innovation.
AI is enhancing RPA in these key ways:
- Automation: Automate repetitive tasks, like data entry and report generation.
- Document automation: Process and extract information from various documents.
- Loan processing: Automate loan applications and approvals.
5. Investment management
According to a study by Mercer Investments, 91% of managers are currently using or planning to use AI within their investment strategy or asset class research. Among managers who are already using AI, most cite availability and data quality as barriers to unlocking its potential, followed by concerns about compatibility, integration, and ethical and legal issues.
Among the ideal benefits of AI algorithms in investment management is its ability to automate data collection and analysis from various sources, including market data and financial statements.
Here are a few use cases where AI is enhancing investment management to help banks improve their outcomes:
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- Algorithmic trading: Execute trades based on predefined algorithms.
- Portfolio optimization: Build diversified portfolios aligned with investor goals.
- Robo-advisory: Provide automated financial advice to clients.
6. Regulatory compliance
In 2024, federal banking regulators signaled that supervisory scrutiny of financial institutions is expected to increase, with particular attention on untimely supervisory remediation, according to Deloitte. What’s more, scrutiny is expanding beyond primarily large banks to include midsize regional banks.
To support regulatory compliance, AI systems can constantly monitor operations and transactions for any issues. They can also automate report generation and documentation for multiple regulatory bodies.
Here are a few ways that AI is already enhancing regulatory compliance practices in today’s banking organizations:
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- Know your customer (KYC) verification: Streamline customer onboarding and verification processes.
- Anti-money laundering (AML) compliance: Detect suspicious transactions and reporting them.
- Regulatory reporting: Automate the generation of regulatory reports.
Banking and financial institutions that want to stay competitive in today’s increasingly AI-driven business world will need to adopt these advanced technologies and leverage the six top use case categories. The benefits will help meet the financial industries’ top business goals — enhancing productivity, maintaining strong customer relationships, creating innovative new products and services, and strengthening their bottom line.