Bank Artificial Intelligence

Artificial intelligence is slowly taking over daily business operations. Many different industries are using artificial intelligence to improve efficiency and enhance the customer experience. From automating repetitive tasks to analyzing large amounts of data in real time, the collection of technologies that comprise artificial intelligence hold much promise for the future.

However, your bank may be lagging in adopting AI. While many other industries are using AI to add value to their overall business strategy, banks are still struggling to develop processes that link distinct data sets while keeping confidential data safe.

This slow adoption doesn’t mean that AI technologies are inapplicable to banking. AI is capable of saving the banking sector over $1 trillion in the next 12 years. Subsets of AI, such as machine learning and language processing, can be used to enhance the online banking experience, improve data security, and automate mundane tasks for employees.

Use Cases for AI in the banking sector

The use cases for AI in banks are widespread. While many of these technologies may currently be in their early stages, growth and usability will increase at a rapid pace over the next few years.

Here are five use cases of AI in banks.

1 – Using intelligent analytics to initiate real-time data analysis

As with many other industries, banks need to follow a data-driven approach if they wish to remain competitive. Data in banking is widespread- including financial records, customer data, market data, ACH data, and credit reports. Artificial intelligence can be used in enabling banks to receive analysis from these data sources more efficiently.

Rather than merely using descriptive analytics, AI allows banks to uncover valuable insights in their data through predictive and prescriptive analytics. This means that you can use complex algorithms and tools that sift through large quantities of data and uncover patterns/correlations that you didn’t think existed before.

These new insights can then be used to model investment risk, implement biometric security models, and detect fraud in daily financial transactions.

2 – Using predictive analytics to enhance data safety

Your bank can also use AI technologies to enhance the safety of customer data. For example, machine learning can be used to implement new security measures that are harder to bypass. Voice recognition is currently being applied to assist with password protection, while predictive analytics can be used to detect unusual customer behavior and alert relevant personnel in good time.

Geographical controls are also useful, where transactions made from a different area than usual can be flagged and verified before approval.

3 – Using digital personal assistants to enhance the customer experience

Digital personal assistants (also called chatbots) are designed to interact with customers in a more human-like behavior. Think of it as a 24/7 personalized customer service resource, which your customers can use to request for information, assistance, and advice. Some chatbots even deliver timely financial tips to bank customers via voice and text.

In this way, your employees will spend less time addressing basic requests (such as balance inquiries and transferring funds) and more time attending to complex customer concerns (such as resolving fraudulent transactions).

4 – Enhance the user interface of mobile banking apps

Online and mobile banking has become the norm for customers today. Many account holders don’t make trips to their nearest bank branch unless they have to.

Artificial intelligence can be used to enhance both the mobile and online banking experience by providing personal, secure, and convenient services. For example, machine learning tracks user behavior and offers a wide range of personalized suggestions.

From budgeting tips to personal planning assistance, the user interfaces of mobile banking apps can be enhanced by AI technologies. This improved experience can be achieved at a lower cost and without increasing the workload of employees.

5 – Using Robotic Process Automation to carry out repetitive tasks

RPA (Robotic Process Automation) is a type of AI technology that can be used to automate repetitive human tasks in a more accurate and timely manner. What happens is that various inputs are set, after which specific rules can be applied to those inputs.

RPA can be used to examine loan applications, aggregate data into a common database, and even automate basic procurement processes (such as purchasing office supplies).

Implementing RPA allows banks to reduce human error, carry out mundane processes faster, and make better decisions based on analytics.

Getting Started With AI

With the numerous benefits that AI provides to banks, you shouldn’t be left behind in implementing these technologies. Don’t know where to start? Insight Financial Marketing (IFM) provides AI support to banks in many different ways. Our real-time customer behavior intelligence technologies help you uncover valuable insights and improve the overall user experience.

IFM also has proprietary Artificial Intelligence solutions that can be leveraged to make your bank more competitive in a fast-moving world. Ready to take the next step into intelligent analytics? Contact IFM today.