Data science is essential to the financial services industry — it helps businesses make better decisions about everything from credit risk to investment strategies. However, the sheer volume of data can be overwhelming, making it difficult for humans to employ traditional data analysis methods to examine thoroughly. Financial firms need a way to analyze this data quickly and efficiently, and machine learning can help them do just that.
AI and machine learning (ML) are already used in the financial services industry to create value and improve decision-making. These technologies are evolving rapidly, and a recent CFA Institute survey found that new analytical methods, including artificial intelligence and machine learning, are expected to dramatically change the industry's job roles over the next 10 years. As machine learning continues to develop, financial institutions that fail to adapt will be at a competitive disadvantage.
Managing such large and complex data sets often proves to be a difficult task, and organizations face three main obstacles when applying machine learning to their practice.
Data bias: Bias is a challenge that any modern use of data for predictive insights faces, including machine learning and artificial intelligence. Human bias can also creep into machine learning models, leading to potential disparities and inadvertent discrimination in credit access, financial assistance, and other important services.
Complexity and compliance: Financial data is heavily regulated. With a growing list of rules and potential for your system to be audited, it's critical to develop a machine learning operations (MLOps) strategy that keeps track of your data sets and model outputs for regulators to review.
Hiring and retaining quality employees: The demand for talent is outpacing the supply, which is creating a shortage of skilled workers, including financial services. As a result, organizations are having to pay premium wages or outsource the work to third parties in order to get the talent they need.
How Leaders Can Use Machine Learning to Face These Challenges
Despite these complexities, machine learning continues to grow and change rapidly with a number of opportunities for businesses to leverage. Here are five ways that financial services organizations can begin implementing machine learning into their workflow and business practices.
Fraud detection: Standard fraud detection methods use rules-based systems to flag suspicious transactions. However, these systems are inflexible and require frequent updates, which can lead to false positives. Machine learning analyzes data quickly and accurately to identify patterns and anomalies that may indicate fraudulent activity.
Risk assessment: Artificial intelligence and machine learning techniques can improve the speed and quality of risk assessment. For example, machine learning can be used to analyze loan data for patterns or inconsistencies that may indicate fraudulent behavior, potential loan defaults, or other risks. This early warning can help financial institutions identify and mitigate risks before they occur.
Regulatory compliance: Financial institutions are heavily regulated, and failure to comply with regulations can result in significant penalties, including fines, imprisonment, and reputational damage. While navigating these systems can be a challenge when implementing machine learning, there are AI-powered systems that can use machine learning algorithms to identify potential compliance violations and generate reports for regulatory agencies in a timely manner.
Algorithmic trading: Algorithmic trading makes use of programs to execute trades based on predetermined rules and algorithms. Machine learning can improve the effectiveness and efficiency of algorithmic trading by analyzing market data, identifying trends, and developing trading strategies.
Customer experience: Artificial intelligence and machine learning can be used to improve the overall customer experience in a number of ways. For example, chatbots and virtual assistants can provide quick and efficient customer service by answering questions and resolving issues 24/7. Personalized recommendations can also be used to move customers closer to purchasing decisions by suggesting products or services that are likely to be of interest to them. Additionally, machine learning algorithms can be used to predict customer needs and offer proactive solutions to improve customer satisfaction and loyalty.
A Look Ahead
The financial services industry is facing a shortage of skilled talent to serve as data scientists, analysts, and other vital roles. This is a major challenge that the industry will need to overcome in order to fully realize the potential of machine learning. Companies in the industry should be prepared to compete for machine learning talent, or to partner with third-party providers that can help them get the resources they need.
The key to successfully leveraging AI and machine learning in financial services is to find ways to integrate them into your existing business processes and workflows. Identify the specific pain points and inefficiencies in your operations, and explore how machine learning can help you solve them.
Dr. Ryan Ries is Practice Lead Data, Analytics and Machine Learning, at Mission Cloud.