Artificial Intelligence and its growing influence on different industries is one of the most widely discussed topics in the tech and business sectors. In fact, the business value of artificial intelligence in the banking industry is projected to reach $300 billion by 2030 with the projected increase in the number of AI projects that are launched and become commercially deployed in banking sector.
So the question is, What does AI have to offer for the banking sector?
Artificial intelligence enables a machine to mimic human cognition. In simpler words, AI enables a system to correctly interpret external data, to generate useful insights from it, and to use those insights to achieve specific goals. A banking institution generates a large volume of data which is beyond the limits of humans to efficiently analyze derive actionable insights from it.
This inefficiency wide opens the doors of the banking sector to the adoption of AI and data analytics. The adoption of artificial intelligence in the banking industry can influence banking from A-Z, right from delivering better customer experience, cost savings to put a better fight against financial frauds. According to a recent study by Business insider, the aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023. Let’s see how banks can leverage on AI to accelerate their business growth.
When 2 sellers sell the same product, the one who provides the best customer experience wins the market. Like any other business, providing the best customer service is of high priority for the banking industry. Based on a customer’s financial transaction patterns and past interactions, an AI model can predict the customer’s needs and enables banks to customize the products and services by adding personalized features. This leads to constructive customer engagements and builds strong customer relationships. Listing down a few implementable use case of artificial intelligence in the banking industry that can boost the customer experience;
⊕ Auto identifying walk-in customers and greeting them with personalized messages
⊕ Suggesting customized and optimized EMI plans based on past transactions history
⊕ Recommending beneficial financial services based on customer demographics
⊕ Auto face recognition in ATMs for card-free access to offered services
⊕ Assigning the best relationship manager to a customer based on analytical scores
⊕ 24*7 active AI-powered conversational agent to answer any customer queries, etc
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Processes that are rule-based can be automated with the help of AI. Repetitive and mundane tasks such as verifying the authenticity of signature in a cheque, extracting key information from legal documents, etc can be automated using AI. This automation enables employees to engage in more productive works. For example, JPMorgan Chase started using an AI system to extract information from documents and the tasks that collectively took roughly 360,000 labor hours per year were completed in a few seconds by the AI system. Banking Process Automation secures the return-of-investment, reduces operations costs and ensures accurate and quick processing of services. Listing down a few implementable use case of artificial intelligence in the banking industry involving process automation are
⊕ Document analysis and key information extraction
⊕ Credit card validation and approval
⊕ Report inference analysis
Increasing automation poses a problem – reduced loyalty due to less personal contact. However, AI can solve this problem in the form of an intelligent chatbot. Thanks to advancements in Machine Learning and Natural Language Processing and Natural Language Understanding technologies, its possible to create conversational tools that can hold conversations with customers in natural language, decipher questions and provide responses with the right information in a more efficient manner. It helps to avoid the expenditure on human resources since banks can considerably reduce the number of customer care executives. Customer satisfaction will also be enhanced as they can avail of the service at the comfort of their homes without having to visit the bank. Chatbots can be used to announce new offers to the customer such as loans or send alerts to customers if they have any payments due. These conversational tools or virtual assistants can help customers access information such as account activity, routing numbers, bills, transfer money between accounts, schedule an in-person meeting at a branch, locate the nearest branch, etc. It can even educate a user about money management. Bank of America launched an AI-driven virtual assistant in 2018 to serve 25 million mobile banking customers. The capabilities of this AI-driven virtual assistant keeps constantly evolving.
⊕ 24*7 active AI-powered agent who can answer to any customer queries
⊕ Assisting and educating customers to do financial transactions
⊕ Kiosk based AI conversational tool can help walk-in customers with their needs
Detecting and avoiding fraudulent transactions is always a challenge for the banks. But the recent advancements in AI and machine learning will bring a substantial positive impact when it comes to fraud detection. The use of artificial intelligence in the banking industry can help the bank to quickly identify potential fraud. By analyzing spending patterns, geo-location, and customer behavior, AI can detect anomalies in spending and can quickly identify potential fraud and alert the customer. The system can flag the suspicious transaction and can ask for additional information from the user to confirm the authenticity of the transaction or just block the transaction within seconds. This is otherwise not possible since a human cant analyzes hundreds of transactions at a single time. Citibank is already using AI and big data analytics to prevent fraud and monitor potential threats to customers. Listing down a few implementable use case of artificial intelligence in the banking industry for fraud detection,
⊕ Anomaly detection in e-banking, transactions patterns
⊕ Recognizing inconsistencies or inaccuracies in payments
⊕ Fraud detection across multiple banking channels
⊕ Signature forgery detection
⊕ Customer face analysis for fraud detection
AI is becoming a significant tool for the bank to reduce risks. Since AI can analyze enormous amounts of data in a short period, it can provide an accurate report on risk assessments in minutes. By viewing the history of cases, the algorithms can also forecast risks and suggest the banks to take the appropriate action. With its power to predict future scenarios, an AI-based system helps potential investors by predicting market trends and to choose the right funds for their portfolio also by analyzing their salary and spending patterns.
In 2017, NatWest introduced an AI-powered virtual bank teller. It can answer up to 200 questions and human customer service representatives are only needed when it comes to complex and high-impact customer questions. In 2018, overall it has handled about 100,000 conversations a month and NatWest expects this to become 2 million in the coming future.
Finding the best option for financial services depends on numerous parameters such as transaction history, demographics, etc. Manual analysis of these data is very time-consuming. However, an AI model can recommend the most suitable financial services such as investment schemes, credit card plans, and other offers to the customers. The system also enables the bank’s employees to recommend suitable plans to the customers quickly. These quick and personalized experiences can help keep customers happy and loyal.
Monitoring live video from surveillance cameras is a tedious activity that demands continuous attention. The burden falls on the security officials to make quick decisions while watching monotonous videos for hours. This often results in security officials overlook the security threats. Whereas in banking institutions, when employees and security officials are held at gunpoint during a robbery attack, mostly they won’t be able to respond to the situation by pressing a panic button to alert authorities or to engage in other security contingencies. Anomaly detection in video analytics is the process of identifying unexpected items or rare occurrences in video footage. AI-powered anomaly detection enables CCTV systems to mimic human cognition by interpreting information from the video to assist authorities. By implementing intelligent video analytics systems into physical locations like ATMs, banks can deliver fool-proof fraud detection.
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It’s no doubt that AI will play an integral part of the bank’s processes and operations. These are just a few use cases of artificial intelligence in the banking industry, and there are many ways AI is going to drive the banking sector into the future. The technology helps both banks and consumers improve accuracy, speed, and convenience. By 2030, banking and financial service sectors can save 22% in costs. Regardless of whether banks want to improve customer service, provide personalized services, or optimize daily processes, AI-powered technology is something banks should consider.