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Artificial intelligence is rapidly being adopted in many industries, making AI-powered automation a matter of "when," not "if." Finance and banking are no exception. In fact, this sector can especially benefit from the real-time fraud detection and prevention that such solutions offer.
But, training AI models using machine learning (ML) requires AI-ready data.
Of course, you might ask how AI-powered automation is used in finance and banking. Here’s how:
AI Use Cases in the Financial Services Industry
Automation and AI adoption can help financial institutions in a variety of ways. Here are some of the roles AI is taking on in the financial services industry:
Improving Customer Experience
No one likes experiencing long wait times when calling a bank (or any other establishment, really). After hearing “Your call is important to us” for the thousandth time, callers start wondering why they even bothered. It’s frustrating, and it leaves them with a negative impression of the bank.
As a business—and let’s face it, that’s what banks are—you would want to make your customers’ experience as seamless as possible. Part of it is ensuring they get top-notch service at every point of contact.
This is what automation helps you enable.
Instead of hiring more people in your customer care department, you can now use chatbots and interactive voice response (IVR) systems. These are powered by Large Language Models (LLMs), natural language processing (NLP), and generative AI, and can talk to customers when your human employees are busy.
These smart tools can then route callers to the appropriate department, where they can get the help or information they need.
You can also make your banking app easier to use with facial recognition and voice commands.
Finally, you can deliver a more personalized service by offering customers services and products based on their past preferences.
Risk Management
In the past, banking and financial transactions used to take place in person. Now, they are carried out online. As a result, bad actors can more easily steal financial information and use it to commit financial crimes.
In the past, it was very difficult to monitor all transactions as they happened. Today, however, AI monitors all activity and can be taught to flag any anomalies as they happen. The result? Any suspicious activity can be caught early and investigated before it becomes a major incident.
Predictive Analytics
Much of what financial institutions do essentially boils down to predicting the future. Of course, these kinds of predictions aren’t made using the alignment of stars or tea leaves. You base them on hard evidence. The problem is, sometimes this evidence—this data—is so voluminous that examining it manually can take time.
For a computer, however, it takes no more than a few seconds.
AI-powered systems can look at real-time feeds to predict short-term price trends in a matter of seconds. They can also scour through social media to monitor shifts in sentiment and predict whether a business’s stock prices will rise or fall.
AI agents can go through a loan application and calculate the applicant’s loan default risk. They can analyze thousands of variables, including credit history, transaction behavior, and even social media activity. Then, almost instantly, they let you know whether the loan should be granted or not.
Artificial intelligence even helps you analyze a customer’s transaction patterns and tells you when to offer them a product or service.
Image Recognition and Document Processing
We live in the age of information, but not all of it is neatly structured for immediate use.
For example, a financial institution has customer details like their name, address, and card information in a neat database. But, it might also have scans of their property title deeds, tax returns, KYC (know your customers) documents, and more. It might store customer bank statements in the form of PDF documents, and chat transcripts in another format. It might even have important information saved in email correspondence.
All of this is called unstructured data. Unlike structured data, it’s slightly more difficult to classify, arrange, and store. Humans generally can’t cope with the volume and complexity that such data comes with.
An AI tool, on the other hand, will be able to find and process it quickly and efficiently. This speed provides you with more than just a competitive advantage. An AI system can scan this information and detect any inconsistencies and compliance issues. This ability is invaluable for your anti-money laundering (AML) efforts.
Such documents also hold a wealth of information that could provide insights for your strategy.
Cost Saving
AI agents reduce the workload on your human employees. They can perform data analysis, document verification, and other repetitive tasks much faster than people. This gives your experts more time to focus on strategy and other higher-value work.
It also means you don’t need to hire as many people.
But that’s not the only way AI solutions help financial firms. These tools monitor and prevent fraudulent transactions, which can be quite costly for you.
Plus, AI governance helps you stay compliant with regulations more easily. This is important because not doing so can lead to penalties, including fines (which can be quite hefty).
Benefits of AI in Financial Services
Accuracy: Reduce the number of manual errors in workflows.
Efficiency: Process repetitive tasks more quickly and with fewer resources.
Speed: Go through more information daily to find patterns, gain insights, and make decisions faster.
Availability: Always be there for your customers, regardless of time or location.
Innovation: Create market-leading products with the extra time and additional insights.
Regulatory Compliance: Protect the privacy of your customers and secure your data infrastructure.
Challenges in Building an AI-Ready Data Architecture
Data Silos and Legacy Systems
In the past, data was often limited to the department that collected it. Financial firms also operated on systems that gradually became less relevant over time. Now, when they try to modernize their operations, the biggest challenge they face is integrating all the data into one database without affecting operations.
In some cases, the data can’t be moved and must be accessed from where it is, which creates a logistical nightmare.
Data Consistency
The problem with silos is that everyone has their own systems and standards for data storage and governance. As a result, the information is not formatted consistently. Such data might need more extensive cleansing and validation to be made usable.
Scalability and Performance
If the organization doesn’t have modern infrastructure, the sudden growth of data can lead to performance issues. This can be easily solved by moving systems to the cloud. However, that migration must be planned carefully.
In-House Talent
If you want to build and maintain your AI infrastructure, you need people with the required skill sets on your team. You will also need to train your staff and empower them to work with AI technology and data management.
Regulatory Compliance
Financial institutions are subject to industry standards like the PCI DSS and laws like the Gramm-Leach-Bliley Act of 1999. AI governance and the regulations around it are a whole different beast. You need to comply with all of these while trying to innovate in a competitive market.
The Importance of AI-Ready Data for Financial Institutions
We’ve already discussed why data preparation is important for AI implementation, but let’s quickly recap. AI data should be in a format that the model can understand. For that, it needs to have certain characteristics, such as being clean, consistent, labeled, and more.
High data quality can give you a more accurate model that’s efficient and scalable. To ensure data readiness for AI adoption, you need to clean, organize, and infuse it with semantic meaning.
All data—structured and unstructured—should be accessible to your data model, ideally through data virtualization. If you can connect your LLM to real-time data, it will deliver more timely insights, more effective monitoring, and better predictions.
Connecting Your AI-Ready Data to Your Model: Get AI-Ready With RAW
We know how important it is to get relevant and timely data for your financial services firm. With RAW, you can build, host, and manage APIs that integrate your financial data in real time with your AI solution. You also get CRUD support for dynamic data management.
Learn about why APIs are the best method for AI integration services.
