- Fraud analytics is all about detecting unusual transactions 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.
Banking & Finance:
Occurs from unintentional data breach, website cloning, use of phishing and malware, stolen cards etc.
Insurance:
False claim in travel insurance and medical insurance.
Retail:
Can arise from employees, vendors, customers, and hackers who unintentionally or deliberately manipulate transactions.
HOW DOES FRAUD ANALYTICS WORK?
Machine learning techniques and neural networks 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 create 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 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. The trail of digital fingerprints presents a big opportunity.
- A systematic plan to pull in the 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.
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