Financial institutions around the world are leaning towards the use of Artificial intelligence (AI) based technologies to improve their businesses. AI systems can filter through millions and billions of data points to uncover patterns and trends that humans might miss. A financial institution’s ability to evaluate data, detect hazards, and make quick choices is also critical to their performance, given that such institutions usually deal with trillions of dollars each year. Artificial intelligence, in combination with natural language processing, can even be used to develop conversational trees that allow customers to communicate and perform certain tasks via chat or voice application. According to a Gartner poll, 27% of financial institutions anticipate using artificial intelligence in the near future.
We have out together the five use cases of the use of AI in financial applications:
Artificial Intelligence for banking automation
It is now possible to automate processes for tasks such as analysing new rules and regulations or providing individualized financial reports for individuals using AI technology. For example, IBM Watson can comprehend complicated legislation such as the additional reporting requirements of the markets for the Financial Instruments Directive and the Home Mortgage Disclosure Act. As a result, rather than enlisting the help of financial professionals to solve difficulties, which could take hours or days, Watson can do so in a matter of seconds. Similarly, financial managers can utilize AI to provide more detailed status reports for their clients faster, allowing them to deliver more personalized advice to a larger number of clients. In addition, AI enables bankers to make loan decisions in seconds rather than months, considering risks and spending patterns, as well as looking at alternative data sources such as rent and utility payment history.
AI also helps in Fraud detection and management; The rise in digital client transactions in recent years has necessitated the development of accurate fraud detection methods to protect sensitive data. AI can be used to help human analysts and improve rule-based models. This can increase efficiency and accuracy while cutting costs. AI can be used to examine customers spending patterns and behaviours to detect anomalies or flag suspicious activity, such as using a card in multiple geographical locations in a short period of time. It is vital to note that while all use cases in fraud management have various AI algorithm needs, each scenario employs them differently. Another significant benefit of AI-based fraud detection is the machine’s willingness to learn. For instance, if a person corrects a red flag for a regular transaction, the machine will learn from the circumstance and make far more nuanced conclusions about what is (not) fraud.
In the banking industry, AI-powered smart chatbots can provide customers with comprehensive solutions while also lowering call centre workload. Voice-activated virtual assistants are becoming increasingly popular, and many of them are powered by Amazon’s Alexa. These devices have self-learning capabilities, can check balances, account activity and payment schedules, and their capabilities are growing each day. Many banks now have applications that provide individual financial advice and assistance in meeting financial objectives. These AI-powered systems can keep track of income, recurring expenditure and spending habits, and then provide financial recommendations and plans based that information. Mobile banking apps can also be used as reminders to pay bills, complete transactions, and connect with the bank more frequently and efficiently.
Quantitative algorithmic or high frequency trading often known as data-driven investment has recently become more popular on global stock exchanges. To accurately predict future market patterns investment firms, rely on computation and data scientists to predict future market patterns in trading. Machines are particularly good at this as they can process massive amounts of data quickly. Machines can also be programmed to recognize patterns in previous data and predict how they will repeat in the future. Although there are statistical anomalies such as the occurrence of the 2008 financial crisis, a computer can be trained to look for “triggers” for these anomalies in the data and plan for them in future predictions.
Artificial intelligence is crucial for risk management. In high-risk settings, algorithms can be utilized to assess case history and identify any potential complications. This means employing machine learning to create precise models that enable financial experts to analyse specific trends and spot potential hazards for instance, individuals with a high-risk tolerance may be able to use AI to determine when to buy, hold and sell equities. Those with a lesser risk tolerance can also receive alerts when the market is predicted to crash, allowing them to decide whether to keep investing or withdrawing. These models can also aid in the collection of more reliable data for future models.
Machine learning in risk management allows large amounts of data to be submitted to powerful processing tools in a shorter amount of time. Both structured and unstructured data can be managed with cognitive computing. All of which otherwise would ordinarily necessitate the use of human teams for long periods of time.
AI is being successfully used to improve decision-making processes in a variety of industries. One of these sectors is credit, where AI can swiftly and cheaply deliver accurate assessments of potential borrowers. AI credit scoring can be far more difficult than traditional credit rating systems. They can assist in identifying applicants who are more likely to default and those who do not have a solid credit history. For instance, financial institutions like ZestFinance employ AI-powered underwriting tools to analyse clients with poor credit histories. This can provide a clear way of evaluating groups that might otherwise be considered high-risk
The financial industry has been transformed by artificial intelligence use cases that have changed the way we acquire, analyse, and comprehend data to deliver additional value to clients. Banks and FinTech firms are taking advantage of the disconnect between what legacy systems can offer and what customers demand so that they can provide their customers with new products and functionalities.