As in most industries, we see a growing impact of artificial intelligence in the financial sector.
The use of AI technologies has helped banks, fintechs, and other financial institutions streamline and optimize operations such as credit risk management, trading, fraud identification, money laundering prevention, and more.
Due to the automation and process improvement that AI brings to the industry, it is no wonder that 70% of all financial services companies use AI and ML in their operations.
With all that in mind, let’s find out what are the main use cases of AI solutions for the financial sector and how businesses can benefit from the implementation of this technology in their companies.
Financial document data extraction
Today, the financial sector is bombarded with a myriad of documents related to transactions, data security, government and corporate regulations, and much more.
The burden of managing so much paperwork represents a significant challenge for financial institutions as it is costly and time-consuming work, forcing staff to perform repetitive and demotivating activities.
However, with the help of AI, deep learning, and computer vision, financial firms can make manual data handling a thing of the past.
Automating manual data handling with AI means allowing the system to identify and extract all the key data from financial documents at a much faster speed, with greater precision and at a lower cost.
Quick service is important for your business to stand out in the financial industry. Customers may be interested in opening an account, making a transaction, or depositing money.
Providing fast assistance to your clients means they can get on with their errands with the least amount of time wasted, which is the key to not only retaining existing clientele, but also attracting new ones.
AI chatbots are a great way to not only serve your customers quickly, but also provide 24/7 support.
AI chatbots are fully independent virtual agents that use machine learning and natural language processing technologies to answer questions based on predefined scripts and learn from new interactions.
In this way, you can automate the routine request about credit card blocking, balance inquiries, card limits, payment problems, etc.
Customer churn prevention
Another essential part of customer service in the financial industry is churn prevention, as it identifies customers with a high probability of leaving the company based on their past behavior and historical data.
Businesses began to pay more attention to it, since acquiring new customers can be 5 to 25 times more expensive than retaining existing ones.
By using data related to customer cash balance, transactions, credit information, and then applying machine learning algorithms, financial firms can predict client churn with high accuracy and guarantee customer retention with data-backed decisions and promotions.
Unfortunately, even today, fraud is identified after the incident occurs and in most cases when it is too late. In the financial industry we still hear stories of unauthorized money withdrawals, check forgery or use of stolen credit card information.
However, AI-powered fraud prevention can help banks fight the problem effectively. Machine learning models can analyze data in a matter of milliseconds and review spending history and behavior.
In this way, the models can instantly learn and detect any kind of irregularities, such as the use of cards from different countries in a short period of time.
All these operations would be impossible for a human fraud analyst to perform as quickly as artificial intelligence systems do, making the solution more effective against fraudulent activities.
Similar to the example mentioned above, in trading too, AI can analyze a large amount of data, detect patterns, and execute trades with high precision and speed. On top of that, as the amount of financial data increases, AI algorithms are getting smarter and smarter. Therefore, when it comes to trading, AI is a more profitable solution for financial institutions due to its ability to multitask and make accurate predictions.
The ability of AI to power and automate operations is invaluable to the financial industry. Many financial institutions began using AI to predict results, improve the customer experience, and take their services to the next level.
As artificial intelligence in the financial market is expected to reach $ 26.67 billion in 2026, it is important that banks, fintechs, and other companies follow the trend and keep up with the competition.
If you want to incorporate artificial intelligence into your business, schedule a free consultation with our AI experts and let’s discuss the potential of our partnership and how our solutions can drive your projects.