Machine learning in banking:
use cases, trends & adoption guidelines

Machine learning solutions for cybersecurity share the same approach as fraud prevention ones and are powered by anomaly detection algorithms that identify the signs of potential cyber threats before they escalate to security incidents. This helps banking institutions prevent data breaches and leaks of highly sensitive financial and customer information, fostering compliance with applicable data management regulations.

For example, AI cybersecurity solutions can detect anomalous behaviors that indicate a malware attempting to access a bank account, including subtle outliers like unusual keylogging activity that may not be identified by traditional systems relying on rigid rules. Additionally, AI systems can spot new behavioral anomalies by autonomously learning from data on successful and failed attacks to recognize emerging cyber threats. These tools can also be used more proactively, for instance, to uncover and address banking systems’ vulnerabilities through automated code reviews, cyberattack simulations or “red teaming”, and AI-powered software patching.

Banks can greatly benefit from using predictive analytics systems powered by machine learning algorithms for credit scoring. These fintech solutions can quickly analyze thousands of data points from clients’ tax returns, payment history, credit inquiries, and other sources and assess each borrower’s creditworthiness and associated default risk without human bias. Additionally, these tools can recommend an appropriate credit limit based on the customer's disposable income to maximize the likelihood of on-time loan payments, as well as suggest an optimal interest rate that balances profit and customer satisfaction.

Investment banks, wealth management firms, and other financial institutions are increasingly implementing machine learning-powered predictive capabilities into their trading management tools to optimize their strategies and financial portfolios. Machine learning algorithms enable organizations to process big data sets or streams of real-time financial data to quickly and accurately forecast market trends, helping pick promising stocks and build balanced portfolios for their clients.

The access to ML technology, however, is no longer the exclusive preserve of professionals, as banks have begun integrating robo-advisors into their mobile apps and platforms to provide retail investors with automated financial advice and assistance. These AI agents can create personalized financial plans based on users’ investment goals and risk tolerance and then handle portfolio management operations such as asset allocation and diversification.

With the advent of AI technologies like natural language processing (NLP) and generative AI, the range of routine tasks that banking institutions can automate has expanded significantly, enabling them to minimize clerical workloads and allocate more resources to customer engagement and decision-making. For instance, ML-powered GenAI agents can help employees draft emails, create banking service descriptions, generate and summarize contracts, bank statements, or other documents, and transcribe online meetings with clients and colleagues to turn them into brief reports.

In recent years, the integration of generative AI tools such as AI agents to automate customer operations has become the major selling point for many popular CRM platforms. Relying on ML tools integrated into a banking CRM solution, marketing and sales departments can better segment their audience to effectively engage them with targeted ads and personalized financial product or service recommendations, boosting lead acquisition and conversion. At the same time, customer support teams can use chatbots and agentic AI tools to provide clients with instant, 24/7 assistance and speed up case resolution, for instance, guiding a client through replacing a stolen card.

In a highly regulated industry like banking, compliance with financial legislation is essential to prevent fines, legal liability, and reputational damage, so many institutions have started applying ML-powered tools to automate their compliance management workflows. For instance, these solutions can extract and summarize investment guidelines from Investment Management Agreements, Statements of Additional Information, and other documents. Furthermore, these tools streamline Know Your Customer operations by analyzing customer data (the source of funds and wealth, the number of accounts, transaction history, etc.) and assigning a risk score to each client.