The finance industry, including banks, fund houses, and fintech companies, is deploying AI/ML algorithms to automate time-consuming, routine processes and offer a streamlined and personalized customer experience. Here are a few key use cases:
8 Use Cases in Finance Industry
1. Financial Monitoring
AI/ML algorithms are used to enhance network security. Data scientists are constantly training systems to detect and flag activities such as money laundering, which can be prevented by financial monitoring. AI/ML technologies may power the most advanced cybersecurity networks in the future.
2. Making Investment Predictions
AI/ML-enabled technologies offer advanced market insights, allowing fund managers to identify specific market changes much earlier compared to traditional investment models.
With major firms investing heavily in AI/ML to develop automated intelligence, there is a huge disruption in the investment banking industry.
3. Process Automation
AI/ML-powered solutions allow finance companies to replace manual effort by automating repetitive tasks through intelligent process automation. Chatbots, paperwork automation, and employee training gamification enhance productivity and are examples of process automation in finance. This allows finance companies to improve customer experience, lower costs, and scale their services.
Additionally, AI/ML technology can easily access data, interpret behaviors, and identify hidden patterns. This is used for customer support systems that can work similarly to an actual human and solve all customers’ unique queries.
4. Secure Transactions
AI/ML solutions are great at detecting transactional fraud by analyzing millions of data points that tend to go unnoticed. AI/ML also reduces the number of false rejections and helps improve the accuracy of approvals. These models are generally based on the client’s behavior and transaction history.
Besides identifying fraudulent behavior with high accuracy, AI/ML solutions can identify suspicious account behavior and prevent fraud in real-time instead of detecting it later.
The most successful application of AI/ML is detecting credit card fraud. Banks generally use monitoring systems trained on historical payment data. Algorithm training and validation are based on huge credit card transaction datasets and can easily classify events as fraud vs. genuine to prevent fraud in real-time.
5. Risk Management
Banks and financial institutions can reduce risk by analyzing massive data sources using AI/ML. Unlike traditional methods, which are usually limited to essential information like credit scores, AI/ML can analyze large volumes of personal information, gather insights, and provide actionable intelligence to help financial institutions make decisions. An example of this could be AI/ML systems analyzing customers’ data applying for loans and assigning risk scores to them. AI/ML algorithms can easily predict the customers at risk of default to help companies offer personalized terms for each customer.
6. Algorithmic Trading
AI/ML in trading is another compelling use case in the finance industry. Algorithmic trading has become increasingly prevalent in global financial markets as AI/ML-based models enable trading companies to make better trading decisions by monitoring trade results and news in real-time. They can detect patterns that drive stock prices up or down.
Machine learning algorithms can simultaneously analyze hundreds of data sources, giving professional traders an advantage over the average investor.
7. Financial Advisory
Several budget management apps are powered by AI/ML, which offer customers the benefit of specialized financial advice and guidance. AI/ML algorithms allow customers to track their finances and help them analyze their spending patterns, and identify where they can cut spending.
Another emerging trend in this context is robo-advisors. Working like regular advisors, they target investors with limited resources (individuals and small to medium-sized businesses) who wish to manage their funds. These AI/ML-based robo-advisors can apply traditional data processing techniques to create financial portfolios and solutions such as trading, investments, retirement plans, etc.
8. Customer Data Management
For banks and financial institutions, data is a crucial resource, making data management central to the business’s success.
The large volumes and structural diversity of financial data, from mobile communications and social media activity to transactional and market data, make it challenging to process data manually.
Integrating AI/ML techniques to manage large volumes of data can bring both process efficiency and the benefit of extracting real intelligence from your data using advanced techniques like data mining and natural language processing.