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The Battle to Detect Fraud: Why AI Is Financial Services’ New Recruit

In 2023, fraud scams and schemes totaled $485.6B in projected losses globally, while an estimated $3.1T in illicit funds1 worked its way through the global financial system. With the increasing sophistication of fraud tactics, traditional methods of fraud detection and prevention are increasingly being outmatched and outgunned. Deploying artificial intelligence (AI) in fraud management can offer a significant upgrade to your fraud prevention firepower. In this article we’ll look at five ways AI is being used to detect fraud and accelerate prevention strategies.

1. As It Happens: Real-Time Transaction Monitoring

Traditional methods of fraud prevention often depend on manual reviews and post-transaction analysis, both of which lead to delays in identifying fraudulent activities. Real-time transaction monitoring revolutionizes this process. It enables organizations to spot unusual patterns or anomalies that might signal fraudulent activity—and take immediate action.

Let’s take a look at how real-time transaction monitoring operates:

  • A transaction occurs and the data is immediately captured and sent to the monitoring system.
  • Algorithms then analyze this data in real-time taking into account factors such as transaction amount, location, and the customer’s historical behavior.

  • The system compares the current transaction against a vast database of known fraud patterns and uses machine learning techniques to identify new and emerging fraud trends.

  • If it identifies a potentially fraudulent transaction, the system triggers an alert to the organization’s fraud detection team.

  • This team reviews the transaction details and makes an informed decision on whether to block the transaction or let it proceed.

This entire process happens within seconds, ensuring that fraudulent activities are caught and dealt with swiftly. By quickly detecting and stopping fraudulent activities, businesses can maintain the trust of their customers and partners.

2. See the Future: Predictive Analytics

Predictive analytics is a cutting-edge, data-driven technique that uses historical data, algorithms, and machine learning to forecast future outcomes. By analyzing vast amounts of data—transactional, behavioral, and contextual—organizations can identify patterns that signal fraud.

This proactive approach helps create early warning systems, prevent threats, reduce financial losses, and maintain customer trust. Predictive analytics uses a diverse toolkit of techniques and methodologies to turn data into actionable insights:

  • Machine learning focuses on developing algorithms that can learn from data without being explicitly programmed. These algorithms analyze vast datasets to detect unusual patterns or behaviors that may signify fraud, continuously improving their accuracy over time.

  • Forecasting and time series analysis examines historical data to uncover patterns and understand underlying trends. Statistical techniques are applied to predict future outcomes based on observed trends and seasonal variations.

  • Text analytics and natural language processing (NLP) evaluates textual data, such as emails, blog posts, or social media content, to detect potential warning signs of fraudulent activities. Techniques like sentiment analysis, topic modeling, and entity extraction are used to identify patterns and associations in unstructured text data.

  • Geospatial and location analytics profiles transaction patterns based on geographical data like IP addresses or GPS coordinates, rapidly detecting anomalies (like multiple logins from different locations or transactions initiated from a high-risk location).

3. Get the Big Picture: Link Analysis

AI can analyze data connections between different entities to detect fraud in complex schemes and networks, discovering the relationships that exist between various entities in the data. It can then identify accounts that share suspicious details, track down accounts connected to known fraudsters and uncover the links that connect them to others.

There are three main purposes to using link analysis:

  • Identify known patterns: Once a network is mapped out, its connections often form recognizable patterns. If you know what these patterns look like, link analysis can help you find them on a large scale. For instance, when a new user joins a social media platform, they typically start by connecting with close friends and family before expanding to mutual acquaintances. Recognizing this pattern helps to understand normal user behavior.

  • Detect anomalies: Since network relationships usually follow predictable patterns, link analysis is also effective for spotting anomalies or deviations from those patterns. For example, if a new social media user rapidly connects with many people who have no mutual connections, this unusual behavior might suggest suspicious activity.

  • Discover new patterns: Networks are dynamic and constantly evolving, which means new patterns can emerge over time. Link analysis helps in identifying these emerging patterns, providing valuable insights into how network behaviors are changing.

4. Improved Risk Assessment: AML Compliance and Screening

AI enhances AML compliance and screening by automating data analysis, improving risk assessment, detecting complex patterns, and reducing false positives.

It can help in assessing the risk profile of customers by analyzing various data points including transaction history, geographic location, and behavioral patterns. By continuously updating risk profiles, AI systems ensure that high-risk customers receive more scrutiny, reducing the chances of money laundering.

The screening of transactions against global watch lists, sanction lists, and politically exposed persons (PEPs) lists can also be automated. NLP techniques can enhance the accuracy of matching names and entities, reducing false positives and ensuring that legitimate transactions are not unnecessarily flagged.

For high-risk customers, AI can conduct enhanced due diligence by gathering and analyzing information from various sources such as social media, news articles, and public records. This comprehensive approach ensures that no critical information is overlooked during the compliance process.

Detailed audit trails of all compliance activities can also be made, providing a clear record of actions taken during the screening process. This transparency is essential for regulatory reporting and internal audits, ensuring that organizations remain compliant with AML regulations.

5. Keep It Conversational: How Chatbots Are Fighting Fraud

AI-powered chatbots offer a fast and efficient way to detect fraud in the banking industry and the market continues to grow, forecasted to reach $15.5 billion by 2028.2 It’s a win-win for customers and financial institutions too, as AI-powered chatbots deliver enhanced CX—with 62% of people preferring chatbots3 over waiting for human advisors—as well as providing a fighting frontline against fraud:

  • Setting up alerts: AI-powered systems can swiftly detect card or account hacking attempts. When this happens, a quick verification message from the chatbot can help prevent losses by confirming suspicious activities with the customer immediately.

  • Analyzing trends: AI analyzes transaction patterns to identify similarities with known fraudulent activities and take action. AI can also detect deviations in writing style or unusual IP addresses.

  • Early resolution: Conversational chatbots minimize the wait time for customers reporting fraud. The faster a fraud query is logged, the quicker it can be resolved.

  • Real-time support and analysis: AI chatbots offer 24/7 monitoring and support to flag and temporarily block suspicious activities, alerting the user to verify the transactions.

  • Personalized experience: By analyzing transaction histories, chatbots tailor their interactions to individual customers, providing a secure and customized experience that adds another layer of protection against fraud.

  • Biometric authentication: Voice and facial recognition offer additional security. For instance, if a user’s voice doesn’t match the stored voiceprint, the chatbot can immediately alert the user and the bank, all in real time.

From real-time transaction monitoring to chatbots, by leveraging AI technologies to detect fraud, financial institutions can stay ahead of fraudsters, protect their customers, and maintain compliance with regulatory requirements. To dive deeper, download our whitepaper to learn how AI can power your fraud management.

Julien Dumery

Julien Duméry

Senior Director of Global Growth, Financial Crime & Compliance

Contact Concentrix

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