Consumers of banks, financial institutions (FIs) and fintechs have to continuously fend a barrage of online scams, with the volume continuing to increase each year. FIs, particularly fintechs face an increased risk of being targeted by novel forms of fraud due to the constant ingenuity of fraudsters in devising innovative tactics to deceive businesses and consumers. Moreover, these threat actors keep coming up with newer ways to circumvent existing fraud detection mechanisms.

Given the significant technological advancements in recent years, automation has emerged as an imperative for these entities in their fight against fraud. With fraud schemes growing increasingly complex and sophisticated, machine learning (ML) techniques have evolved in tandem, equipping companies with the ability to defend themselves effectively. 

This ongoing development in technology is vital in enabling businesses to stay ahead of fraudsters and safeguard themselves and their customers from potential financial losses.

Deploying AI-Based Fraud Detection Systems 

Tech giants such as Facebook, Amazon, Apple, Netflix, and Google have been utilizing proprietary artificial intelligence (AI) AI tools to enhance both front-end and back-end business processes for some time. However, they are now taking these tools to the forefront of their operations, bringing an AI-first strategy to the core of their business. 

This has set the tone for the rest of the industry, with many following suit. Here is a look at some technologies leveraged by FIs to safeguard themselves from evolving threats:

Machine Learning: Fraud detection solutions have already been developed for a range of industries, including fintech, e-commerce, banking, healthcare, and online gaming. Through ML algorithms, vast amounts of data can be processed, and patterns can be identified to protect businesses of all types from fraudulent activities. 

Deep Learning: Mastercard has leveraged AI to prevent card-related fraud and minimize the occurrence of false declines. By utilizing deep learning models that continuously learn from the 75 billion transactions processed each year across 45 million locations worldwide, the system makes decisions based on a constantly flowing stream of data and self-teaching algorithms, yielding impressive results, with significant reductions in fraudulent activity and false declines.

Natural Language Processing (NLP): Several prominent enterprises, including American Express, Bank of New York Mellon, and PayPal, are leveraging the power of NLP for fraud detection purposes. By extracting signals from chat, voice, and IVR interactions, NLP enables these companies to identify and prevent fraudulent activities more effectively, thanks to its ability to improve anomaly detection over time. 

Neural Networks: This model of AI that emulates the intricate structure of the human brain, is being used by banks to parse a historical database of previous transactions, including those known to be fraudulent. Every transaction the model processes increases its accuracy of detection and adds to its enormous repository of historical information, so, it continually learns the patterns of habitual fraudsters to defeat them.

Decision Trees: This is a type of AI that involves creating a visual representation of a decision-making process. In fraud detection, decision trees are used to identify the most important variables that contribute to fraud and create a framework for identifying fraudulent transactions.

Learning From Others Experiences 

Fortunately, fintechs can leverage AI for fraud detection, following in the footsteps of banks, FIs, and e-commerce companies. Paytm relies on Pi, an AI and ML-powered fraud and risk management platform, which can categorize customers into different risk tiers in lending scenarios. It uses various models to identify unusual patterns and outliers, and protects customer accounts from fraud by analyzing information such as IP addresses, transaction amounts, locations, times, and historical transactions. 

For instance, Razorpay, a major player in the digital payments industry, uses Thirdwatch, which employs advanced AI and machine learning algorithms to help merchants detect risky users, fraudulent orders, and impulse purchases, among other things. 

However, fintech's evolving landscape where they are constantly innovating in newer areas like cryptocurrencies, peer-to-peer lending, Neo Banks, BAAS, and so on, can create opportunities for fraud. Multi-player partnership models can introduce more transactional bits, increasing the risk of fraudulent activity. 

Additionally, financial crimes thrive during economic downturns, as seen in 2008 and during COVID-19. The upcoming economic slowdown and the emergence of new commercial avenues are likely to create more opportunities for sophisticated and innovative fraud tactics.

A recent survey on payments fraud revealed that 70 per cent of businesses believe that fraudsters are outpacing the industry in committing business payment fraud. In light of this, the question arises as to how standard fraud detection models will fare against the ever-evolving tactics of fraudsters. 

Fintechs that specialize in smaller parts of the ecosystem, such as wallets, B2B payments, P2P lending, cross-border payments, and payment platforms, require tailored solutions as a ‘one-size-fits-all’ approach may not be effective.

There is no single approach to prevent fraud. Fintech start-ups are bound to embrace AI-powered transaction monitoring systems as a means of enhancing their fraud prevention capabilities. In an ongoing game of cat and mouse, these startups must stay ahead of banking institutions, which are also utilizing AI tools to regulate their own fraud detection systems. 

Ideally, fintechs and big players in the field of technology should work together to innovate—as both parties struggle to stay ahead in the race to detect and prevent fraud. They should collaborate and build an ecosystem that brings in solution providers in the space of AI in fraud detection. 

-Jaya Mahajanam, Director–Solutions and Presales, E42.ai