AI, Large Language Model

Impact of LLMs and Generative AI on Reducing Banking Fraud

When it comes to banking fraud, AI technology such as Large Language Models (LLMs) and Generative AI can be viewed as a double-edged sword. It is being employed by scammers to generate convincing phishing emails, fake banking documentation and IDs, or even generate deepfake voices and videos that impersonate banking employees.

Never before has it been as quick, easy, and affordable to create sophisticated content at scale that can appear trustworthy to even the most cautious consumers. 

This ultimately is fueling an increase in fraud cases and related losses for banks. The need for them to stay ahead of the spike in sophisticated fraud schemes and consumer exploitation is imperative. To do so they are turning to AI and deploying advanced AI-powered systems and techniques. 

Using Large Language Models for Fraud Detection and Prevention in Banking

With the power to analyze large datasets and detect patterns and anomalies that were difficult to discover using traditional technologies and methods, LLMs offer enhanced fraud detection and prevention capabilities for banks.  Here are some ways in which LLM capabilities can help banks in their battle against increased fraud.

1. Pattern Recognition and Anomaly Detection  

• LLMs can process vast amounts of transactional data to identify complex fraud patterns that deviate from normal behavior. 
• They also excel at identifying previously unseen patterns by continuously learning from historical data, current activities, and external fraud trends.They can detect new fraud techniques early, even if no prior rule exists in traditional systems.
• LLM capabilities detect subtle anomalies, such as unusual transaction sequences, account access patterns, or geographic activity shifts. Such indepth analysis is akin to finding a needle in the haystack when it comes to fraud prevention. 
• They can categorize fraud attempts into types (e.g., identity theft, credit card fraud, insider fraud) for better prevention strategies and resource allocation.

2. Analysis of Customer Conversations & Behavior

• By analyzing real-time customer conversations and interactions with agents, across voice, email, chat, and messaging channels, LLMs can spot potential fraudulent intents. This can assist agents in real time to further authenticate a customer, validate transactions, or lock down an account, if needed.
• Contextual understanding and analysis of unstructured data, such as emails, text logs, or social media, helps detect fraudulent schemes that exploit specific vulnerabilities. This includes detecting scam language in phishing emails or fraudulent requests.
• Sentiment analysis capabilities can also help identify stress or urgency that may indicate fraudulent activity.
• LLMs can create a multi-dimensional profile for each customer by integrating data from transaction histories, account activity, and communication patterns. These profiles help detect deviations, such as a sudden increase in high-value transactions or atypical communication methods.

3. Enhanced Risk Scoring & Monitoring

• LLMs can assign dynamic risk scores to transactions or interactions based on real-time analysis of data. High-risk activities can be flagged for immediate action or escalated for human review.
•  LLMs analyze activities across multiple channels (e.g., ATMs, online banking, mobile apps) to correlate behaviors and detect fraud attempts spanning multiple touch points.

How LLMs and Generative AI Can Automate Workflows in Fraud Detection

Fraud detection and prevention is not confined to a single area of banking. It exists at multiple interfaces where customers interact and transact either digitally (via chatbots, web portals, mobile apps, social media, email, messaging or voice channels), or with live agents (via voice calls or in person). It is also an important role in back office operations that monitor risk and fraud and that are tasked with loss prevention. 
Let’s look at how LLMs and Generative AI can help detect and prevent fraud in both front office and back office operations by automating workflows that can assist agents and employees in real time.

Agent Assist for Customer Support

By analyzing ongoing customer interactions and conversations (calls, chats, emails etc.) for suspicious activity such as unusual transaction requests or account behaviors, an AI banking assistant can provide agents with real-time fraud alerts while they are handling a customer conversation or chat. Gen AI assistants can give agents real-time prompts, such as specific security questions or they can flag potential fraud using sentiment analysis.

By continuously monitoring transactions for suspicious activity, such as location-based anomalies, multiple login attempts, or rapid large withdrawals, LLMs can recommend immediate actions like account holds or proactive customer outreach.

Generative AI can also be used to provide customized responses to common queries about suspected fraud such as account lock notifications, transaction clarifications, and more. This helps agents deal with customer queries about fraud in ways that are compliant, accurate and that adhere to company guidelines.

When fraud-related anomalies are detected by LLMs, an agent can be updated in real time and the AI assistant can automate customer outreach for verification and follow-up.

AI Agents for Employee Support

Generative AI-based automation is equally relevant in supporting back office employees, streamlining fraud detection techniques by analyzing historical and cross-channel data efficiently. Here are some highlights of how it can support internal staff in tackling fraud:

Gen AI can be deployed to correlate data across the multiple touch points and channels used by a bank. This helps identify fraud attempts by linking data from phone calls, emails, mobile app usage, and in-branch activity. This creates a unified fraud risk profile for each account or customer.

The fact that LLMs are particularly suited to sifting through large datasets of banking transactions helps to quickly identify unusual patterns or inconsistencies. For example, it can be used to automate comparisons across accounts to detect coordinated fraud attempts. LLMs can also analyze years of banking data to uncover previously undetected fraud schemes, identifying previously undiscovered vulnerabilities that fraudsters can exploit.

It can also be deployed to enhance employee efficiency on document processing and data analysis. AI can be applied to review customer-provided documents (e.g., IDs, proof of income) for forgery or manipulation using image recognition and data consistency checks. By validating information across internal and external databases, anomalies can be quickly identified.

The Impact of LLM-Powered Automation on Banking Fraud Prevention

Proactive Fraud Prevention

LLMs enable banks to predict and intercept fraud before it occurs by recognizing early warning signs and patterns. This reduces losses associated with unauthorized transactions and reputational damage. 

Timely Customer Alerts

Banks can use LLMs to analyze data in real-time and alert customers about suspicious activities, such as unusual transactions or login attempts. Immediate communication empowers customers to confirm or deny suspicious activity, preventing escalation. Additionally, real-time alerts create a sense of security with banking customers.

Improved Fraud Response Rates   

With detailed insights, banks can prioritize high-risk cases and respond faster to breaches. This improves the effectiveness of fraud investigations and recovery efforts.

Reduced False Positives

LLMs reduce the number of false positives by understanding context and customer behavior better than traditional rule-based systems. This minimizes unnecessary alerts and enhances customer trust.

Increased Employee Efficiency

Automation reduces manual workloads, allowing employees to focus on complex fraud investigations and decision-making. This speeds up case resolution times by providing instant insights and prioritization. Quick resolutions to fraud-related concerns also enhance the customer experience.

Enhanced Compliance

By analyzing vast datasets and interactions, LLMs help banks meet regulatory requirements for fraud monitoring and reporting.

Conclusion

Banking fraud is on the increase as fraudsters leverage generative AI to conduct increasingly sophisticated scams. To combat this, banks are integrating LLMs and generative AI into their fraud detection workflows across the organization and across all the touchpoints with customers. Increased automation and real-time data insights are enabling them to address the challenges they face in detecting fraud, reducing associated losses, and preventing future breaches.