We have seen the transformative impact of Artificial intelligence(AI) and Machine Learning(ML) from day one and how enterprises have expanded their business, including the banking and finance sector. Most enterprises are just experimenting with AI, but only a few of them have benefited from advanced AI, which will bring strong competition in the market in the coming years.
However, introducing AI in the banking sector can change various aspects, from applications to customer services. It’s not too late; the global banking market will experience the benefits in the coming years by doing so.
Old Approach vs AI/ML Approach in Banking
If we look back at those days when customers had to face various challenges such as fraud activities, too many days in loan approval, long queues to get a response to the quarry, and many other disadvantages, AI and ML technologies are far better than old banking approach and are more secure, faster, prevent from fraud activities, better customer services and many more. Let’s look at how AI and ML impact the banking sector.
Applications of AI and ML in Banking
Fraud Detection and Prevention
Most of the financial sectors, are victims of fraud activities and have to face losses in various aspects. The integration of AI and ML can stop this; it can prevent such fraudulent activities and detect them before such things happen.
Customer Service and Chatbots
The financial sectors, mostly lose their customers because of poor customer service, and that leads to the business’s downfall. AI and ML play a vital role in improving customer service. The chatbot will be available 24/7, providing services to the customers, which will increase brand trust and increase new customers.
Earlier, knowing credit scoring was like standing in a queue at an ATM, but with the planting of AI and ML services, things will go smoothly by analyzing various data points, including transaction history, income sources, and spending patterns, and this will help in knowing credit score in no time. Banks will get data on loans that are paid or not, which makes it easy and allows banks and customers to maintain credit scoring.
Banks often take a long time in the Anti-Money Laundering and KYC process, which leads to delays in the overall process and customers get irritated. AI and ML help in AML and KYC processes, where ML algorithms can identify suspicious activities and ensure the provision of accurate and efficient processes.
With the traditional Process, banks often find potential risks in loan complications due to fake documents. ML models may assist in assessing large datasets and allow banks to evaluate customers, credit history, tax payments, salary, and daily transactions. This creditworthiness of loan applicants will be more accurate and efficient and allow banks to stay away from risks.
Voice and Facial Recognition
AI and ML technology provide biometric and facial recognition systems to make security robust in banking services. This will allow extra protection for customer data and transitions, compared to traditional security, which is less secure than AI and ML security processes.
Real-world examples of AI and ML in Banking Industry
JPMorgan Chase: The researchers at JPMorgenChase have leveraged AI and Deep learning techniques for risk management and trading strategies.
Bank of America is one of the great examples of personal banking, which has used AI, ML, and Deep learning to prevent credit card frauds and developed virtual assistance named “Erica.
HSBC: has leveraged AI, Pytourch, and ML in resolving customers’ quarries with the help of chatbot assistance in both web and mobile banking.
Challenges can be faced by adopting AI and ML in Banking.
Data Quality and Privacy Concerns
The banking industry collects a lot of data on a daily basis, which requires security measures as AI and ML depend on high-quality data, whereas poor-quality data can lead to inaccuracy and security breaches or violations. Therefore, it is good to connect with the best AI development company that understands both AI/ML and always ready to provide solutions related to data quality and privacy concerns.
Integration with Legacy Systems
Mostly, every banking sector is working on legacy systems or legacy technologies, which might be less costly and less effective, but Integration of AI and ML technologies with legacy systems can be time-consuming and costly, and small banks won’t be able to afford it.
Lack of Explainability
Banks need to take a look at quality data and its structure. Sometimes, AI models can be complex to understand, and it can be challenging for banks to explain the Process. Therefore, AI developers play a crucial role in developing the Process and modifying the data to mitigate all privacy and compliance risks.
Be the AI-First Banking Sector in the World
Currently, all the banking sectors aim to become the first AI bank, which is really good, and becoming the one will help the bank’s overall structure. In the early days, banks faced difficulties in effectively transforming their structure, but becoming a first bank can promote a holistic approach to providing effective services to its customers and fulfilling their demands and expectations.
Becoming a first bank, your customers will find better customer service, which will be available 24/7, and they will find every solution related to banking through AI-based chatbots, and banks can avail of this through AI only.
- Customer-Centric Transformation
- Effective Solution for Internal Challenges
- Personalized Financial Planning
- Automation of Routine Transactions
- Continuous Learning and Adaptation
- Get a chance to collaborate with FinTech Innovators
How you can Become the AI-First Banking Sector in the World
- Build AI-based Strategy
- Plan to include AI use cases in Process
- Develop and Deploy
- Operate and Monitor
- The Sucess
Future of Banking Sector with AI. Is it Safe?
There is no doubt that AI is the best technology that revolves around every industry, and the banking sector is not exceptional. If we look at the future of the banking sector, it looks totally safe. Artificial intelligence has numerous benefits, but a few challenges need to be addressed. Therefore, connect with the best AI development company and Hire AI developers, who will help you to become the first AI bank and provide effective solutions to the challenges of AI and ML.
Integrating AI and ML in the banking sector finds a prominent advantage, where the sector will find opportunities to enhance its various operations, from automating tasks to improving customer services.
AI aids in better decision-making, which will help in better strategy planning to prevent fraud activities. Overall, the Banking industry will see tremendous changes by accepting AI and ML in the development process and finding a competitive edge in the financial landscape.
Chandresh Patel is a CEO, Agile coach, and founder of Bacancy Technology. His truly entrepreneurial spirit, skillful expertise, and extensive knowledge in Agile software development services have helped the organization to achieve new heights of success. Chandresh is fronting the organization into global markets in a systematic, innovative, and collaborative way to fulfill custom software development needs and provide optimum quality.