Use Cases Of AI in Banking
1.Customer care service
Chatbots, interactive voice response (IVR), and virtual assistants are popular AI-enabled tools. And as the capabilities of AI such as natural language processing and speech recognition increase, banks will continue to adopt these solutions. Banks are not only employing these solutions to minimize costs by up to 30%, but also to reduce end-to-end communication time with clients. For routine inquiries, bots are shown to improve response times by 99%, reducing time-to-resolution from hours to just a few minutes.
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https://www.definedcrowd.com/ai-in-banking-3-use-cases/
2.Banking Process Optimization
Banking is among one of the biggest adopters of these initiatives, and several applications are being used to transform departments. A great example of a company using AI to optimize processes is American banks, JP Morgan. Their internal IT team uses bots to respond to requests, such as changing an employee’s password. With over 1.7 million minor applications year on year, these bots are highly valued, especially for one of the largest banks in the US. Don't use plagiarised sources.Get your custom essay just from $11/page
Source
https://www.definedcrowd.com/ai-in-banking-3-use-cases/
3.Banking Compliance and Risk Management
Banks need to respond to large amounts of unstructured data that emerge from challenging regulatory demands. AI has proven particularly effective in dealing with this data in daily tasks such as automating legal, compliance, and risk documentation, as well as analyzing data sets that train machine learning algorithms to track credit card fraud or money laundering. A lot of these tasks involve excessive manual work; by moving them to an AI-powered system; instead, banks can free up employees to deal with more complex decisions.
Source
https://www.definedcrowd.com/ai-in-banking-3-use-cases/
4.Banking Credit Decisions
Artificial intelligence solutions are helping banks and credit lenders make smarter underwriting decisions by utilizing a variety of factors that more accurately assess traditionally underserved borrowers, like millennials, in the credit decision making process.
Source
https://builtin.com/artificial-intelligence/ai-finance-banking-applications-companies
5.Managing Financial Risk
Time is money in the finance world, but the risk can be deadly if not given the proper attention. Accurate forecast predictions are crucial to both the speed and protection of many businesses. Financial markets are turning more and more to machine learning, a subset of artificial intelligence, to create more exacting, agile models. These predictions help financial experts utilize existing data to pinpoint trends, identify risks, conserve the workforce, and ensure better information for future planning.
Source
https://builtin.com/artificial-intelligence/ai-finance-banking-applications-companies
6.Quantitative Trading
Quantitative trading is the process of using large data sets to identify patterns that can be used to make strategic trades. Artificial intelligence is useful in this type of trading. AI-powered computers can analyze large, complex data sets faster and more efficiently than humans. The resulting algorithmic trading processes automate trades and save valuable time.
Source
https://builtin.com/artificial-intelligence/ai-finance-banking-applications-companies
7.Investment Banking And Simulation
Automation hit investment banking earlier than other bank sectors, and it hit hard. Probably the most famous example of that is this: In 2000, there were 600 traders at the Goldman Sachs U.S. cash equities trading desk. In 2017, only two remained. But the result wasn’t a gutting so much as a shift: The firm has added thousands of computer engineer jobs.
Source
https://builtin.com/artificial-intelligence/ai-finance-banking-applications-companies
8.Front Office And Customer Support
Artificial intelligence has impacted this landscape, with AI-enabled chatbots and voice assistants now the norm at major financial institutions. We also see AI change biometric authorization and, for those who enjoy the occasional throwback visit to a physical bank, AI-enabled robotic help.
Source
https://builtin.com/artificial-intelligence/ai-finance-banking-applications-companies
9.Quantitative Analysis
Quantitative analysis is used to process vast sets of unstructured data and identify real-time patterns in financial markets. One of Kavout’s solutions is the Kai Score, an AI-powered stock ranker. The Kai Score analyzes massive amounts of data, such as SEC filings and price patterns, then condenses the information into a numerical rank for stocks. The higher the Kai Score, the more likely the stock will outperform the market.
Source
https://builtin.com/artificial-intelligence/ai-finance-banking-applications-companies
10.Search Engine For The Finance Industry
The platform utilizes natural language processing to analyze keyword searches within filings, transcripts, research, and news to discover changes and trends in financial markets.
Source
https://builtin.com/artificial-intelligence/ai-finance-banking-applications-companies
11.Provides Machine Intelligence and Data Analytics to Leading Financial Institutions
AI offers analytical solutions using a combination of cloud computing and natural language processing (NLP). The company’s systems can provide answers to complex financial questions in plain English.
Source
https://builtin.com/artificial-intelligence/ai-finance-banking-applications-companies
12.Integration of AI in Mobile Banking Apps.
Most of the banks have started embracing AI and related technologies worldwide. As per the survey by the National Business Research Institute, over 32 percent of financial institutions use AI using voice recognition and predictive analysis. Banks are using AI technology to enhance the customer experience by giving it a personalized touch.
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13.Accumulate and analyze useful data
The revolutionary AI technology works on the principle of data collection and analysis. Any AI system can work well with better data sets. A simple mobile banking app enriched with AI-based features can collect all the relevant and useful data of the users to improvise the learning process and enhance the overall user experience. After accumulating and analyzing the data, the experience can be made more personalized.
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14.Drive banking business
Wealth management and portfolio management can be done effectively and efficiently with AI. It can bring ‘banking at your fingertips’ for the users who just hate to visit the banks. It strengthens the mobile banking facility by managing essential banking services. Customers can get the benefits of automated and safe transactions. They get a notification instantly for any suspicious sale as per their usual patterns. Another useful application of AI is a card management system. It not only automates the credit and debit card management system but also makes it safer. It helps the customer get rid of a lengthy authentication process in the case of losing the card. The AI system saves time and efforts of the customers and in a way, improves the mobile banking services.
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15.Prevent frauds
Banks should be bankable for providing secure and swift transactions. AI is designed to detect the fraud in the operations based on a pre-defined set of rules. Also, the mobile app can find out any suspicious activity in the customer’s account based on behavior analysis. For example, any online transaction of a considerable amount from the customer’s account that has a history of small purchases can be figured out instantly.
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