AI in finance is certainly making things a lot more safe and convenient for banks and other financial institutions as well as their customers. Artificial intelligence is already helping the financial industry to optimise their processes and streamline services. In short, AI is transforming the way in which we interact with money.
Artificial intelligence can help with a range of processes from financial risk management, to credit decisions and chatbot assistants. In this article we will take a look at some of main applications of AI in financial institutions and in predicting stock fluctuations.
Table of Contents
Customer Service Chatbots and Virtual Assistants
Banks and other financial institutions have found that the use of chatbots for enhancing the customer service experience proved to be highly successful. A 24/7 access to services such as account information, financial advice and loan applications has greatly enhanced the customer experience.
Chatbots and virtual assistants can answer a multitude of questions and ask them too. This saves a great deal of time for the customer as they don’t need to wait for a person to become available. Automation has greatly streamlined the CRM (Customer Relationship Management) system.
These chatbots are constantly evolving and improving. They can recognise speech patterns, recall past conversations and use the information provided by the user to determine how to help them. In other words, a chatbot can learn, reason, understand, perceive and interact with customers.
Fraud Detection and Security
Cybersecurity has been using machine learning for a number of years now to detect fraudulent claims and money laundering.
There are two machine learning applications that have been used in fraud detection and anti-money laundering (AML). These two are anomaly detection and prescriptive analytics.
Anomaly detection software is used to create a baseline of normal transaction activity by learning the data points that correlate to legitimate transactions. Then if a transaction is entered into the system that is suspiciously off that baseline, the system will flag it up as potential fraud or money laundering.
Predictive analytics could potentially offer a ready-made detection system that would reduce false-positives. The algorithm would be programmed to learn which data points are more likely to correlate to legitimate transactions and which are more likely to be fraudulent. This program would need to be installed and running in the financial institutions software for at least a month to establish a baseline.
Investment bankers, traders and wealth managers could use natural language processing (NLP) software for investment research purposes. This NLP software could scour the web looking for information about companies mergers, acquisitions and profitability. Thereby giving a better idea about which stocks are likely to soar and which are likely to plummet.
According to leading AI company SAS “Through a combination of sentiment analysis, predictive analytics and prescriptive analytics software, the wealth managers and investment bankers would be better placed to know what to invest in. “
Digitalising Paper Documents
In order for banks and insurance firms to create a full picture when processing things such as mortgage applications and insurance claims, they may need to go through historical data. Often this data has been previously stored on paper documents.
AI software can help to digitise the old paper documents through machine learning algorithms that can ‘read’ these documents once they have been scanned in. This will massively speed up the process of checking historical data.
AI software can be used to optimise the insurance claims process. Machine learning could assist with automating the claims process, but also reduce overpayments and claims leakage.
AI could also learn to customise insurance policies through the use of the internet of things (IoT). This system gathers information about the customer and weighs up risk factors etc in a matter of minutes.
AI can be used to streamline many applications in the financial world and drastically improves security on many levels. There are more applications for AI than we have managed to cover in this article, such as underwriting and credit scoring. The possibilities are almost endless.