More than just translating text or recommending the video you watch next, Machine Learning (a subset of Artificial intelligence) has made significant impacts in most prominent places such as healthcare, banking, and more.
While healthcare has been limited to a few scenarios, Machine learning models in banking have helped to solve the biggest concerns and made our everyday banking easy. In this guide, you will learn about the impact of machine learning in the banking sector and how it is enhancing digital banking services.
What is Machine Learning (ML)?
Machine learning is a subset of artificial intelligence that enables systems to learn and make decisions on their own using data without being explicitly programmed to do so. It analyses huge amounts of data to identify different patterns and predict outcomes based on the data. There are three primary types of machine learning.
- Supervised machine learning: In supervised machine learning, the model is trained using labeled data to predict outcomes based on past data.
- Unsupervised machine learning: On the other hand, in unsupervised learning, the model is trained using non-labeled data and predicts output on its own based on hidden patterns.
- Reinforced learning: The model learns in real time based on feedback.
For example, imagine you want a computer that can recognize pictures of a cat. In supervised learning, you train a computer with pictures labeled “cat” and “not a cat,” and the model learns about cats so that it can predict the cat picture in the data.
Meanwhile, in unsupervised learning, you train a model using non-labeled data. Here, you don’t explicitly mention it was a cat; however, based on the hidden pattern or the structure, the model identifies a pattern and predicts cats in the future.
Finally, in reinforced learning, you reward the model if it identifies the cat correctly or penalizes it if it doesn’t, and it learns based on the feedback.
Machine learning is vast. It takes years to learn and understand how it works. However, you don’t need to learn everything about machine learning to learn how it is impacting our lives, especially in digital banking; machine learning has become essential in delivering faster, more efficient, and more secure services.
Personalizing Banking Services
Banks can offer more personalized services using Machine learning. By analyzing customer transactions and account activity, banks can create personalized offers based on customer financial goals. Additionally, machine learning algorithms can help analyze customer spending, saving habits, and transaction histories and offer personalized recommendations.
For example, if you consistently spend money on dining or entertainment, the bank could suggest a credit card with benefits tailored to those exact categories.
Dynamic Credit Scoring
Traditional credit scores are based on a limited set of factors such as income, debt levels, and an individual’s credit history. However, these factors don’t represent the user’s entire financial situation.
In addition to standard factors, Using machine learning, banks can evaluate customer spending behavior, transaction history, and even social indicators such as education and employment history, lifestyle choice, and other informational financial transactions to offer credit scores.
This helps banks make more accurate evaluations and avoid errors, as well as help users, especially those with limited credit history. It also allows banks to extend credit to more people, including those who traditional methods might have overlooked.
Enhancing Fraud Detection and Security
Security is one of the biggest concerns in digital banking. Considering rising bank frauds, banks need efficient systems to safeguard their customer’s sensitive financial information. This is where Machine learning can play an important role in detecting and preventing fraud.
Using Machine learning, banks can identify suspicious transactions in real-time and prevent unauthorized transactions. Moreover, these models learn and adapt to new types of fraud and help in preventing them. For example, if a customer makes a large withdrawal from an unfamiliar location, a machine learning system can flag this behavior and alert the customer or simply block the transaction.
Along with these, using different ML models, banks can track customers’ unique behaviors to verify their identity as customers. This includes analyzing how fast customers type, mouse moment, or how they interact with their mobile phones, as well as checking if current patterns are similar to the customer’s historical data to confirm the actual account holder.
Optimizing Overdraft Services
Overdraft protection is a popular feature that many banks offer to prevent customers from incurring fees when they don’t have enough money in their accounts to cover a transaction. Using ML, banks manage overdraft coverage more effectively by analyzing customer behavior, spending habits, transaction timing, or account balances and flagging accounts at risk of overdrawing.
Using these data, Banks can then send notifications to customers, giving them a chance to transfer funds or cancel a pending payment. ML also allows banks to offer more personalized overdraft protection services instead of a one-size-fits-all solution. Banks can use machine learning to assess a customer’s financial situation and offer overdraft coverage that suits their needs.
For example, customers who frequently experience low-balance issues might be offered a higher level of protection, while those with stable account activity might receive a different option. This guarantees that you only pay for the level of protection you truly need, with no extra fees.
Improving Customer Support
Reliable customer support is the most important part of digital banking. Most banks are now using intelligent chatbots, virtual assistants, and different automated systems to help customers get support. From answering customer questions to personal support, machine learning can help banks build trust and quickly resolve customer problems.
AI chatbots can help answer customer inquiries in real-time, understand and respond to a wide range of customer questions, and offer personalized guidance. This way, banks reduce wait times and free up human agents to focus on more complex issues.
Virtual Assistants for Personalized Support
Like chatbots, virtual assistants have become common in offering personalized support. These virtual assistants use machine learning to analyze customer data and provide tailored advice or information according to what data is associated with customer accounts.
For example, a virtual assistant might recommend a specific savings account based on a customer’s financial goals and even remind customers of their upcoming bills based on their usual spending habits. Over time, as the assistant learns from past interactions, it becomes better at anticipating your needs and offering you more and more relevant advice.
Automating Routine Processes
Machine learning also allows banks to automate the most common tasks, such as data entry and document verification processes, to save time and human resources. This helps banks speed up operations and also reduce human error.
For example, machine learning systems can automatically classify and sort financial documents, as well as verify customer information, and even process loans or claims more quickly. Automation also saves money, allowing banks to focus their resources on innovation and providing better services to you and other customers.
Optimizing Loan Decisions
Machine learning is also transforming how banks issue loans. Along with traditional credit scores, banks can analyze user spending patterns and make accurate lending decisions. This way, the bank and the borrower can both benefit from machine learning.
Conclusion
ML is playing a really important role in reshaping digital banking services. It’s involved in enhancing personalization and improving fraud detection, as well as streamlining customer support and driving the future of banking. As most banks continue to adopt these technologies, you and other customers can expect even more tailored experiences, better security, and innovative financial services. The potential of machine learning in banking is vast, and it will only continue to grow as technology evolves.
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