Rule-based risk scoring has been used in fraud detection since the early 1990s when credit card companies began using predefined rules to flag potentially fraudulent transactions.
More recently, artificial intelligence (AI) advancements have yielded considerable improvements to the speed, sophistication, accuracy, and effectiveness of risk scoring and analytics.
AI is used in fraud risk scoring and analytics to provide faster, more accurate, and more efficient identification of fraud than can be achieved through traditional rules-based approaches.
AI is particularly well-suited to risk scoring because it can analyze large amounts of data from various sources, including transaction histories, user behavior, and external data sources, to identify patterns and anomalies that may indicate fraudulent activities, and either flag the transactions for review, or block them altogether – all in real-time.
Here are some common uses of AI in risk-scoring and their impact on the fight against card fraud.
Machine learning (ML) is a subset of AI that involves training algorithms to make predictions or decisions based on data. In other words, it allows computers to learn from data without being explicitly programmed.
The primary advantage of ML over rule-based fraud detection is its ability to learn and adapt to changes in fraud tactics and quickly recognize subtle patterns not immediately apparent to human analysts.
Deep learning is a subset of machine learning that uses layered neural networks to analyze large datasets. Neural networks are a set of algorithms that are designed to recognize patterns in data similar to the way the human brain processes information.
Deep learning algorithms are called “deep” because they are designed to learn from data in multiple layers. Each layer of the neural network learns a different level of abstraction, with lower layers learning simple features and higher layers learning more complex features.
In the same way traditional machine learning is often better than human analysts at detecting fraud, deep learning algorithms are often more effective than “traditional” ML.
Deep learning often underlies behavioral analysis algorithms that analyze things like login and transaction patterns, and device, network, and session data to detect account takeover fraud.
Natural Language Processing (NLP)
Natural Language Processing is a technique that allows computers to understand and analyze human language. NLP is increasingly used in risk-scoring to analyze unstructured data, such as social media posts or customer service conversations.
Subsets of NLP that may be used in fraud detection include:
- Sentiment analysis, which analyses text data for sentiment that may indicate increased risk.
- Entity recognition, which can extract entities, such as names, dates, and locations, from text data.
- Topic modeling, which can be used to identify topics that are discussed in text data.
Robotic Process Automation (RPA)
In risk scoring, RPA can be used to automate repetitive, manual tasks related to data collection, analysis, and decision-making, thus reducing manual errors, and improving the accuracy and efficiency of the risk-scoring process.
In particular, RPA can be used to automate:
- Data processing like sorting, filtering, and analyzing data to identify patterns and trends
- Risk assessment by using predefined rules and algorithms to identify potential risks and flag suspicious activity for further investigation
- Decision-making for risk scoring, such as determining the level of risk associated with a particular transaction or account.
Predictive analytics uses historical data to predict future outcomes. In risk scoring, predictive analytics can identify potential fraud trends and patterns before they become significant problems.
Unlike the other AI methods discussed here, predictive analytics can be used to build and optimize fraud risk scoring models over time based on input from analysts and its own automated analysis.
Artificial Intelligence in Risk Scoring and Analytics: The Game Changer
As fraud techniques continue to grow in complexity and sophistication, the integration of AI in fraud risk scoring has become essential for businesses and financial institutions to protect themselves and their customers. AI provides a proactive approach to fraud prevention and helps companies stay ahead of the curve in detecting and mitigating fraud risks.
REDi specializes in fraud solutions for community banks and credit unions, uniquely qualified to tackle the challenges of smaller financial institutions. Feel free to reach out to us to learn more about our services and how we can help you.