Generative artificial intelligence (genAI) is making significant inroads in the financial services industry, with adoption rates and implementation levels being the most advanced in information technology (IT), cybersecurity and finance functions, according to a global Deloitte study conducted in Q3 2024.

The study, which polled 2,773 leaders, found that the IT function stands out as the most developed area for genAI deployment in the finance sector, with 21% of organizations indicating high adoption levels.

This trend mirrors a boarder industry pattern, where IT leads in genAI implementation at 28% across all sectors, a popularity that’s largely due to the technology’s ability to generate computer code, streamline software development and testing, enhance bug detection and security, and automate IT support.

Cybersecurity is the second most advanced area for genAI application in financial services, with 14% of organizations demonstrating mature implementations.

A leading bank shared how genAI transforms secure software development by analyzing application vulnerability alerts, reducing false positives, and allowing engineers to focus on critical issues.

Each day, this bank’s security team faces millions of alerts related to code-level security issues, such as endpoint vulnerabilities and misconfigurations. Managing this volume of alerts is both time intensive and yields false positives, leading to tension with the application developers whose performance incentives are aligned with new feature development rather than vulnerability remediation.

To tackle this challenge, the bank deployed an AI-powered platform that translates regulations, policies and standards into security controls, including preventative controls, detective controls, responsive controls and corrective controls, and then codifies those controls across the software development life cycle.

From there, facing a daily deluge of potential application security alerts, the bank needed an efficient yet accurate way to identify critical vulnerabilities. To address this need, its security operations center implemented a genAI solution to streamline its vulnerability management processes and systems. This is done by triaging millions of incoming cyberthreat alerts and paring them down to thousands of “real threats” that then go to different cyber teams, such as distributed denial-of-service and malware.

This dramatically reduces the volume of common application security vulnerability alerts the cyber team must triage and development teams must address, down to fewer than 10 critical vulnerabilities a day. As a result, the bank’s cyber risk is greatly minimized, enabling the security and development teams to focus their time and effort on problems that are real, impactful and actionable.

Additionally, the solution boosts morale and productivity across the engineering team by reducing the time spent on DevSecOps so they can focus more time on developing new software and push critical updates into production.

High adoption of genAI in cybersecurity is accompanied by remarkable return on investment (ROI) results. Across all implementation areas, organizations focused on cybersecurity are far more likely to be exceeding their ROI expectations, with 44% of cybersecurity initiatives across all industries delivering an ROI somewhat or significantly above expectations. In comparison, only 17% of genAI initiatives are delivering an ROI somewhat or significantly below expectations, representing a 27-point gap.

Finally, the finance function is the third most advanced area for genAI adoption in financial services, with 13% of organizations reporting mature implementations. This is significantly above the cross-industry average of just 4%.

Common applications of genAI in finance at financial institutions include fraud detection and prevention, as well as credit risk modeling.

According to a 2024 McKinsey survey, 20% of credit risk organizations have already implemented at least one genAI use case in their organizations, and a further 60% expect to do so within a year.

Similarly, a study by Forrester Consulting of more than 400 senior fraud leaders last year revealed that 73% believe genAI has permanently altered the fraud landscape. 71% agree that AI and machine learning (ML)-based fraud solutions are critical to stay at pace with a growing fraud threat.

Featured image credit: edited from freepik