How novel technologies are transforming fraud detection in the BFSI landscape

By Prasad Pathapati, Head of Engineering – mPokket

As digital transactions become increasingly ubiquitous globally, there is a concomitant threat of fraud. With fraudsters continuously contriving novel ways to exploit technology loopholes, legacy fraud detection mechanisms find it more challenging to thwart such fraud.

As per research from an overseas body, neo-banks and other fintech firms experience an average fraud rate of 0.30%. This is twice the rate of fraud on credit cards (0.15% to 0.20%) and triple the rate on debit cards (0.10%).

Most people are aware that it is extremely difficult to pinpoint fraud as it happens and even tougher to recover the loss after it has occurred. In such scenarios, prevention is the best alternative since the stakes remain high if the issue is left unaddressed. Besides financial loss, the negative outcome includes reputational damage. While reputational damage for traditional banks and other financial entities can be bad, the impact on fintech firms barely a few years young could be devastating.

However, technology solutions enabled by AI (artificial intelligence) now make it possible to stop most fraud before it occurs. Innovative technologies are transforming the fraud detection ecosystem by helping in the identification and prevention of fraud in real time. With AI and ML (machine learning) based fraud detection systems, the novel technologies are assisting BFSI players to always stay a step ahead of cybercriminals.

Outlined below is an overview of the technology-related fraud detection mechanisms:
AI and ML-based systems: Both AI and ML tools can analyse massive mounds of data to detect fraud in real-time. Having been trained on historical data, AI-enabled systems can discover patterns that indicate fraud and automatically move to thwart these attempts.

Big data analytics: As data keeps burgeoning by the day, these large troves of information are being used to identify specific patterns that indicate fraud. This data includes everything from social media activity to credit and debit card transactions.

Biometric authentication: Be it fingerprints or facial recognition, biometric authentication helps to confirm a user’s identity. Thereby, fraudsters can be discovered well in time and debarred from using stolen identities or creating fake accounts.

Continuous authentication: This deals with the monitoring of user behavior over time to ascertain whether the person is who he/she claims to be. Such authentication involves tracking elements such as mouse movements, keystroke patterns, and other behavioral data.

Risk-based authentication: This is done by evaluating the risk of any transaction or login attempt that is based on factors such as the user’s location, history, and the device used. By red-flagging suspicious activities, it helps prevent fraudulent transactions.

Blockchain: This digital ledger-based technology provides a secure, tamper-proof means to store data. Be it the BFSI landscape or supply chain management, blockchain ascertains there is transparency in entries or the movement of goods while also verifying the authenticity of deals.

Real-time monitoring: Systems based on real-time monitoring help in detecting fraud as it happens, which allows companies to respond quickly to prevent losses. Triggers such as fraud alerts and notifications can set this system in motion the moment something amiss is detected.

IoT and sensor-based security: IoT and smart sensors can help detect fraud in the domains of physical security and supply chain management. This is done by deploying sensors that monitor physical spaces for any suspicious activities or track the movement of goods.

Collaborative security: This relates to the sharing of data across various organisations for detecting and preventing fraud. It could involve sharing information on fraudsters or any suspicious activities or working collaboratively to discover and track fraud across diverse systems.

Machine vision: Meant for pinpointing fraud in fields like facial recognition or document verification, it concerns the use of computer vision algorithms to analyse images and highlight inconsistencies or suspicious patterns.

The above constitutes some of the novel technologies spearheading the fight against fraudsters. By integrating these technologies into their fraud detection ecosystem, companies will be better placed to safeguard themselves from fraudsters while preventing financial loss or reputational damage. Undoubtedly, AI, ML, and other new-age technologies can help companies bounce back from fraud and also prevent such unwanted incidents from happening in the first place.

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