In the past decade, we have seen many exciting, and disruptive technologies enter the financial services industry. From mobile payments and digital wallets to blockchain and artificial intelligence, FinTech innovations are changing the face of finance forever. However, while AI has recently become a hot topic in banking and other industries, it’s not a new concept. Machine learning has been around for decades. What has changed is the availability of affordable computing power and cheaper data storage options that allow for more widespread use of these techniques. Keeping this in mind, we look at some of the most useful machine learning algorithms used in AI today.

What is Machine Learning?

Machine learning (ML) is a form of artificial intelligence that enables computers to learn without being programmed. It is a method used to create algorithms that can learn from data. In simple terms, it is a process where computers use data to identify patterns and make predictions based on those patterns. ML algorithms do not need to be programmed for every eventuality; instead, they use data to change and improve their functionality over time. They analyze data and recognize patterns, then use those to make predictions. For example, a banking customer who regularly makes transactions at certain times of the week might be a shift worker or have a side gig that pays weekly. Machine learning algorithms can use this information to tailor their communications with customers and provide them with the information they need when they need it.

How does ML work in the banking industry?

Here are some of how machine learning is changing the banking industry. – Fraud detection: Fraudsters constantly look for new ways to exploit banking systems. ML algorithms can analyze patterns in user behavior and flag any abnormal behavior. This way, any suspicious activity is automatically detected and investigated by the team responsible for fraud detection. – Customer experience: Banks have much work to do regarding customer experience (CX). Customers are often faced with long queues and unhelpful representatives. ML can help streamline the process when dealing with customers by identifying issues and areas that need improvement. – Product development: Product development requires plenty of time and resources. Financial institutions are already using machine learning algorithms to identify and predict customer needs and develop tailored solutions to address this.

 

Types of ML in Banking

– Artificial Neural Networks:  Artificial neural networks (ANNs) are ML algorithms inspired by how our brains work. They “learn” by identifying patterns in data, such as customer behavior, and adjusting their behavior accordingly. ANNs are very helpful for predictive modeling as they can be applied to various fields and industries. They’re also handy for finding correlations in data that wouldn’t otherwise be obvious. 

– Support Vector Machines: Fisher’s linear discriminant analysis (LDA) is a popular method for decision-making. This method uses the coefficients’ maximum likelihood estimation (MLE) to find patterns in the data. Support Vector Machines (SVM) are similar to LDA, and only they are more robust since they can handle noise in the data and have a more significant margin between the closest points on either side of the dividing line. SVM can be used for classification and regression problems and is often used in computer vision and other applications. 

– Decision Trees: Decision trees are a type of supervised learning. This means that the algorithm is trained by being fed data that has been labeled. These algorithms are often used for classification and forecasting, such as predicting customer behavior or the economy.

Key uses of ML in bank operations

– Fraud detection: The goal of fraud detection is to identify any illegal behavior or actions that could risk the bank’s finances. ML algorithms can flag any suspicious user behavior and trigger a manual review from the team responsible for fraud detection. This way, any fraudulent actions are automatically detected and stopped as soon as they are discovered. 

– Customer experience: When it comes to CX, banks have plenty of work. Customers are often faced with long queues and unhelpful representatives. ML can help streamline the process by identifying areas where the customer journey needs improvement and coming up with solutions. 

– Product development: Product development requires plenty of time and resources. Financial institutions are already using machine learning algorithms to identify customer needs and develop tailored solutions to address this.

Risk Management using ML

Risk management is one of the most critical aspects of the banking industry. Financial institutions must maintain proper risk management practices to be regulated by the government. ML algorithms can identify data patterns that indicate risk and flag them for further review. These algorithms can be applied to both internal and external data. Internal data includes financial statements and internal customer data, while external data refers to macroeconomic indicators and other economic data. For example, a sudden decrease in interest rates across the board could indicate a change in monetary policy. 

– Customer Segmentation: Customer segmentation is a valuable risk management technique that allows banks to identify their customers based on their risk tolerance and investment goals. This way, institutions know who is using their service and what they need. 

– Customer Profiling: Customer profiling is another risk management technique that involves creating a general customer profile based on their financial history. For example, a customer who frequently misses their payment dates might be experiencing financial difficulties and pose a higher risk.

Human Resources and Talent Management using ML

HR is one of the most critical departments in any company. It is responsible for hiring, onboarding, and training new employees. However, it can be a lengthy and expensive process. ML algorithms can speed up the hiring process, providing companies with data-driven insights about potential candidates and making the hiring process more efficient. There are many ways that machine learning can be applied to HR and talent management. Some of these include: 

– Applicant Tracking Systems: An applicant tracking system is a centralized database that stores information about job applicants. They track the hiring process and identify areas that need improvement. These systems can be easily integrated with ML algorithms to streamline the hiring process further. 

– Employee Retention: Many financial institutions struggle with employee retention. This often leads to high turnover rates and hiring new employees, which is both time-consuming and expensive. RL algorithms can identify areas that cause employees to leave their jobs and recommend solutions.

Bottom Line

The technology in banking is evolving rapidly, and it will be exciting to see what the future holds. Undoubtedly, the financial services industry will see much change in the coming years, and machine learning will play a significant role in that transformation. From fraud detection to product development, there are many areas in the banking industry where ML can be applied. Although many banks are just now discovering the potential of ML, it’s clear that this technology will play a significant role in the financial services industry in the coming years. Reach out to DeepDatum for your organization’s ML needs.