A fraud score uses advanced algorithms and machine learning models to prevent chargebacks by assessing the risk level of transactions in real-time. By analyzing various data points such as transaction size, frequency, customer’s purchasing history, IP address, and device information, companies can assign a score to each transaction that represents the likelihood of fraud.
Fraud scores allow fraud teams to use different processes to handle different levels of risk. For example, if a purchase is scored as low-risk for fraud, it can be quickly processed. For moderate-risk transactions, friction can be introduced into the process to gain more information before accepting or declining the purchase. High-risk transactions can be immediately declined.
The introduction of tiered friction-levels has an impact beyond chargeback prevention and minimizing false positive declines. It allows companies to tailor the customer experience they provide. Companies can then pair the friction levels introduced to the risk profile. Safe customers can move through the purchase flow without any issue, providing a great customer experience. Every customer will only encounter as much friction as their risk profile necessitates, balancing the customer experience in line with the danger.
How to Set Up a Fraud Scoring System
To use fraud scoring to prevent chargebacks and provide a great customer experience, it’s important to follow best practices. Below are three of the most important things to consider when building fraud prevention models.
Data Integration
Aggregate data from various sources into a single data lake , including transactional history, customer interactions, and third-party databases, to create a comprehensive view of each transaction. Here are a few examples of the kind of data you can use to prevent chargebacks:
- Transactional Data: Transactional data forms the core of fraud prevention models, providing a detailed account of customer transactions, including purchase amounts, transaction times, and payment methods. This data is invaluable for identifying patterns and anomalies indicative of fraudulent behavior, such as unusual transaction sizes, rapid succession of transactions, or transactions in atypical locations. Examples: Purchase History, Payment Details, Geolocation
- Customer Behavioral Data: Understanding how legitimate customers typically interact with your platform can help in distinguishing fraudulent behavior. Customer behavioral data includes login patterns, navigation paths within the application or website, and typical interaction times. Examples: Login Patterns, Session Duration and Activity, Device and IP Information
- Third-Party Data: Incorporating data from external sources can significantly enhance the predictive power of fraud prevention models. This includes information from credit bureaus, fraud prevention networks, and blacklists of known fraudsters or compromised devices. Examples: Credit Reports, Fraud Databases, Device Reputation Services
- Social Media and Public Records: Publicly available information from social media and other public records can be used to corroborate customer-provided data, adding an additional layer of verification to fraud prevention models. Examples: Social Media Profiles, Public Databases
- Biometric Data: Biometric data is increasingly becoming a viable source of information for fraud prevention. This includes fingerprints, facial recognition data, and voice patterns, which can be used to authenticate user identities securely. Examples: Authentication Attempts, Behavioral Biometrics
Algorithm Development
After building a data lake, the next step to use fraud scoring to prevent chargebacks is to build algorithms to analyze the data it contains. Ideally, these algorithms will identify patterns and indicators of fraudulent behavior with a high degree of accuracy. Threshold cases should be analyzed by human fraud analysts in order to increase sophistication and accuracy over time. Similarly, new data should be used to continuously improve.
Here are some strategic ideas to consider when building a fraud scoring algorithm:
- Define Objectives and Scope: Begin by clearly defining the specific types of fraud you aim to detect and prevent, such as payment fraud, account takeover, or identity theft. Understanding the nature of the fraud will guide the selection of data, features, and modeling techniques.
- Feature Engineering: Feature engineering involves selecting, modifying, or creating new variables (features) from your dataset that are most relevant to detecting fraud. Effective features can significantly enhance the performance of your fraud prevention algorithm.
- Choosing the Right Algorithm: Commonly used algorithms in fraud detection include:
- Supervised Learning Algorithms: Such as logistic regression, decision trees, or neural networks, where the model is trained on a labeled dataset containing both fraudulent and non-fraudulent transactions.
- Unsupervised Learning Algorithms: Useful when labels are not available, algorithms like clustering (K-means) or anomaly detection (Isolation Forest) can help identify unusual patterns or outliers indicative of potential fraud.
- Hybrid Models: Combining multiple models or approaches can sometimes yield better results by leveraging the strengths of each method.
- Fraud Model Training and Validation: Train your model using the prepared dataset, ensuring to split the data into training and validation sets to test the model’s performance on unseen data. Key performance metrics for fraud detection algorithms include accuracy, precision, recall, and the area under the ROC curve (AUC-ROC) .
Threshold Tuning and Continuous Monitoring and Updating
Flagging transactions as approve/decline is one part of threshold tuning, but it is not enough. To properly use fraud scoring to prevent chargebacks and provide a great customer experience, the thresholds for increasing customer friction should also be defined . In other words, the model must work in tandem with the workflows you have established to further investigate medium or high risk transactions.
Fraud patterns evolve, so continuous monitoring of the performance of your algorithm’s overall performance, as well as performance at each risk threshold, is important. This evaluation must include an understanding of the effect of the checkout process on overall customer satisfaction and retention–something that requires cross-team collaboration with customer success, sales, and revenue departments.
Balancing Fraud Prevention and the Customer Experience
When it comes to minimizing the negative impact of fraud algorithms on customer satisfaction, be proactive. Fraud scoring does not work in a vacuum. Companies can use strategies before and after customers make a purchase to minimize the negative impacts. In some cases, proactive measures can actually turn fraud prevention measures into a net positive for customers’ experience. For example, customers of high-value items consistently report that the visible presence of anti-fraud measures reassures them that a company is secure and will protect their personal information.
Customization by vertical, product, known customer expectations, and more require bespoke strategies. However, there are a few things companies should keep in mind:
- Personalization: Customize fraud prevention measures based on customer profiles and purchasing history. Trusted customers or those with a history of legitimate high-value transactions can be subjected to fewer controls.
- Customer Education: Educate customers about the importance of security measures and how they protect their interests. Transparent communication can turn potentially frustrating security checks into positive customer service experiences.
- Rapid Response Teams: Establish dedicated teams to quickly address and resolve disputes, ensuring that genuine customers do not suffer due to false positives in fraud detection.
- Feedback Loops: Implement mechanisms for customers to provide feedback on their experience with security measures. Use this feedback to refine and adjust your approach.
Conclusion
Knowing how to use fraud scoring properly is important to maximize revenue. Comprehensive data, advanced analytics, and continuous learning will build more effective models. When done with the customer in mind and a commitment to ongoing optimization, it’s possible to strike the right balance between chargeback prevention and providing a great customer experience.
These types of customer-centric fraud prevention strategies offer an opportunity for businesses to not only protect their revenues but build trust and loyalty among their customers, turning a potential challenge into a competitive advantage.
