AI in Banking
The introduction of artificial intelligence in banking apps and services has made the banking sector more customer-centric and technologically relevant.
AI-based systems can help banks to reduce costs by increasing productivity and making decisions based on information unfathomable to a human agent.
The banking and finance sectors are swiftly moving towards AI, which improves service efficiency, productivity and reduces costs.
Here are some major AI applications in the banking industry through which you can reap the numerous benefits of the technology.
- Cybersecurity and fraud detection
- Chatbots
- Loan and credit decisions
- Tracking market trends
- Data collection and analysis
- Customer experience
- Risk management
- Regulatory compliance
- Predictive analytics
- Process automation
Cybersecurity and fraud detection
Every day, huge number of digital transactions take place as users pay bills
withdraw money deposit checks and do a lot more via apps or online accounts
Thus, there is need for the banking sector to ramp up its cybersecurity and fraud detection efforts.
AI can help banks to improve the security of online finance, track the loopholes in their systems, and minimize risks.
AI along with machine learning can easily identify fraudulent activities and sends alerts to customers as well as banks.
Chatbots
Undoubtedly, chatbots are one of the best examples of practical applications of artificial intelligence in banking. Once deployed, they can work 24*7, unlike humans who have fixed working hours.
Additionally, they keep on learning about the usage pattern of a particular customer. It helps them understand the requirements of a user in an efficient manner.
By integrating chatbots into banking apps, banks can ensure that they are available for their customers round the clock. Moreover, by understanding customer behaviour, chatbots are able to offer personalized customer support and recommend suitable financial services and products accordingly.
Loan and credit decisions
Banks have started incorporating AI-based systems to make more informed, safer, and profitable loan and credit decisions.
An AI-based loan and credit system can look into the behaviour and patterns of customers with limited credit history to determine their creditworthiness. Also, the system sends warnings to banks about specific behaviours that may increase the chances of default. In short, such technologies are playing a key role in changing the future of consumer lending.
Tracking market trends
Artificial intelligence in financial services helps banks to process large volumes of data and predict the latest market trends, currencies, and stocks. Advanced machine learning techniques help evaluate market sentiments and suggest investment options.
AI for banking also suggests the best time to invest in stocks and warns when there is a potential risk. Due to its high data processing capacity, this emerging technology also helps speed up decision-making and makes trading convenient for both banks and their clients.
Customer experience
Customers are constantly looking for a better experience and convenience. For example, ATMs were a success because customers could avail essential services of depositing and withdrawing money even when banks were closed. Customers can now open bank accounts from the comfort of their homes using their smartphones. Integrating artificial intelligence in banking and finance services will further enhance consumer experience and increase the level of convenience for users. AI technology reduces the time taken to record Know Your Customer (KYC) information and eliminates errors. Additionally, new products and financial offers can be released on time. Eligibility for cases such as applying for a personal loan or credit gets automated using AI, which means clients can eliminate the hassle of going through the entire process manually. In addition, AI-based software can reduce approval times for facilities such as loan disbursement. AI banking also helps to accurately capture client information to set up accounts without any error, ensuring a smooth experience for the customers.
Risk management
External global factors such as currency fluctuations, natural disasters, or political unrest have serious impacts on banking and financial industries. During such volatile times, it’s crucial to take business decisions extra cautiously. AI-driven analytics can give a reasonably clear picture of what is to come and help you stay prepared and make timely decisions.
AI also helps find risky applications by evaluating the probability of a client failing to pay back a loan. It
predicts this future behavior by analyzing past behavioral patterns and smartphone data.
Regulatory compliance
Banking is one of the highly regulated sectors of the economy worldwide. Governments use their regulatory authority to ensure that banking customers are not using banks to perpetrate financial crimes and that banks have acceptable risk profiles to avoid large-scale defaults.
sAI uses deep learning and NLP to read new compliance requirements for financial institutions and improve their decision-making process. Even though AI banking can’t replace a compliance analyst, it can make their operations faster and more efficient.
Predictive analytics
One of the most common use cases of AI includes general-purpose semantic and natural language applications and broadly applied predictive analytics. AI can detect specific patterns and correlations in the data, which traditional technology could not previously detect.
These patterns could indicate untapped sales opportunities, cross-sell opportunities, or even metrics around operational data, leading to a direct revenue impact.
Process automation
Robotic process automation (RPA) algorithms increase operational efficiency and accuracy and reduce costs by automating time-consuming repetitive tasks. This also allows users to focus on more complex processes requiring human involvement. As of today, banking institutions successfully leverage RPA to boost transaction speed and increase efficiency.
Banks are in a race to become AI-first, and that too for a good reason. For many years, the banking industry has been working on transforming itself from a people-centric business to a customer-centric one. This shift has forced banks to take a more holistic approach to meet their customers’ demands and expectations. With their focus now on the customer, banks must begin thinking about how to serve them better. 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.
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