No industry or domain has been unleft by the disruptions of Artificial intelligence, so how can we skip one of the most important domain of the economy which is banking and finance? AI in banking applications turned the sector into a customer-centric tech innovator. AI systems are helping banks significantly to reduce their costs by productivity upliftment and making data-based decisions unfathomable to a human agent.
How can we skip mentioning, intelligent algorithms which spot fraudulent data within seconds? Nearly 80% of banks are aware of the AI benefits which can give them all together a different edge. In the current year 2023, banks are expected to save $447 billion by AI apps usage. Such a number indicates the finance sector is swiftly moving in favor of AI to improve their overall efficiency and reduce costs. In this article, we shall be focusing on the key applications of AI in the banking domain. Let’s have a look at how this beautiful technology has redefined the customer experience with its exceptional benefits.
Applications Of AI In Banking And Finance
Here are some major AI applications in the banking industry through which you can reap its plethora of benefits. Common, let’s dive in!
Cybersecurity And Fraud Detection
There is an increasing need for the banking domain to ramp up its cybersecurity and fraud detection efforts since a huge number of digital transactions take place such as payment of bills, money withdrawals, checks deposits, or online accounts. This is the place exactly where we can use AI in banking. AI can help banks improve the security of online finance, loopholes tracking in their systems, and minimize risks. AI along with ML is able to identify fraudulent activities and alert customers as well as banks. The DL (deep learning) tool has increased the fraud detection capability of banks by more than 50% and reduced false positives by approximately 60%. AI can also help banks to manage cyber threats. In 2019, the financial sector accounted for 29% of all cyber attacks, making it the most-targeted industry which helped the banks to respond to potential cyberattacks before it could affect their internal systems.
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It is the best example of AI practical applications in banking. Chatbots can work 24*7, unlike humans and with its successful integration the banks are able to ensure easily that they are available for their customers around the clock. Moreover, by the correct perception of customer behavior, chatbots offer personalized client support. One of the best examples of AI chatbots in banking apps is Erica which managed over 50 million client requests in 2019, a virtual assistant from the Bank of America (BOA). This AI chatbot handles tasks like credit card debt reduction and card security updates.
Loan And Credit Decisions
In this era too, banks give leverage to credit history so as to determine creditworthiness. Although, one cannot deny that these credit report traditional systems are often riddled with errors and are not based on real-time data. But if the banking system incorporates an AI-based loan and credit system, it can very well look into the patterns of customers with limited credit history to determine their creditworthiness.
Tracking Market Trends
AI also helps banks to process huge volumes of data so as to predict the latest market trends, currencies, and stocks. Advanced ML techniques evaluate the market sentiments suggesting investment varieties. AI is not limited to it, but the investors can also know the best time to invest in stocks and makes them aware of any potential risk.
Data Collection And Analysis
Since millions of transactions are recorded every single day by the banking industry so we can clearly imagine the volume of the enormous data which is generated. Structuring and recording without error become impossible. Here is where AI comes to the rescue. The information can also be used to detect fraud or to make credit decisions.
Clients often look for a better banking experience. This generation of customers opens their bank accounts from the comfort of their homes using their mobile. Integration of AI in banking enhances the consumer experience and increases convenience for its clients. AI has also reduced the time taken to record KYC, thereby eliminating errors. Moreover, AI software reduces approval times for features like loan disbursement and accurately captures client data to set up accounts without any error, ensuring a smooth experience for the customers.
Havoc like currency fluctuations, natural disasters, or be its political unrest, all of them has serious impacts on the banking domain. AI-based analytics could give a fair idea and helps to make timely decisions. Moreover, it also evaluates any risky application of a client failing to pay back a loan.
As we are aware of the strict regulatory mode that the banking industry falls into so as to ensure that banking customers are not using banks to perpetrate financial crimes. Although banks maintain an internal compliance team to deal with these serious issues, these processes are time taking and require a lot of investment. The issue becomes serious when the regulations change, and banks need to update their workflows with these regulations constantly. The boon is AI, DL and NLP reads new compliance requirements for FIs and improves their overall decision-making process.
One of the most frequent use cases of AI includes general-purpose semantic and NLP and applied predictive analytics. AI detects correlations in the data, which traditional technology could not. These patterns could indicate untapped sales opportunities and cross-sell opportunities.
RPA algorithms enhance operational efficiency and accuracy by cost reduction by automating time-consuming repetitive tasks. As of today, the banking industry leverages RPA to boost transaction speed and increase its efficiency.
Customers now expect a bank to be there for them whenever they need it – which means being available 24 hours a day, 7 days a week – and they expect their bank to do it at scale. The way banks can do this is with AI. In order to deliver on these customer expectations, banks must first overcome some of their own internal challenges; legacy systems, data silos, asset quality, and limited budgets. As these are just some of the issues that inhibit banks from moving quickly enough to keep up with their customer’s demands, it’s no wonder that many banks have turned toward AI as an enabler of this change – but the question that remains is how?
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