It is inevitable that companies, regardless of the industry they are part of, will bring all their internal processes and customer service systems to the automation and technology market, including the powerful banking industry, one that would benefit from machine learning.
Machine learning, according to the definition offered by the Oxford dictionary, is to make use of computerized systems that are capable of adapting to the needs of the user without following precise instructions from humans, but rather by making use of algorithms.
This type of artificial intelligence feeds on the interactions with the user, adjusting to what they need from their service, thus managing to imitate as close as possible to a human mind, moving away from the typical chatbot.
Uses of machine learning in the banking industry
Machine learning fits perfectly within the processes of the banking industry since it can be fed from the huge databases of banks to generate useful information, such as: customer transaction history, keeping records of customer service chats user or with bank representatives, all this helps the bank to process data and thus be able to make a future in-depth analysis of them.
Fraud detection and optimizing credit allocation and approval are one of the processes in the banking industry that benefit from machine learning, in addition to others that we will explain in detail.
Personalization
Each client or user of a banking institution is a world in itself. None of them have the same needs or go to the bank's page every day to carry out the same procedures, so the website or application must be prepared to attend to each one.
With machine learning, banking institutions can learn in depth the behavior of customers and thus offer timely and effective options to keep them satisfied. For example, they can generate a budgeting tool, this keeps the customer loyal and drives the value of the institution.
Automate repetitive tasks
The benefits and uses of machine learning are not only applied to improve customer service, but also to automate tasks of employees in banking institutions that can be eternal and demand a large amount of time and effort that can be used for others. processes.
Auditing and documentation are two types of tasks where you can make good use of machine learning, thus saving workers from spending hours and hours reviewing each piece of data. With an appropriate tool, you can highlight only the data that is relevant to the audit and thus complete the process in a very short time.
An example of this case is that of the Quontic bank in New York. The institution adopted a machine learning tool for its internal tasks and achieved excellent results. In summary:
- 210% increase in the total number of bank customers
- 100% increase in new personal banking customers
- 261% increase in retail deposits
- 15% increase in year-over-year funding growth
Cybersecurity
In a survey led by IBM, it was observed that banking and finance institutions ranked first in the list of most cyberattacks received, this for the fifth consecutive year. This is a piece of information that triggers the alarms of any banking company, making it a top priority to strengthen the security of its websites and applications.
The usual security measures are no longer an option, making use of tools based on machine learning is the most viable option to be aware of the behavior of possible attacks and thus be able to stop them in time, with algorithms that continuously analyze events in the system.
Risk management
For years, banks have used behavioral simulators to predict possible market risks and be able to adjust to them in time, this has been done with a traditional and manual approach that over time has been proven to have errors that can be crucial when applying results.
Having a machine learning tool that helps analyze future market scenarios has been a great benefit for the banking institution, it helps reduce errors and they can quickly adapt to new needs.
Chatbot
Maintaining constant communication with clients and users is vital for any banking institution. A Chatbot tool, which is powered by machine learning, is essential in any bank website or mobile application.
A notable success story of this tool is Erika, the chatbot with which Bank of America has communicated with its customers since 2018. Erika has already handled almost a billion integrations, so thanks to its engine based on machine learning, is able to answer almost a million unique questions.
These are the most common uses of machine learning in a banking institution. This is a solution that is gradually dominating technological trends. Tech pollster 99Firms predicts that 8.4 billion virtual assistants will use machine learning by 2024.
Do not stay are the machine learning solution that your company needs. At Rootstack we have a team of experts at your disposal, contact us and let's start working together.
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