Top 7 Use Cases of AI For Banks

Top 7 Use Cases of AI For Banks

As a bank customer, does it annoy you to stand in line for hours to extend the expiration date of the passbook? Most people do their best to avoid unnecessary trips to the bank. In the modern world, you no longer need to leave your home to resolve various issues; many procedures are available virtually.

Automation has dramatically facilitated the lives of customers in the banking industry. Most routine activities can be optimized using conversational AI. According to a recent report by Business Insider, about 80% of companies are adopting chatbots in one way or another. Advanced AI-ruled digital assistants help automate up to 90% of all incoming requests to financial institutions. Let’s talk about the most popular AI use cases in banks.

The most famous examples of applying AI in banking

The Business Wire report says that by 2024, banking will be one of the two areas that spend the most on implementing and maintaining AI-ruled products. Many banking processes follow a given algorithm, and artificial intelligence loves to deal with routine. Advanced AI includes a large set of technologies such as:

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  • machine learning,
  • natural language processing,
  • expert systems,
  • computer vision,
  • robotics, etc.

AI algorithms perform anti-money laundering actions in a few seconds; otherwise, it takes hours and days. Digitalization allows banks to manage a large amount of information in record time to extract valuable data. Let’s look at the options for using AI in banking operations.

Implementation of chatbots

One of the enormous benefits of using AI for banks is the construction of conversational assistants or chatbots. Advanced bots are becoming the preferred customer support platform. It is helpful for financial service providers as virtual assistants facilitate two-way communication with machines using natural language commands. Other benefits of virtual agents:

  • Availability of chatbots: smart financial assistants, unlike employees, are available around the clock and seven days a week. Customers find it convenient to use such software to answer questions and solve simple banking tasks that previously involved face-to-face interaction.
  • Presale support: banks increasingly use their chatbots to inform customers about additional services and offers. Business customers may not be aware of financial solutions to help with payments and loans. With predictive analytics and machine learning, chatbots (and live agents) can make the correct suggestions on the proper devices in real time.
  • Increasing employee productivity: chatbots can save a lot of employee time, which they previously spent answering questions and providing assistance. Thanks to this, employees do not get tired of repetitive tasks. As a result, their efficiency grows, and they become more productive.

Any bank can determine the ROI of a chatbot by conducting a survey, improving customer experience, and feeling how satisfied they are. However, there is one condition technical experts recommend. Suppose a bank plans to implement a chatbot. In that case, it should initially test it in-house to understand the reliability and compatibility of the system with financial services before scaling it up.

Top 7 Use Cases of AI For Banks

Automation of investment processes

Some advanced banks use innovative platforms to make investment decisions. Well-known organizations such as the Dutch ING and the Swiss UCS are using artificial intelligence systems to find untapped investment opportunities in the market and feed this information to algorithmic trading systems. While people still doubt the rationality of such investment decisions, systems AI for banks opens up additional opportunities through simulation and research.

Many financial institutions offer robo-advisers to help customers manage their portfolios. These robots provide quality investment advice through personalization, chatbots, and client-specific models.

Fraud detection

Another expensive area in banking operations is fraud. Since more and more financial transactions are taking place remotely, this contributes to the emergence of new types of fraud. AI for banks can detect and prevent fraud by analyzing customer behavior in real time to determine which actions are not consistent with normal behavior.

Machine learning algorithms can detect various fraud patterns in financial transactions:

  • Collective anomalies, e.g., two parallel transactions from the same account that occur in different locations, are identified by the system as fraudulent transactions.
  • Conditional anomalies, such as multiple withdrawal attempts within a short period.
  • Spot anomalies, such as large incoming transfers to a user account that typically receives smaller amounts.

Manual verification of customers is still crucial, but algorithms effectively complement it by choosing anomalies the expert should check in the following stages. Thus, financial institutions that use AI-ruled applications can make fraud detection operations more efficient, reducing the risk of human error.

Risk management

External international factors, including sharp fluctuations in the exchange rate, wars, and natural disasters, significantly impact the banking area. During unstable periods, it is vital to make any business decisions carefully. AI-based analytics gives you an accurate idea of ​​what events are expected in the future, allows specialists to be prepared for any troubles, and takes timely action.

Artificial intelligence also helps find risky decisions by assessing the likelihood the customer will be able to pay off their loan obligations. It makes predictions about the relative behavior of the borrower, given his past behavior patterns and personal information.

Regulatory compliance

Banking is highly regulated in any country. The government is making the most of its powers to guarantee customers do not use financial institutions to manipulate illegal money. Banks have specific risk profiles that will help avoid a massive default.

Most financial organizations have an internal department that monitors compliance, but these processes require significant time and a massive investment if done manually. The government frequently amends laws, and banks must constantly update their operations according to accepted rules.

Platforms AI for banks uses deep learning and NLP to study new terms and quickly start meeting them. Although artificial intelligence cannot replace the compliance analyst, it allows many processes to be completed instantly and more productively.

Dealing with loans and credit decisions

Banks use AI-ruled systems to make the most reasonable, profitable, and safe decisions on issuing loans. Today, many financial institutions are too limited by using credit ratings, credit histories, and reviews to identify customers’ creditworthiness.

Existing credit reporting technologies are imperfect and often contain errors, lack a complete history of monetary transactions, and misclassification of creditors. Financial platforms based on AI and machine learning algorithms will be a beneficial addition to such systems. They analyze the behavior pattern of each person and allow you to determine whether an applicant with a limited credit history can become a conscientious payer. AI applications try to find clients whose behavior pattern increases the chances of a return on capital by default.

How does AI change the banking industry?

Adding AI in banking is primarily driven by demand and customer needs. Accenture surveyed bank clients to understand their ever-changing needs better. It turned out that 71% of respondents prefer computerized customer support, while 78% of people who took part in the survey are ready to use automated investment support. There are many other ways AI for banks change our life:

  • Artificial intelligence optimizes banks’ internal and external processes, reducing spending, increasing productivity, and guaranteeing security.
  • Banks may harness the power of AI to assess credit risk by replacing standard calculations with machine learning methods to identify patterns the human eye misses. In this way, the customer quickly receives borrowed capital, and the lender decreases the time spent on manual verification.
  • Financial institutions can add AI products to their mobile banking apps or websites. It allows you to strengthen relationships with customers, increase their satisfaction with the service and attract attention through personalized communication, which positively affects sales. All this, in parallel, reduces maintenance costs through automation.

Banks are harnessing the power of AI for a comprehensive transformation that spans multiple levels, including operations, marketing, customer support, risk mitigation, and compliance. Artificial intelligence transforms standard processes into scalable, flexible functions. Financial institutions with AI offer personalized services and modern and appropriate products for each client.

Top 7 Use Cases of AI For Banks

Some steps to become AI-first bank

Now that we have understood how AI can be used in the banking sphere to achieve optimal results, it’s time to discuss how financial institutions should implement digitalization effectively. There are four key factors to pay close attention to: people, financial management, process, and technology. The algorithm of actions is described below:

  • Think of an AI tactic: carefully research the market to find gaps in people and procedures that AI tools may fill. Make sure the tactics you choose meet industry standards and norms. It is essential to clarify the internal policies to manage people, data, infrastructure, and algorithms to develop precise guidelines for adding modern technologies to different departments of the bank.
  • Develop a case-based AI implementation plan: financial institutions need to assess the extent to which AI-ruled banking products are being adopted within existing operational processes. After identifying promising options for implementing AI and machine learning, technology departments should analyze all questions and understand what problems may arise in adopting projects. Banks need to find programmers and data engineers to add AI solutions. You can use outsourcing services if you do not have your experts.
  • Building and scaling: Before building full-fledged platforms, you may prototype them to determine the possible complexities. Before testing, the algorithm must access a large size of data. AI models are created and trained on these information bases, so using only accurate data is essential. After the training of a virtual assistant, it needs to be tested to comprehend the features of working in the real world. If the test confirms the model’s effectiveness, you can deploy and update it.

The addition of AI-ruled banking products requires constant controlling and improvement. Banks should develop a validation algorithm to closely monitor and evaluate the effectiveness of adding artificial intelligence techniques. It will facilitate the fight against cyber threats and allow specialists to quickly and efficiently perform various operations.

Some challenges with implementing AI in banking

According to McKinsey, artificial intelligence could generate $1 trillion in additional bank profits annually. Artificial techniques today are considered an integral element of the civilized world. They are known as one of the most significant value creators across industries. While AI is a promising technology, the scale of change needed to make it successful is enormous. However, there are three main issues that banking professionals need to be aware of in advance:

  • Significant expenses: there is often a long delay between the creation of an algorithm and its implementation in activities; this comes with a high startup cost.
  • Information security: the problem when working with AI in banking is that much of the data collected contains sensitive details, and additional measures must be taken to ensure their security. Therefore, finding the optimal partner who knows different ways to ensure safety and the correct interaction with customers’ personal information is essential.
  • Data quality: AI is a technique that relies on the data as the quality of the insights affects the system’s ability to make accurate predictions. High-quality facts are needed to create algorithms banks can use in real life. If the information is not presented in a machine-readable form, this may disrupt the operation of the artificial intelligence model.
  • Narrow focus: AI algorithms may quickly solve a particular problem but cannot solve contextual problems. The presence of emotional intelligence will allow you to train the platform to function beyond the reasonable.

Artificial intelligence cannot be ignored as another revolutionary technology for products and services in the banking business. Intelligent applications have become more efficient over the years and positively impact financial institutions’ profitability. Early implementation of AI-based techniques creates a reasonable basis for success now and in the future, while banks that fail to implement intelligent systems may be left behind.