Empowering Organisations with AI-Enabled Data Security: A Roadmap for Fraud Detection

In March this year, the Reserve Bank of India (RBI) asked banks to brace for heightened cybersecurity threats. This comes after the regulator’s latest round of Cyber Security and Information Technology Examination (CSITE), and an earlier report that revealed the number of banking frauds in the banking sector in the first half of FY24 had risen to 14,483 cases. For context, the number was at just 5,396 at the same time in the previous year.

 

The fact that this rise in fraud numbers comes alongside the arrival of generative Artificial Intelligence (AI) is no coincidence. The rising popularity of accessible generative AI has lowered the level of technical skill one requires to commit fraud, thereby putting financial organizations at risk.

 

Synthetic identity fraud, where real and fake information is combined to form new identities, is one of the fastest-growing forms of fraud, comprising more than a staggering 85% of identity fraud cases.

Thankfully, increasingly sophisticated technology works both ways – while newer forms of AI are being used to commit fraud, they can also be used to prevent fraudulent activities. Generative AI is thus being used along with traditional machine learning (ML) algorithms to address complex fraud patterns.

How generative AI is augmenting traditional fraud detection methods

Generative AI models that have been trained on large data sets based on legitimate transactions can detect and flag irregular transactions. It can also be utilized to create synthetic data sets that mimic the patterns and behaviors of fraudsters and strengthen existing data sets. This helps create a more robust training ML model that can quickly and effectively identify fraudulent activity. 

 

What’s more, generative AI models can analyze reams of use data from several sources, including search and purchase history, to facilitate a deeper understanding of user patterns and preferences. As a result, any deviations from regular patterns can be identified and flagged immediately.

 

Several financial organizations have been quick to capitalize on the immense potential of generative AI in combating fraud. Earlier this year, payments technology giant Mastercard announced its decision to launch ‘Decision Intelligence Pro’ a real-time decisioning solution that leverages generative AI to scrutinize a stunning 1 trillion data points to predict the authenticity of transactions.

 

Initial modeling has shown an average 20% increase in fraud detection rates and a reduction of more than 85% in false positives.

 

Aside from generative AI, financial institutions are successfully leveraging several other aspects of the technology to protect their data and customers. For instance, leading enterprises across the world are using Natural Language Processing (NLP) to extract signals from chat, voice, and Interactive Voice Response (IVR) interactions, which in turn helps them identify and even prevent fraudulent activities more effectively.

 

Some are also using neural networks to parse historical databases of previous transactions, including those that have already been identified as fraudulent. Every transaction this AI model processes helps it learn fraudulent patterns to help flag them in future.

 

Conclusion 

 

As technology evolves, so will the kinds of fraud that financial institutions are exposed to. To effectively address these dangers, it’s imperative that all of us in the financial industry remain on our toes, seeking out the best ways in which to use AI and other technologies to remain one step ahead, always.

                                                        By Rajat Deshpande CEO & Co-Founder, FinBox