Digital transformation, understood as technological innovation and a cultural change in consumers, is changing the way of business in all sectors. Banks are also called upon to adopt new solutions to remain competitive and win the competition. The first need for evolution undoubtedly concerns the relationship between the bank and an increasingly digitized potential clientele, constantly looking for totally online relationships based on speed and the analysis of their needs.
It is, therefore, necessary to rely on models that generate continuous engagement based on the use of Apps, payments and digital transactions and maximum customization of relations with the consumer. Not only that: we also need to anticipate people’s needs through prediction tools. Another relevant aspect concerns internal operations. Even in the banking sector, the way of working is different, and one of the main needs is to automate processes.
Furthermore, we find ourselves managing an enormous amount of data every day, which, with the help of the correct data analysis solutions, can generate priceless value for the business, both in terms of risk assessment in managing customer relations and obtaining fundamental financial and marketing forecasts for those who have to make crucial decisions. This article presents a list of practical examples demonstrating how Machine Learning and Artificial Intelligence can offer effective and concrete help to financial institutions.
A Breakthrough In Customer Experience
As anticipated, today, there is a need for a relationship with customers based on simple and fast online services and continuous day-by-day support. To go in this direction, it is necessary to exploit the information collected, analyze user behavior and develop personalized offers. The same approach must also be used in setting up modern and rapid customer service.
For this purpose, the ideal tool is a Chatbot system, which allows you to serve your customers faster, more efficiently and at any time of the day. In this way, consumers’ trust in the bank increases daily: it will be possible to provide personalized insights and connect people to the right products and services for their needs when they need them, making a normally complex offer accessible and not easy to understand.
Decisive Support For Risk Management And Decision Making
Moreover, products and services offered by banks present complex profiles also in terms of risk and management of issuance requirements, with the need to carry out calculations and assessments that often go beyond human capabilities. Take, for example, the decision of whether or not to grant credit to a customer. The traditional method is based only on essential information and statistical calculations of credit scoring.
With the help of Machine Learning algorithms, much more significant volumes of personal information can be analyzed to evaluate without prejudice and reduce risk objectively. Analyzing the data collected is also essential for decisions following the granting of credit, mortgages or other banking products and services. It allows obtaining predictions of future events ( forecasting ) through the early detection of errors and potential risks, such as, for example, the prediction of the abandonment rate or churn prediction. This way, banks can prepare ahead of time and make better decisions.
Prevention Of Banking And Financial Fraud
Machine learning can also significantly reduce the number of fraudulent activities against financial institutions. Thanks to analyzing a large amount of data, which often tends to go unnoticed by people, it is possible to detect and prevent potentially harmful operations. In particular, it is possible to improve the accuracy of approvals in real time by using algorithms that require an additional identity check for certain operations, perhaps via a text message or a phone call. Furthermore, ML systems can identify suspicious behavior and movements of accounts and credit cards, with the result of preventing fraud immediately rather than only detecting it after the crime has already been committed.
Streamline Internal Operations
Another application of Machine Learning for the banking sector is to enhance Robotic Process Automation (RPA) to simplify internal operations. These solutions reduce the time staff spend on redundant tasks by performing routine tasks with minimal risk of errors. In this way, the bank can provide more efficient solutions while the workers have the opportunity to pay more attention to the most important issues. Conversational AI tools, such as a Virtual Assistant, allowing you to easily manage basic transactions and focus on much higher-value activities, such as deepening customer relationships.
Process automation is part of a wider ecosystem of work transformation, which takes place in a new digital workplace and process management concept. The innovation of the way of working comes from an inevitable cultural change within companies and is enabled by technological tools. These solutions include Machine Learning and tools such as Google Workspace and the Interact business platform, the only one that allows social management of business processes.
The collection and analysis of data, with the consequent creation of effective business models, help banks obtain better forecasts and make more informed decisions, including marketing. ML algorithms provide more accurate information on attracting new customers, starting from the analysis of the use of mobile applications, web activity and responses to previous advertising campaigns. Thanks to this information, it will be possible to create a solid marketing strategy and decide more effectively where it is worth investing time and resources. This advantage translates into an increase in revenue, even in the short term.
The Future Of Machine Learning In The Banking Sector
These technological solutions deal with myriad activities, constantly learn from the volumes of data and allow us to approach a secure and automated financial system every day. The need for banks today is to identify the most suitable solutions for their situation with the help of an expert partner who can develop and implement the right models to obtain the desired results. India represents the ideal choice in this sense, thanks to its proven experience and high skills in Machine Learning and Artificial Intelligence, with an approach always oriented towards business needs and the people who represent its beating heart.