Fraud detection – or scam detection – is a structured approach or protocol to recognise potential fraud attempts, track malicious requests and safeguard a business against fraud. Malicious actors attempting to misuse business systems to harm the company or achieve personal gains is a common occurrence across industries and business domains. And therefore, employing fraud detection processes and protocols is essential before the business is affected, especially as real-time transactions become a norm for modern businesses.
Types of fraud
Some of the common types of fraud include:
- Credit card information theft
- Account hijack
- Fake accounts
- Reward (or loyalty) bonus abuse
- Friendly fraud
- Affiliate fraud
- Return fraud
Fraud detection techniques
Fraud detection services rely on data analysis. And based on the method of data analysis, you can classify fraud detection techniques as
Statistical data analysis
As the name suggests, this fraud detection technique relies on various statistical operations to conduct in-depth investigations about fraud detection and validation. Sub-categories of this technique include:
- Statistical parameter calculation
- Probability distributions and models
- Data matching
- Regression analysis
AI-based approach to fraud detection has emerged as a front-runner in effectively identifying fraud attempts, enhancing the agility of security protocols and streamlining fraud detection and prevention processes. AI-based fraud detection techniques include:
- Data mining
- Neural networks
- Machine learning
Fraud detection best practices
The top eight fraud detection best practices are:
- 1. Build a comprehensive and robust potential fraud risk profile.
- 2. Address every potential fraud indicator.
- 3. Be consistent in monitoring and auditing.
- 4. Raise cross-organisational awareness about fraud monitoring and prevention protocols.
- 5. Leverage artificial intelligence solutions.
- 6. Adopt and encourage suspicious activity reporting.
- 7. Implement analytics-driven intelligent case management solutions.
- 8. Don’t be complacent. Keep learning, adapting and evolving.
Challenges of fraud detection
However, despite your best intentions and efforts, fraud detection can potentially backfire and harm your business goals. Some of the major obstacles to effective fraud detection include:
- A false positive can harm your business's revenue and reputation. If you have a system in place to block a potentially fraudulent transaction, a false positive means the customer would not be able to complete their purchase. And they may end up going to a competitor and lose faith in your business.
- There is no one-size-fits-all approach to fraud detection. With fraud attempts evolving every day, you must be agile and adapt your fraud detection protocols.
- A stringent fraud detection protocol can create friction in your customers’ journey, slowing access to your services and driving customers towards competition.