A naukri.com initiative
TechBullion
2w
240
Image Credit: TechBullion
Expense Fraud Detection Using Machine Learning: Catching Fraud Before It Strikes
- Expense fraud is a significant challenge for organizations worldwide, and detecting such fraud manually can be tedious, time-consuming, and error-prone.
- Machine learning (ML) offers innovative ways to combat expense fraud efficiently and proactively.
- ML systems can process vast amounts of expense data in real time, identifying anomalies that may signal fraudulent behavior.
- ML models can detect unusual patterns that deviate from normal expense behaviors and assign risk scores to expense claims.
- Machine learning employs various techniques to identify fraudulent expense claims, including supervised, unsupervised, natural language processing, and reinforcement learning.
- Implementing a machine learning-based expense fraud detection system requires careful planning and execution.
- Addressing challenges requires a collaborative approach involving robust data governance, regular model audits, and input from domain experts.
- Machine learning offers a transformative solution that enables organizations to detect and prevent fraud with unprecedented efficiency.
- Large corporations, expense management software companies, and financial institutions have successfully implemented machine learning to combat expense fraud.
- Organizations that invest in machine learning technology not only safeguard their finances but also foster a culture of transparency and trust.
Read Full Article
14 Likes
For uninterrupted reading, download the app