Artificial intelligence (AI) in banking is playing a critical role in analyzing data, predicting trends and fraud risks, and improving customer engagement.
AI is helping various banking sectors, such as retail, corporate, and investment banking, to gain a deep understanding of market movements, customer behavior, and digital interactions on a large scale.
How are banks using AI?
Banks can effectively use AI in five main ways: to personalize services and products to individual needs, to identify new business opportunities, to predict and detect risks and fraud, and to streamline operations.
What is Generative AI in Banking?
Generative AI is a type of AI that can generate new text, images, videos, sounds, or code.
Generative AI is powered by foundation models (large AI models) that can perform various tasks such as summarization, Q&A, and classification.
Generative AI uses ML models to learn patterns and relationships in a dataset of human-generated content. It then uses the learned patterns to generate new content. You can also learn more about how Generative AI can be used in banking.
Responsible AI in Banking
While AI has a lot of potential to improve the banking sector for banks and their customers, it must be developed and used responsibly. This is especially important when it comes to generative AI.
That’s because some of the concerns about the accuracy and security of generative AI can be exacerbated when used in regulated and controlled industries, such as banking.
We have identified four key issues for generative AI in banking. These are explainability, regulation, privacy and security.
How can AI solve banking challenges?
Speech recognition
Speech can be converted into text to gain insights from customer interactions (e.g. contact center calls) and improve service.
Sentiment analysis
Natural Language AI can be used to analyze emotional opinions in a given text, such as investment research, chat data sentiment.
Anomaly detection
Anomaly detection can be used to detect fraud, financial crimes, and cyber threats.
Anti-money laundering
AI can be used to detect suspicious and potentially money laundering activities in retail and commercial banking faster and more accurately.
Recommendations
Customers can be provided with personalized recommendations for financial products and services, such as banking offerings, based on their journey, interactions, preferences, and financial goals.
Translation
Translate your content, such as financial news and apps, into multiple languages with fast, dynamic machine translation to enhance customer engagement and reach a wider audience anywhere.
Document Processing
Extract structured and unstructured data from documents. Then analyze, search, and store this data for document-related processes like loan servicing and investment opportunity discovery.
Reduce fraud with image recognition
Gain insights from images and videos to speed up customer onboarding processes with identity document verification.
Customer Engagement
Enjoy your customers with human-like AI-powered contact center experiences, such as banking concierge or customer center. This reduces costs and frees up agents’ time. Empower customers with more control over their finances through smart, intuitive experiences.
Data Science and Analytics
Insights from customer, risk, transaction, transaction, or other data can be used to predict specific future outcomes with high accuracy. These capabilities can help detect fraud, mitigate risk, and predict future customer needs. A full suite of data management, analytics, and machine learning tools can be used to generate insights and extract value from data for business intelligence and decision making.
Predictive Models
Insights from customer, risk, transaction, transaction, transaction, or other data can be used to predict specific future outcomes with high accuracy. These capabilities can help detect fraud, mitigate risk, and predict future customer needs.
Cybersecurity
Aspects of cybersecurity can be automated by continuously monitoring and analyzing network traffic to detect, prevent, and respond to cyberattacks and threats.
Generative AI and Engaging Experiences
By creating, recommending, synthesizing, analyzing, and engaging in natural and responsible ways, we can create new AI-powered search and conversational experiences. Watch this video to see how banks are addressing customer credit card concerns with generative AI.
Benefits of AI in Banking
Automation
AI can help automate workflows and processes, support decision-making and service delivery. For example, AI can help automate aspects of a bank’s cybersecurity by continuously monitoring and analyzing network traffic. Or it can enhance a bank’s more customer-centric approach with flexible, personalized digital banking experiences that can meet customer needs more quickly.
Accuracy
AI can help reduce human error in data processing, analytics, document processing, onboarding, customer interactions and other tasks. This is because the algorithms involved in AI always perform the same processes.
Efficiency
When AI is used for repetitive tasks, people can focus on more strategic activities. AI can be used to identify, summarize, It can be used to automate processes like converting phone calls to text or answering customer questions like “when are you closing?”
Speed
AI can speed up the way we process data, find patterns, and discover relationships. This can lead to faster insights for decision-making, trading, risk modeling, and compliance management.
Accessibility
With AI, you can help your customers perform financial tasks, find solutions to meet their goals, and manage their money whenever and wherever they want. When AI and ML run in the cloud, they can continue to perform the tasks assigned to them.
Innovation
The ability to quickly analyze large amounts of data can create unique and innovative products and services that outperform competitors. For example, AI can be used to modernize the banking customer experience without losing its human touch.
The Future of AI in Banking
AI is poised to accelerate growth across the banking sector. Digital platforms are enabling banks to adopt new sales strategies, improve efficiency, focus on data usage, and provide personalized customer relationships at scale.
AI is critical to providing personalized customer experiences, providing more secure and reliable product and service recommendations, and building trust through concierge services that customers can access at critical moments.
Banks are also creating distinct, permission-based digital customer profiles. The challenge is that the data they need is often fragmented. By removing this fragmentation and integrating AI and human interactions seamlessly, banks can design experiences that meet the individual needs of their customers. And they can scale effectively for growth.
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