Artificial Intelligence in Banking: Top 5 Solutions

Artificial Intelligence is quickly becoming a new standard for the banking industry worldwide. Though not organizations are yet ready to face the changes, experts state that the technological transformation for the banking industry is inevitable. Customers expect personalized and omnichannel services while banks have to keep up with tech giants who decide to enter the fintech sector. 

Hence, the deployment of artificial intelligence in banking is the only way for financial institutions to remain competitive, maintain a high level of services, and bring more efficiency to current processes. Below, we will have a look at the five most common AI-powered solutions that banks can already start using and the overall benefits of AI for banking.

What benefits does AI bring to banking?

You might have heard that AI adds accuracy, speed, and efficiency to processes but these are very vague descriptions. If you need more definite benefits, here are the biggest ones:

• Personalization via virtual assistants, personalized recommendations, and smart money management solutions;
• Better data management and storage due to automation and application of ML to data processing;
• Risk mitigation and risk management due to predictive analytics.

If we drill down, these benefits can be broken down into smaller ones, depending on the area of AI deployment. It is also important to state that AI benefits all areas of banking, including the front office, middle office, and back office. Therefore, artificial intelligence in banking does not only improve the processes – it takes them to a brand new level. And now let’s talk about the most common AI-powered solutions.

AI for banking: most common solutions

While AI can benefit the banking industry in many ways, we will list the most popular solutions that can be already implemented by banks. It will still take some time and effort to properly design and implement these solutions but they will reward you with increased customer satisfaction and a significant boost in the efficiency of processes.

Personal assistants and personalized recommendations

Personalization plays a critical role in any business and numerous statistics prove that. Customers now expect all businesses to provide them personalized services and banking is no exception. Hence, personalization in banking comes mainly in the form of personal virtual assistants and personalized recommendations.

Smart virtual assistants are AI-powered chatbots that are capable of the following:

• Recognizing one’s spending behaviour and suggesting certain actions based on that;
• Providing the requested information to users;
• Communicating with users and assisting them in decision-making.

These bots act as a liaison between users and the bank and significantly facilitate the majority of processes for customers. By instantly providing the needed information and guiding the user through processes, smart chatbots boost user experience and increase the level of trust from the customers’ side.

As for personalized recommendations, the use of artificial intelligence in banking can help learn about the online behavior of their clients and use this information to provide relevant and timely offers. In this way, customers can rest assured that all the offers they receive will be useful to them and that the company does not do “spam emailing”.

Fraud mitigation

As banks went digital, fraudulent behaviour has become one of the most severe problems. The costs of lost data can be incredibly massive and any case of fraud hurts not only the bank but disrupts customers’ loyalty as well.

Artificial intelligence can help banks resolve the issue with the help of machine learning technology. ML can recognize any warning and potentially fraudulent behaviour online and act correspondingly: i.e. automatically warn system owners or take preventative measures. As well, AI can be successfully used in lending as it helps evaluate the credibility of borrowers and contribute to decision making.

Data security

Related to the point above, data security is another critical aspect to pay attention to. It depends not only on the mitigation and prevention of fraudulent behaviour but also on the quality of data storage and its security. 

By using artificial intelligence in banking, financial institutions can significantly enhance the security of their data by applying biometric authentication, organizing and processing their data in a smarter and more structured way, and “cleaning the data” with the help of machine learning. In this way, any potentially warning behaviour can be easily detected and the AI-powered system can independently take action in order to protect the data and notify the specialists about the threat.

Omnichannel services

Mobile banking has become incredibly popular especially in the light of the COVID-19 pandemic. And while the growth of mobile banking is itself a big step towards the transformation of the banking industry, things get even better if you add AI to it.

AI plays a big role in mobile banking apps as it offers the following:

• Personalized planning;
• Smart notifications;
• Biometric authentication;
• Virtual assistants (similar to Siri or Alexa).

And obviously, the introduction of a mobile application allows banks to embrace omnichannel services and ensure the customers can receive the same quality of service both offline and online.

Smart document processing

The banking industry has always been dealing with an overwhelming amount of documentation stored in physical format. This includes scanned and printed documents, handwritten documents, and images. With the help of optical character recognition and computer vision, banks can take their document processing to the next level.

OCR and computer vision share the same purpose: to “translate” the text on physical documents and transfer it to electronic format. Now imagine how much storage space you can clean by transferring all your paper documents into cloud storage. Not only does electronic format contribute to better-organized storage but it also brings higher document security and allows a much more convenient and better document organization and management.

Main considerations about AI implementation

Artificial intelligence is quickly becoming a must-have for any business and banks have to take action now in order to remain competitive and avoid being left behind. However, there are several challenges that prevent the implementation of artificial intelligence in banking:

• Lack of technical skills and technology systems;
• Lack of strategy (or poor strategy for AI implementation);
• Lack of flexibility needed for AI implementation;
• Lack of professional personnel.

The main challenge, though, lies in creating a uniform and holistic strategy that would cover not several but all processes within an organization, from front to back offices. According to McKinsey, banks need to review the three layers of their operations in order to become AI-first.

Customer engagement

The first layer involves customer engagement and means reimagining the end-to-end process of the customer journey. Banks need to offer their clients the following:

• Seamless movement between various points of interaction: physical branches, mobile apps, call centers, websites. The interactions with each point of contact should be frictionless and a customer should be able to freely move between them.
• Integration with non-financial services such as messengers. By allowing certain banking services on third-party apps, banks not only significantly expand their digital presence but provide a holistic experience to users.
• Smart and personalized recommendations that resolve customers’ needs and provide valuable advice. This can include building forecasts for future spendings and smart management of finances.

Decision-making

This layer involves a majority of processes that help a bank make decisions on a daily basis (i.e. document management or customer acquisition). By bringing AI to the decision-making layer, banks can significantly improve their processes with the help of such tools as computer vision, machine learning, robotic process automation.

Banks will also have to reshape and improve such processes as code management or sharing knowledge across the team. The main goal here is to streamline, improve, automate, and reorganize all the processes so a bank can seamlessly implement AI.

Technology and data infrastructure

As we already said, the lack of needed technologies and tech knowledge is one of the biggest challenges for implementing AI in banking. Hence, banks have to adopt the tech-forward strategy and create a modern, scalable, and flexible tech infrastructure within the organization. As well, banks will have to reorganize their data management to ensure a high level of security and organization in order to protect the data from threats. And don’t forget about hiring skilled and experienced employees that will be responsible for handling new processes and teaching others.

Conclusion

While it will take quite a significant amount of time to become fully AI-first, banks need to understand that the benefits in the long run will surely outweigh the challenges that they might face in the next few years. Therefore, it is highly recommended to pay attention to either existing AI software solutions or to request custom development in order to make the first step towards embracing AI technology