In recent times, every industry has benefited from technology, and the banking sector is no exception. There is currently an ongoing digital revolution within the banking sector. It is increasing the accuracy and reliability of financial services. While artificial intelligence has been around for a while, it is only now that different organizations are leveraging it. AI disrupts existing systems and ensures better, optimized processes. The digital revolution has increased the use of cyber security and compliance services and instant verification through real-time analysis.

Application of AI in Banking

Artificial intelligence has various applications in the banking sector. From securing services to ensuring that all banking processes are streamlined, banks can benefit from the implementation of AI. Here are some of the ways in which banks can utilize AI solutions.

• Better Customer Support

AI can improve customer service in banks and similar financial organizations through chatbots. Chatbots are cost-effective and accessible, and the use of chatbots reduces the load on internal systems and contact centers. Clients can access chatbots for generating mini statements, balance inquiries, and fund transfers without having to contact the teams in place, which can then concentrate on other crucial tasks.

• Cybersecurity

Cybersecurity is crucial for banks as they deal with a lot of money and sensitive information. Banks usually outsource cybersecurity and utilize the best cyber security service companies. These companies implement AI to leverage data from previous threats. Insights from such data are used to determine patterns and eliminate potential threats before they affect the entire system, potentially leading to theft or fraud. Through constant monitoring, AI solutions can also suggest various ways of securing the system against third-party attacks.

• Robo-Advisors

AI is primarily responsible for automation. This can be done in the form of digital or Robo advisors. While the authenticity of Robo-advisors has been contested as controversial, banking organizations are trying to process multiple customer requests within a short period of time. Therefore, automated advice can be of help. Robo-advisors usually provide guidance based on financial and historical data. They can also provide information on equity and investments.

• Improved Credit Scoring

When clients approach a bank for loans or any other financial services, the organization needs to check their creditworthiness. This is usually done by obtaining data on the client from various sources. Data analysis is not possible for the human workforce, and such interventions can lead to increased labor costs and excess time. Therefore, automation is necessary. AI solutions can automate the analysis of data to improve credit scoring. Such processes will not only provide accurate information and insights but will also help the bank protect its money and sensitive information.

Similar tasks are possible through the implementation of predictive analytics. It has a significant role in the AI revolution that is being witnessed in the banking sector.

Predictive Analytics in the Banking Sector

Like every other organization across industries, banks must process vast volumes of data at any given instance. However, such data can be analyzed to obtain insights that will help improve various banking processes. But it is not possible to do so manually. This is where predictive analytics comes in. The application of predictive analytics on collected data is an essential aspect of AI. Therefore, a significant part of the digital revolution involves the implementation of predictive analytics. It helps in the following aspects:

• Easier Management of Risks

One of the most prominent applications of predictive analytics is in risk analysis and management. In the financial sector, particularly in banking, there is always a risk involved. Therefore, it is vital to manage it and work towards minimizing it. Predictive analytics deals with different data points and reviews customer relationships to determine accurate credit scoring. This helps the bank understand the degree of risk they may have to deal with once a certain customer avails the service. Through analyzing customer behavior and other patterns, predictive analysis can allow banks to make correct decisions regarding how appropriate a customer is for a specific financial service like insurance.

• Prevention of Fraud

Every bank deals with a significant amount of money and personal information. Therefore, it is at risk of third-party attacks. Banks can avoid fraud or theft of money or user information through predictive analytics. When AI is used for predictive analysis, it combines real-time data analysis with tools for model identification. This minimizes identity theft. There may be specific threats that internal operations teams cannot detect, affecting entire systems. MDR or managed detection and response services, are necessary to deal with such threats. Predictive analysis insights can be used to understand if the bank needs to outsource cybersecurity. Predictive analytics can determine which parts of the system are particularly vulnerable and whether there is a need for cyber security MDR services. Such solutions help prevent fraud, and the bank saves considerable revenue as it does not have to cover any loss due to theft.

• Instant Verification

Before providing any financial service, banks need to verify the information provided by clients. During this verification, they can determine if a high risk is involved that could lead to fraud or theft. However, the manual process can be time-consuming, and thus predictive analysis is necessary. The application of predictive analytics helps create systems that automatically verify and detect vulnerabilities within the system. If the same tools are applied to verification models, banks can check instantly if the client is authentic. Predictive analytics also helps create secure, self-reliant solutions for clients. These solutions constantly monitor user activity as clients continue to access the system and use banking services at any time of the day.

Conclusion

IT infrastructures are present in every sector, including banking. IT automation with AI is a part of the digital/ AI revolution. Banks and other financial organizations can decrease costs and ensure error-free processes through automation. Such solutions lead to secure services protected from fraud and are also cost-effective. The AI revolution is focused on changing existing, time-consuming processes with quick, automated systems that can create space for better services and increase customer satisfaction.