Innovative Use Cases of AI in Finance 2023 Artificial intelligence (AI) originated on the pages of fiction and fairy tale literature, much like many other technological advances. In less than 70 years since the term “artificial intelligence” first appeared, it has integrated itself into the most demanding and fast-paced businesses. And yet, despite its pervasiveness in our daily lives, we are unaware of it. AI is transforming every industry and business set of functions, which has increased interest in AI, its subfields, and related subjects, such as data science and machine learning.
According to a recent McKinsey poll, 56% of businesses employ AI in at least one operational area. Thus, one should first understand how AI might help their business and what use cases or applications might be incorporated. But specifically, how can AI benefit the finance industry? In this article, we’ll have a look at several key AI use cases in the financial sector and make some predictions about this industry.
AI in Banking and Finance The study of AI focuses on building intelligent computers that function and carry out work much like humans. These machines possess the capacity to learn, organize, and understand data to build predictions on it. Accordingly, it has evolved into a critical component of technology in the banking, financial services, and insurance (BFSI) sector, changing the way goods and services are provided.
Finance artificial intelligence enables organizations to automate mission-critical procedures like risk management and fraud prevention, not to mention opening up new possibilities such as chatbots and intelligent recommender systems usage for retail banks.
Also Read:- Which is the Most Used AI Technology in Banking and Finance
Additionally, customers now can independently check their card balances using financial AI bots, schedule their payments, monitor transaction history, ask a digital assistant for additional info and support, and receive personalized banking advice wherever they are.
Use cases in AI and ML for Finance in 2023 The use cases mentioned below will provide you with an understanding of what is possible in the sphere of finance and where it is best to use AI systems:
Fraud Detection and Prevention You can use machine learning and AI to identify fraudulent and abnormal behavior, as well as improve overall compliance and performance. There is also a possibility to reduce your exposure to counterfeit paperwork and save operational costs. Here are some other goals you can achieve with AI:
preventing credit card fraud recognizing identity theft detecting fraud connected to money laundering
Risk Management Use natural language processing to swiftly look for compliance concerns in a huge number of legal and regulatory materials. In addition, processing tens of thousands of documents without human involvement is also possible due to AI, as well as the following aspects:
recognizing false reviews phishing and spam filtering for email determining if data is correct or not by classifying it
Customer Experience AI encourages banks to develop services like saving recommendations and expenditure analysis that meet fundamental client needs with the least amount of human participation. AI can provide banks with data on products and services, profit margins, and costs. Here are some other ways you can improve your customer experience with AI:
transaction classification receiving a loan in seconds rather than hours or days thanks to credit scoring suggesting the subsequent item a consumer might find interesting
Process Automation To monitor your accounts, use AI-powered chatbots and mobile assistant apps. Choose your desired consumption or savings rates based on your own goals. The remainder will be taken care of by your finance assistant, who will also provide you with suggestions on how to reach your financial objectives. Also, consider the following opportunities:
creating a chatbot using natural language reading papers to find key information and classified documents analyzing market trends and carrying out automated forecast analysis
Trading Machine learning and other AI approaches will be useful for financial analysis, trading, and other investment tactics:
trading software that uses sentiment analysis to evaluate Internet content like tweets, articles, news, etc advisory services and portfolio management using satellite data analysis to determine how often a company’s parking spaces are used Challenges of Using AI in Finance It’s equally important to comprehend the potential difficulties related to applying AI and machine learning in this sector:
Data Issues There has to be a method for quickly identifying anomalies throughout the whole pipeline, identifying the issue, and fixing it. Many companies are based on this concept, and some even provide git-like version control for their data.
Dimensionality reduction Financial institutions are said to be sitting on data troves since a single transaction might include hundreds of data pieces. Many machine learning techniques tend to suffer as the number of attributes increases.
Security and Compliance The volume of consumer data, which includes private and sensitive information, like transaction history, account information, and loan details, is one of the key issues facing AI in financial services.
This necessitates the use of extra security measures. To guarantee that client data is handled properly, the suitable data partner will offer a variety of security solutions, robust data protection through certifications and regulations, and security standards.
Why Is It Important to Study and Use Examples for AI? The financial sector is anticipating a highly interesting year in 2023 as a result of the growing use of cloud-based services, machine learning, and AI. Significant breakthroughs are expected to occur in financial technology as a whole, as well as in data analysis and automation.
AI and machine learning are growing swiftly. New applications and use cases are created daily. Although it may be difficult to keep up with all the many uses of AI, it is important to understand the possible benefits of implementing them in your business. By looking into market trends, it will be simpler to modify internal and exterior operations to the realities of competition.
