As transformative impact has been profound since its advent, changing how enterprises, including those in the banking and finance sector, operate and deliver services to customers. The introduction of AI in banking apps and services has made the sector more customer-centric and technologically relevant.
A report by Business Insider suggests that nearly 80% of banks are aware of the potential benefits of AI in banking. Another report by McKinsey suggests the potential of AI in banking and finance would grow as high as $1 trillion.
In this blog, we will discover the key applications of AI in banking and finance sector and will also look at how this technology is redefining customer experience with its exceptional benefits.
Applications of AI in Banking and Finance
Artificial intelligence has become an integral part of our world, and banks have already started integrating this technology into their products and services. Here are some major AI applications in the banking industry:
Cybersecurity and Fraud Detection
Several digital transactions occur daily as users pay bills, withdraw money, deposit checks, and do much more via apps or online accounts. Thus, there is an increasing need for the banking sector to ramp up its fraud detection efforts.
Chatbots
Chatbots are one of the best examples of practical applications of artificial intelligence in banking. Once deployed, they work 24*7, unlike humans with fixed working hours.
Additionally, they keep learning about a particular customer’s usage pattern. It helps them understand the requirements of a user efficiently.
Loan and Credit Decisions
Banks have started incorporating AI-based systems to make more informed, safer, and profitable loan and credit decisions. Currently, many banks are still too confined to the use of credit history, credit scores, and customer references to determine the creditworthiness of an individual or company.
Loan and Credit Decisions
Banks have started incorporating AI-based systems to make more informed, safer, and profitable loan and credit decisions. Currently, many banks are still too confined to the use of credit history, credit scores, and customer references to determine the creditworthiness of an individual or company.
Tracking Market Trends
AI-ML in financial services helps banks to process large volumes of data and predict the latest market trends. Advanced machine learning techniques help evaluate market sentiments and suggest investment options.
Data Collection and Analysis
Banking and finance institutions record millions of transactions every single day. Since the volume of information generated is enormous, its collection and registration become overwhelming for employees. Structuring and recording such a huge amount of data without any error becomes impossible.
Risk Management
External global factors such as currency fluctuations, natural disasters, or political unrest seriously impact the banking and financial industries. During such volatile times, taking business decisions extra cautiously is crucial. Generative AI in banking offers analytics that gives a reasonably clear picture of what is to come and helps you stay prepared and make timely decisions.
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.
Predictive Analytics
One of the most common use cases of AI in the banking industry 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.
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.
