What is Fraud Detection?
Fraud detection, also known as scam detection, refers to a structured approach to recognising, tracking, and preventing fraud. Within businesses across every domain and industry, some bad actors misuse the system to harm it for personal gain or disruption. Therefore, enterprises must employ fraud detection processes before they are affected.
The first step in mitigating risk is scam detection, which can be manual or automated. One can utilise risk management strategies that include Risk Ops tools, fraud detection software, and company policies. Staff engaged in this process can range from risk managers and trust officers to fraud analysts.
Real-time transaction fraud detection is a critical part of business operations for online enterprises, where attacks can come in many modes and affect the business adversely.
Fraud Detection Techniques
Techniques for scam detection typically include data analysis in some form. Such approaches can be broadly classified into AI-based and statistical data analysis methods.
Statistical data analysis
As the name suggests, this includes different statistical operations ranging from fraud detection, collection, and validation. These include:
a. Statistical parameter calculation: This includes evaluating different statistical parameters ranging from performance metrics, probability distributions, and averages for fraud-related data gathered during the data extraction process.
b. Probability distributions and models: This approach involves mapping the probability distributions and models to different fraudulent activities using various parameters.
c. Data matching: Here, the different data sets are compared against each other based on programmed loops or algorithms.
d. Regression analysis: This involves analysing the relationship between two or more variables (both dependent and independent). It helps comprehend and define relationships between various fraud-related variables to help forecast future fraudulent actions. The forecasting has to do with the usage patterns of fraud variables in a possible fraudulent activity.
AI has emerged as the clear front-runner in the race for more efficient scam detection technologies and includes:
a. Data mining: This technique enables classification and data segmentation to find correlations between data sets that help in scam detection and prevention. It is an integral part of transaction fraud detection.
b. Neural networks: Technology is used to predict fraud-related data that can be mapped against financial documents and audits.
c. Machine Learning (ML): ML algorithms are employed to identify previous fraud patterns and also identify the same in future activities and transactions.
Types of Frauds
Fraud comes in several forms and across various business models. The types can be classified as:
- Credit card information theft: When a person steals credit card information to purchase goods or services that will be charged to the owner.
- Account hijacking: Theft of credentials ranging from identity documentation to financial information to stealing money or sensitive information.
- Fake account creation: Creating fraudulent accounts for applications dealing with finance or personal identification for personal gain. This is often the case with forex trading and stock trading accounts and anything related to them.
- Reward/loyalty abuse: Misusing accounts that offer rewards or loyalty points. This can range from signup promotions to other related rewards.
- Friendly fraud: Accidental fraud occurs when the actual credit cardholder raises a payment dispute because they either forgot that they made the purchase in the first place or had other unanticipated circumstances.
- Affiliate fraud: When affiliates in a marketing arrangement send bad traffic to the target site purposefully. The industry related to online games of chance is highly affected by targeting the PPC (pay-per-click) acquisition model.
- Return fraud: Known to most as online purchase return fraud. Fraudsters resort to buying items in an e-Commerce store with the sole intent of misusing the store return policy for personal gain. The fraudster might do this to obtain free goods or services in exchange for very little or no money.
How Fraud Detection Works
Perpetrators of fraud achieve their objective by discovering weaknesses within any system that handles transactions, financial or otherwise. By gaining access through system loopholes, the fraudster can syphon off funds, pose as someone else, or rack up rewards.
As an example of how scam detection works, we can consider payment processing platforms and providers with their own suite of fraud prevention tools. The tools collect user card and transaction data, which are mapped to prior transactions. These tools work by blocking users with a known history of fraudulent transactions. The transaction fraud detection approach uses the vast reserves of historical data analytics, making it a considerably useful system. However, since these companies avoid chargebacks, the accuracy is exceptional most of the time.
Advanced Fraud Detection Systems
Advanced fraud detection systems go well beyond historical data and come packed with complex features that can be chosen according to enterprise requirements.
Internal Fraud Prevention Systems
Creating a self-sustaining fraud prevention department within a company can be a reality with the right technical know-how and IT resources. This will require employees from risk management backgrounds with expertise in transaction fraud detection systems. The company can benefit from this product knowledge and specialisation that can only come from long-term employees that work within the company.
The only problem with this approach is it becomes hard to scale as companies will need to be on top of when frauds will spike or decrease. Based on this, the staff can be increased or decreased, leading to complications in terms of payroll management and related tasks.
Third-Party Cloud-Based Providers
Third-party cloud-based solutions have cutting-edge risk management technologies, such as fraud prevention API, which work well for present-day online enterprises. The advantages of such solutions include scalability and turnkey delegation, mitigating salary overheads and liabilities. Infrastructure maintenance falls on the third-party providers, which is also an added advantage.
The challenges with such providers are that integration processes will be complex, and tech offerings must be chosen according to requirements, since each provider is unique.
More than one scam detection and prevention solution is employed to ensure maximum protection from attackers. The obvious advantage is the employment of both internal and external teams, giving you scalability, accelerated manual reviews, gapless security, and data enrichment.
Any enterprise that deals with online transactions and finance on any level needs to prioritise scam detection and prevention. This can be achieved by planning and executing fraud detection and prevention strategies. Not only is it prudent to adopt this approach, but it also lines up with regulatory and compliance requirements that most organisations need to fulfil. With the latest in AI and statistical techniques, you can ensure your business will keep fraud risk to a minimum and the stakeholders happy!
What is the cost of fraud detection systems?
This depends on the technology used and whether it will be used in-house or be outsourced to a third party.
What are the primary product features of fraud detection tools?
Fraud detection and prevention tools involve risk mitigation by monitoring and reviewing user actions. Device fingerprinting, social media lookup, and data enrichment are part of the fraud detection and transaction fraud detection tools.
How can fraud detection systems help my enterprise?
These solutions help mitigate account takeovers, identity theft, and chargeback rates. There is also the advantage of fulfilling regulatory and compliance norms in the industry.