The changing definitions of customer experiences in the domain of financial services have led to growing demand for better solutions in financial analysis, forecasting, and financial engineering. With the rising adoption of machine learning in finance, it is important to check how you can use ML to improve financial services. Some of the popular financial institutions, including banks such as JP Morgan and investment funds, have embraced AI in their business operations. As a matter of fact, around 70% of all financial services institutions use machine learning in some form. Machine learning can serve the domain of finance with different applications that can help with optimization of processes in different departments and types of businesses. Let us take a look at some of the most popular use cases of machine learning for financial services.
How Can You Use Machine Learning in Finance?
Machine learning is a broad subdomain in computer science that helps computers learn from data without the need for special programming. It has emerged as a significant tool for improving processes and systems in different sectors, such as healthcare, retail, and manufacturing. The search for answers to queries like “How is machine learning used for finance?” draws attention to capabilities of machine learning to serve different areas, including social media communications and marketing. Therefore, it is easy to imagine how machine learning can transform the world of finance. For example, machine learning can empower automation in finance with models for credit risk predictions. Banks can use credit risk prediction models to determine the possible risks of certain lending decisions.
- Machine learning can also serve as a useful tool for improving finance by recommending suitable financial products at the right time. The use of machine learning in finance can help banks determine which customers they must approach for their new services. As a result, banks can ensure better management of their service portfolio alongside reducing costs through automation of repetitive processes.
- Machine learning models also help improve asset management and trading decisions by analyzing big data sources. Take your first step towards learning about artificial intelligence through AI Flashcards
Notable Facts about the Use of ML in Finance
Before you discuss the real-life use cases of machine learning for finance, it is important to learn some facts about ML in finance. Statistics about machine learning in finance industry can help in understanding how machine learning has matured into a trusted tool for transforming financial services. Research by the World Economic Forum and Cambridge Centre for Alternative Finance has revealed that around 56% of financial services companies have embraced machine learning for risk management. The banking sector has gradually increased the adoption of machine learning technology. Around 75% of executives at banks with more than $100 billion in their assets have reported that they are working on adoption of AI and ML technologies. On top of that, 48% of US bank executives believe that generative AI can help in empowering customer-facing virtual assistants and chatbots. Therefore, it is important to discuss the opportunities for machine learning in the financial services industry.
- Machine learning can serve as a powerful resource for making sense of large collections of data by discovering important patterns. The insights from machine learning systems can help enhance business processes, serve prediction tasks, and make informed decisions.
- Let us take a look at some of the top applications of machine learning in the domain of financial services. The most important machine learning use case in finance is financial monitoring. Machine learning can help in enhancing tools such as investor dashboards and budgeting apps that are the best picks for monitoring financial health. In the domain of finance, financial monitoring is also referred to as personal capital management. Financial monitoring tools help financial advisors track their expenses and guide people toward their financial goals. The accuracy of machine learning can improve financial monitoring tools and notify users about discrepancies in their budget planning. Such tools can help create recommendations to ensure adjustments to individuals’ financial planning methods. Identify new ways to leverage the full potential of generative AI in business use cases and become an expert in generative AI technologies with Generative AI Skill Path
Automation of Financial Services
Different companies leverage automation with machine learning for cost reduction. Financial institutions such as banks can have an application program interface that can generate new account applications. With machine learning in finance, the API team can automate their workflow. As a result, they could focus more on tasks such as interacting with customers and providing advice. Automation also helps reduce paperwork, which is a major goal for different financial firms. According to Reuters, banks would spend billions of dollars every year on compliance activities and paperwork. Financial advisors can also use machine learning to avoid mistakes in manual data entry tasks.
The applications of ML in finance also involve data management. Data management is a crucial requirement in the financial services sector as it helps collect, store, and segment data for better analysis. The use of machine learning in finance can help in enhancing data management to ensure effective monitoring of volatility in financial markets. For example, ML systems can monitor data about all transactions by a firm to recognize patterns or trends that may indicate malicious activities.
