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AI And the Future of Banking

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AI And the Future of Banking

Recently, we have seen an enormous transition in the banking sector, which has been propelled even further by the current epidemic, from traditional banking practices to digital banking and Flexcube implementation. Banking data has become richer as a result of widespread digital adoption by banks and their clients. It is possible to harness the value of this data to propel innovation farther and more quickly than ever before with the use of artificial intelligence. According to research, banks have recognized the power of artificial intelligence to extract value from data, with many banks and financial services organizations considering investing in AI to apply across a range of critical functions, such as customer service, marketing automation, security, and operational efficiency, among other things.

Outcomes Expected from AI

Using AI to lower costs, reduce errors, and optimize resources in use cases such as digital engagement and problem-solving can assist banks in achieving profitable growth. Examples of such applications include augmenting the workforce by automating repetitive processes and reducing costs through cost reduction. For example, an American company employed artificial intelligence in credit analysis to generate a 30% boost in mortgage collections. A bank in the United States uses artificial intelligence to cut the length of the compliance procedure by 80 percent. Conversational artificial intelligence was employed by another bank to handle 30% of ordinary customer care calls. In general, artificial intelligence may assist banks in achieving the interconnected goals listed below:

  • Increased Efficiency and Operational Excellence: Operations efficiency can be improved across the board by implementing AI-based automation projects. This is true whether the goal is to automate end-to-end business processes or reduce financial risks and fraud.
  • Customer Personalization and Improved Experience: In an age of escalating consumer expectations and increased competition, artificial intelligence assists banks in leveraging diverse data sets to provide valuable insights that can be used to build engaging client goods and services.
  • Compliance and Risk Management: In this digital era, artificial intelligence gives the capacity to evaluate and find important data for simplifying credit choices and risk reduction, cybersecurity, and compliance adherence, therefore assisting in the development of consumer trust.
  • Revenue Growth: Artificial intelligence can study the behavioral economics that underpins consumers’ financial actions, allowing banks to uncover possible new income streams that are matched with client wants.

AI Use Cases

Leading banks worldwide are considering an enterprise-wide AI strategy that cuts across front-office, middle-office, and back-office functions and identifies, ideates, and implements use cases across these layers.

Today’s customers are asking for goods and services that are tailored to their specific needs and circumstances. Customer sentiment and behavior analysis using artificial intelligence may assist banks in personalizing their customer offers. With customer and product data, it may help agents become trusted consultants in their respective fields of expertise. To free up human agents’ time to handle increasingly complicated inquiries, banks are using conversational AIin the form of chatbots.

In intelligent payment routing, Flexcube artificial intelligence may save both money and time. At the same time, credit processing can be enhanced from scoring through the collection, resulting in lower default risks.

As an example, banks are utilizing artificial intelligence to generate real-time early action-based alerts in the areas of fraud analytics and financial crime prevention.

Ensuring Successful AI Implementation

Although it has a plethora of advantages, artificial intelligence poses significant hurdles that may prevent certain financial institutions from using the technology on a large scale. According to a recent study, 46 percent of banks and financial services businesses in the United States are experiencing a shortage of experienced artificial intelligence (AI) personnel. Banks encounter several obstacles to integrating AI, including a lack of an enterprise-wide AI strategy, lack of management support, a scarcity of relevant use cases, and an inability to incorporate artificial intelligence into the organization’s culture at large. The following strategy for artificial intelligence adoption is thus recommended for banks:

By developing an enterprise-wide artificial intelligence strategy, banks will be able to address siloed procedures while also reaping advantages across units and throughout the business. One of the most critical components of adopting artificial intelligence is to start with internal stakeholder alignment and management buy-in from the top down. Business and information technology groups must come to terms with shared goals and then collaborate on establishing the skill sets and corporate culture that will enable them to be achieved. It is critical to ensure that all processes and transactions are examined through an artificial intelligence lens in order to gain the most significant advantage from AI adoption throughout the enterprise. Artificial intelligence should be a vital component of any digital transformation plan.

What Are The Common Challenges Banks Might Face In Implementing AI?

It is not always simple to integrate artificial intelligence technologies into financial operations. You must make sure that you have the appropriate staff and skills. In addition, you will need access to data, financial means to invest in the project, and parties who are prepared to accept new technologies.

  • Access to data: Implementing artificial intelligence is one of the most challenging difficulties. Aside from that, banks may encounter difficulties in the preparation of training data. Updating or improving AI models becomes difficult without access to the required data and knowledge to utilize and learn from.
  • Localization: When it comes to banking, localization is crucial since they often need to build models that can be used in numerous areas. When it comes to customizing the consumer experience, localization can assist you. An experienced linguist from your data partner may help you with localization by developing elements like style guidelines and voice personas for you.
  • Security and compliance: It’s challenging to keep all of the information private and safe. Many security alternatives may be provided by a suitable data partner. They have security measures in place to guarantee the safety of the information you provide to your clients. Consider only working with data partners that are regulated and certified to secure your personal information. Using their services, you will be able to safely annotate files. On-site service options, private cloud deployments, and on-premise deployments will also be available.
  • Trust, transparency, and 6: Customers must understand and trust AI models for them to be effective since they will want to know that their personal information is safe and secure. To better understand the model, have a discussion with your spouse. Alternately, you may always go back to the original training data and see if there is anything that makes sense.
  • Data pipelines: It’s not as simple as it sounds to connect data pipeline components so that segregated data may be used. The suitable collection and structuring of bank data are critical to this process’ success. They also need to make sure that this data can be used to build ML models that can accurately anticipate business outcomes. As your financial service firm grows, you’ll want to work with a partner with a wide range of security offerings.


JMR Infotech helps improve the openness and explainability of your banking procedures by incorporating artificial intelligence and Oracle Flexcube into the decision-making process. APIs supplied on-premise, in the cloud, or as a SaaS solution may give a simple-to-use interface for our customers.

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