Introduction
Fraud analytics is all about detecting unusual transaction arising from fraud/bribery after the transaction is completed (detection) or before they occur (prevention).
Fraud detection and prevention relies on data mining and makes use of ML and deep learning algorithms in addition to traditional rule-based methods.
Types of Fraud
Banking & Finance – Occurs from unintentional data breach, website cloning, use of phising and malware, stolen cards etc
Insurance – False claim in travel insurance and medical insurance
Retail – Can arise from employee, vendors, customers and hackers who unintentionally or deliberately manipulate transactions
How does Fraud Analytics works?
Machine learning techniques and neural network can be effectively used in analyzing fraud
- Various outlier detection techniques help identify the anomalies between usual and unusual events.
- Unsupervised methods like clustering algorithms creates homogeneous groups to discover fraud patterns
- Supervised models are trained on fraud and other events to develop a pattern which can predict fraudulent activities by producing fraud scores. These scores are based on of attributes like transaction amount and time, IP address etc or reject payments among many others.
Challenges Of Fraud Analytics
- Fraudsters are constantly improvising their tactics which requires the models to keep pace and evolve.
- Stringent rules from complex models may result in too many false positives causing harassment to genuine people by blocking legitimate transactions.
Future
Fraudsters will continue to find new ways to manipulate system causing distress to business entities as well as to end consumers.
Trail of digital fingerprints presents a big opportunity.
A systematic plan to pull in wealth of available data scattered across different systems into a central platform will help take a holistic approach to analytics.
Constantly evolving techniques in big data mining and AI are adding value to existing efforts.