Manual Analysis Resulting in Decreased Productivity and Visibility
Bank fraud reached an all-time high in 2015. To combat this threat, Alliant Credit Union employs a team of fraud specialists that scrutinizes detailed reports in search of fraudulent financial transactions.
In the past, these analysts often had to resort to a manual process of sifting through financial transactional information in conjunction with security information to correlate patterns and trends to detect fraud.
Financial Fraud Detection, Simplified
Alliant chose LogRhythm as their partner to tackle this challenge. The LogRhythm team helped Alliant identify the common patterns and trends that the fraud analysis team had previously looked for manually.
Once they understood what fraud looked like, the team then applied custom AI Engine rules and tuned them to work with financial and security data. Finally, they applied behavioral analysis capabilities to actively predict and automatically alert on suspicious behavior in real time.
These changes saved the analysts time, which they previously had spent manually parsing through work. They also enabled the analysts to be more proactive as dashboard visualizations quickly featured the outliers they should investigate.
“We can now see what our systems are doing at any point in time. We pride ourselves on our efficiency, and LogRhythm has definitely improved ours by helping us automate as much as possible.” –David Rebman, senior cybersecurity engineer, Alliant
Learn more about how Alliant Credit Union automated their financial fraud detection. Get the full story below.