Fraud in financial services: Leaning on generative AI to protect a rapidly expanding attack surface

AI technologies, particularly machine learning, have been used for many years within finance to execute high speed trading, detect fraud, improve risk scoring, and deliver overall efficiencies. However, we are now seeing a rapid adoption of AI/ML for monitoring and detecting, along with responding to fraud across the growing attack surface. Most recently, generative AI is being deployed to increase the impact of a fraud analyst in really amazing ways, such as:

One of the key strengths of AI in fraud detection lies in its ability to detect anomalies or deviations from normal patterns of behavior. Machine learning algorithms can learn from historical data to establish a baseline of normal behavior for each account or transaction type. Any deviation from this baseline can then trigger an alert for further investigation, potentially flagging fraudulent activity that might have gone unnoticed by human analysts. The emergence of generative AI has further improved anomaly detection — by learning the underlying structure of genuine transactions, generative models can detect subtle deviations indicative of fraudulent activity, complementing traditional anomaly detection methods. 

AI-powered fraud detection systems can also analyze user behavior across multiple channels and touchpoints to identify suspicious activities. Know Your Customer (KYC) is an entirely new way of managing this risk. By monitoring factors such as transaction frequency, location, device type, and interaction patterns, these systems can detect anomalies that may indicate fraudulent behavior, such as unauthorized account access or unusual spending patterns.

Another valuable application of AI in fraud detection is the analysis of networks and ecosystems, which involves examining the relationships and connections between entities (e.g., customers, accounts, transactions). Systems supported by AI and generative AI can uncover complex fraud networks and identify previously undetected links between seemingly unrelated entities, facilitating the detection and disruption of organized fraud rings.

The deployment of AI in fraud detection and prevention is already yielding tangible results across the financial services industry. Leading banks, payment processors, and fintech companies are leveraging AI-powered solutions to detect and prevent fraud in real time, protecting both their customers and their bottom line. For example:

• Payment fraud prevention: AI algorithms can analyze transaction data in milliseconds to identify fraudulent transactions, such as unauthorized purchases or account takeovers, and block them before they are completed.

• Identity verification: AI-powered biometric authentication systems can accurately verify users’ identities based on facial recognition, voice recognition, or behavioral biometrics, reducing the risk of identity theft and account fraud.

• Anti-money laundering (AML) compliance: AI systems can analyze vast amounts of transaction data to identify suspicious patterns indicative of money laundering or other illicit activities, helping financial institutions comply with AML regulations and mitigate regulatory risk.

While the benefits of AI in fraud detection are undeniable, it is essential to address ethical and regulatory considerations to ensure responsible and equitable use of these technologies. AI algorithms should be transparent and explainable, enabling stakeholders to understand how decisions are made and identify potential biases or errors. Also of course, while AI can automate many aspects of fraud detection, human oversight remains critical to review flagged cases, investigate suspicious activities, and make informed decisions based on contextual information that AI systems may lack.

However, one area of increasing concern surrounds data privacy and security. Financial institutions must safeguard customer data and ensure compliance with data protection regulations to maintain trust and confidence in AI-powered fraud detection systems. As FSIs incorporate third parties into the fraud process (for example, in the case of generative AI), combining public information with proprietary data naturally raises privacy and regulatory concerns. Safeguarding their systems with the right tools is thus imperative.

At its core, fraud is undoubtedly a data problem. As financial institutions continue to innovate in the realm of fraud detection and prevention, the role of Elasticsearch®, an open-source distributed search and analytics engine, becomes increasingly significant. Elasticsearch's scalability, real-time data processing capabilities, and advanced search functionalities make it a valuable asset in the fight against fraud in financial services.

• Real-time data analysis: Elasticsearch's distributed architecture allows financial institutions to ingest, index, and analyze vast volumes of transactional data in real time, across time and space (including geospatial analysis), enabling them to detect and respond to fraudulent activities as they occur.

• Complex querying and analysis: With Elasticsearch, fraud analysts can perform complex queries, aggregations, and machine learning-driven analytics on large data sets with ease. By leveraging Elasticsearch's advanced search and analytics features, financial institutions can uncover hidden insights and correlations that may signal fraudulent behavior.

• Scalable infrastructure: Elasticsearch's scalable infrastructure ensures that financial institutions can handle the growing volume and complexity of data associated with fraud detection and prevention efforts. Whether analyzing millions of transactions or conducting network analysis to uncover fraudulent schemes, Elasticsearch provides the necessary scalability and performance to support these critical tasks.

• Gen AI: The Elasticsearch Relevance Engine (ESRE) is a powerful tool that combines artificial intelligence and text search capabilities to create smarter generative AI applications in financial services. With ESRE, developers can make search results more accurate and relevant by using advanced algorithms that integrate with large language models for generative AI search engines. Elastic AI Assistants can enable faster investigations, reduce time to resolution, and enable every analyst to have a digital partner to help guide them quickly and completely through the remediation process.

• Data privacy and security: In highly regulated industries like financial services, ensuring data privacy and security is crucial. Elastic® includes native support for role-based and attribute-based access control to ensure that only those roles with access to data can see it, even for chat and question answering applications. Elasticsearch can support your organization’s need to keep certain documents accessible to privileged individuals, helping your organization to maintain universal privacy and access controls across all of your search applications.

The role of generative AI in financial fraud detection and prevention is transformative, offering financial institutions the ability to combat fraudulent activities with unprecedented speed, accuracy, and efficiency. However, to realize the full potential of AI-powered fraud detection, financial institutions must also leverage the right tools and technologies, such as Elasticsearch, to effectively analyze, interpret, and respond to fraud-related data. With continued innovation and collaboration, generative AI and Elasticsearch will undoubtedly remain integral tools in preventing fraud in financial services, safeguarding the integrity of the financial system and protecting the interests of customers and FSIs alike.

Learn more about how to accelerate fraud detection and prevention with Elastic.