The smart customer of today needs convenient and safer ways to access, spend, save, and invest money. That is why banks are trying to go the extra mile for an improved customer experience. ATM machines, card payments, mobile banking, etc. exemplify how the banking sector is continuously evolving and believing in technological advancements like never before.
Modern technologies, such as Artificial Intelligence, Blockchain, Robotic Process Automation are bringing disruptive changes to the banking industry. The rise in customer-centricity has brought some amazing use cases of these technologies in the banking and finance sector. In this blog post, we will discuss the groundbreaking applications of Machine Learning (ML) and Artificial Intelligence (AI) in banking software.
1. Conversational AI for Banking
A chatbot is a conversational interface that imitates conversation with a user in natural language. In general, chatbots are the profound use cases of AI technology.
In banking software, bots communicate with thousands of customers, answering their inquiries regarding monetary transactions, new schemes for investment, etc. In addition, chatbots reduce the human workload by being available for the costumes 24/7. According to a survey, a chatbot saves four minutes for each conversation that a chatbot handles.
Chatbots in banking can be utilized for a personalized customer experience. Since the chatbots are made to cognitively learn with experiences, they help in offering personalized suggestions to the customers, checking for unauthorized transactions (based on transaction history), etc.
One of the finest examples of chatbots in banking is for the Bank of America. The bank uses a chatbot to send notifications to the customers, provide transactional details, make recommendations for saving, etc. That way, the bank is enabling its clients to make informed decisions and add value to their money.
ALSO READ: How Banking Can Benefit From Conversational AI
Similarly, voice conversational AI integration with mobile apps is quite prominent and banking applications are making the most of it. Just take an example of the virtual assistant by Apple- Siri.
Siri in iOS 12 was launched with the ability to monitor the user activity and suggests shortcuts based on in-app behaviors. The mobile app for Royal Bank of Canada has integrated Siri with their mobile banking app. The app users can simply ask Siri t transfer money, check for account balance, open a bank account, and more. This use case of AI in banking improves the quality of services and offers convenience to the users that are the priority of the users.
2. AI for Safeguarding Banking Data
The banking industry has a huge amount of personal and confidential data. Thus, the security of customer data is of utmost importance for banks.
While the digital era has limited offline visits to the banks, it has opened ways for cybercriminals to hack the personal and monetary information of the customers. For banks, it is imperative to create unhackable systems and in this process, AI has been their partner.
Artificial Intelligence has been a fraud detecting tool. AI can help in analyzing terabytes of data and uncover fraud trends, which otherwise would have been difficult for humans. This subsequently helps in fraud detection in real-time. In the case of a fraud suspect, the ML models at the backend reject transactions or flag them for investigation. These flagged transactions, when investigated, help the models to analyze the transactions and make informed decisions in the future.
3. AI for Compliance Automation in Banking
“A study confirms that more than 51% of financial institutions regulate KYC and AML processes manually.”
Regulations and compliance have a significant role in running a banking system. Artificial Intelligence can help in automating compliance processes, which may include Know Your Customer (KYC), Anti-money Laundering (AML). If we consider, say KYC and AML; these are data-centric processes that help in understanding customer and transactional behavior. If compliance maintenance is done by humans, it would be a time-taking process and might delay service delivery at the customer end.
4. AI for Financial Forecasting in Banks
Loans and interest received from them are the major source of revenue for banks. AI can help in automating credit risk analysis for banks. By evaluating a customer’s financial history, recent transactions, and purchasing patterns, a machine learning model can forecast their future spendings. This assessment can enable banks to offer the best possible terms for loans and credit products. Over time, the forecasting algorithms learn from past experiences and provide more accurate predictions.
AI in Banking Software: Challenges Involved
In adopting and executing Artificial Intelligence technology in banking solutions, there are a few challenges involved:
Daffodil software has been an early adopter of AI technology. With the experience to deliver several AI-based software solutions, it has entered into the banking and finance sector to deliver secure, automated, and performance-oriented solutions.
You can check out how our team helped the Reserve Bank of India (RBI) to develop an AI-based mobile app to aid the visually impaired to identify the denomination of Indian currency notes. Read more…
To know how Daffodil can help your financial institution make the most of AI technology, connect with our fintech experts through our no-obligation, free consultation session.