Overview
AI in financial data security helps businesses detect threats, monitor systems continuously, identify fraud patterns, and automate compliance checks. This article explains how AI-driven tools, combined with human oversight, strengthen protection against cyber risks, reduce financial losses, and improve the accuracy and efficiency of safeguarding sensitive financial information.
Introduction
When you run a business, you and your clients count on the system to keep financial data safe. The security of financial data is a growing concern, as fraud and theft become increasingly complex and harder to prevent. These ongoing threats increasingly benefit from the use of artificial intelligence as part of a broader security approach. When paired with established security controls and human oversight, automated AI systems can help organisations reduce the risk of compromised financial data.
Anticipate System Weaknesses
Many businesses do not even know which threats they face due to the systems they have. Weak or ineffective encryption, outdated infrastructure, or poorly maintained systems that tread constantly toward obsolescence, can put your data at risk. AI‑driven tools can analyse system signals and configurations to help identify potential weaknesses, such as weak passwords or low system protection for remote workers. This information can help systems administrators create plans to mitigate the risks in advance of a cyberattack. Consequently, the company can reduce both financial losses and reputational damage by helping prevent certain types of attacks or limiting their potential impact.
Provide Continuous Monitoring
Systems administrators can provide regular attention to the condition and security of the systems they manage, but they cannot be on task every hour of the day. Cybercriminals may attempt to exploit periods of reduced staffing, such as nights or weekends, hoping to capitalise on this lapse in attention. AI systems do not need to take breaks or time off, which means that they can provide continuous monitoring. They can even continue monitoring the system during updates or testing, provided appropriate redundancy and safeguards are in place, so that the data does not lose encryption or security at the most vulnerable moments.
Detect Fraud Patterns
Although cyberattacks can take a wide range of paths, the fraud tends to follow certain patterns. With access to big data, AI systems can identify these patterns that people follow when they are attempting to gain unauthorised access to a financial system. The AI systems can use this information to create sets of patterns that will trigger alerts or temporarily restrict a user’s access until a human can review the activity. Because fraud tactics evolve over time, these systems require ongoing tuning and oversight to reduce false positives and adapt to new threats. The data can also inform administrators, so they can implement processes to prevent unauthorised access during the early stages of an attack.
Identify Anomalies in Transactions
Similar to assessing fraudulent patterns used in cyberattacks, AI can process client and employee transaction data and use it to create a system of understanding for the average user. For example, if a person rarely makes a very large payment or withdraws a significant sum of money, such a transaction may warrant additional scrutiny based on the user’s historical behaviour. AI integration can use this data to generate alerts or require additional user verification before proceeding. As a result, the system can help flag or reduce the likelihood of significant transaction errors while also minimising the impact of unauthorised access.
Automate Compliance Checks
Security in financial data is increasingly a factor of regulatory compliance. Many companies are required to show how they protect the financial data of the business and its users, with potential penalties or enforcement actions for businesses that fail to meet regulatory requirements. Automated compliance checks are an increasingly common use case within AI‑enabled systems. Finance reporting software can automate data collection and report generation to increase accuracy in reporting, decrease human workload, and improve the quality of in-house audit trails.
Designing a system for a business that is secure and protective of financial data is not a simple task. Companies rely on the system to keep unauthorised users out, lest they gain access to sensitive financial data they can use to steal identities, drain accounts, or extort the business. For most organisations, purely manual approaches can struggle to scale effectively without automation, as humans are more likely to miss key anomalies or be out of the office when an attack hits. With an AI system that augments human expertise, companies can better protect their financial data from increasing threats.
Christen Wojciechowski is Digital Marketing Manager for Donnelley Financial Solutions™ (DFIN), a global financial solutions company headquartered in Chicago. She focuses on the company’s marketing operations through brand awareness, lead generation, and engagement across channels. Her work covers both overall strategy and hands-on execution within the tools.
