The Fraud Landscape in India’s Digital Economy
The popularization of services like Internet banking, eCommerce and other digital transactions have added lots of convenience into our lives. It has also made us significantly more vulnerable to fraud, a type of crime that is very much on the rise in India.
According to government data, high value cyber fraud cases in the country jumped more than 4x in FY24 compared to the previous year. UPI fraud cases surged by 85% in the same period, hitting 13.42 lakh incidents in FY24. Enterprises are also being affected – according to the 2024 PwC Global Economic Crime Survey, 59% of Indian organizations surveyed said they faced financial or economic fraud in the past 24 months. It is a pressing concern because of the impact fraud has on bottom lines – according to a report last year by the Association of Certified Fraud Examiners, the average organization loses 5% of its annual revenue to fraud.
AI-Powered Threats: How Fraudsters Use AI
As the nation keeps emerging as a global economic powerhouse, Indian enterprises face fraud threats from all kinds of bad actors, ranging from organized criminals and local fraud rings to cyberterrorists, nation-state actors and even malicious insiders. In recent times, their attacks have exponentially increased in variety, scale, sophistication and frequency – and AI has a big part to play in all of this. Here are some of the advanced fraud trends we are seeing today:
- Fraud-as-a-Service and GenAI Plug-ins: AI has lowered the barriers to entry for fraudsters. Nowadays, attackers with little to no coding experience have easy access to genAI plug-and-play packages that help in committing fraud attacks faster and on a broader scale. Banking in particular involves authentication through multiple factors, one of which is verification against government identities. Reports indicate that Aadhaar-linked identity data is readily available on dark web marketplaces at nominal fees.
- Phishing at Scale with AI: Once all the documents and details to carry out fraud are provided, AI then revolutionizes the process of identifying and approaching potential victims. GenAI-enabled phishing attempts through text-based mediums are more sophisticated and able to mimic legitimate communications with remarkable accuracy. Most detrimentally, AI can carry out hundreds of these phishing attacks at the same time.
- Deepfakes and Synthetic Identities in Finance: The banking industry is facing a serious problem with synthetic identities, involved in 85% of identity fraud cases. This is when attackers use a mix of real and fake information to create a fictitious identity used to open bank accounts, apply for loans and get as much money as possible until the credit maxes out. AI can help create countless realistic identities and launch hundreds of attempts from around the world to reap benefits.
Why Traditional Fraud Detection Methods Are Falling Behind
These advancements in fraud technologies are also coinciding with skyrocketed customer expectations for seamless digital experiences. They demand fast, secure and convenient transactions, switching quickly to competitors if these expectations are not met. Therefore, a fundamental dilemma exists for organizations when it comes to balancing fraud control with optimized customer experience. Add too many controls, and friction is added to the customer experience – add too few, and the chance of fraud exponentially increases. These problems are getting exacerbated due to the limitations of traditional methods in the face of these advanced threats:
- Accurate detection is derived from effective feature engineering, which involves turning unstructured data into valuable features (transaction velocity, location, time) that draw attention to possible fraud trends and anomalies. Doing it manually is extremely time-consuming, requiring intensive data extraction, transformation and cleaning. Even after you do this, essential parameters for accurate detection may be missed.
- Traditional fraud detection is a heavily resource-intensive operation, requiring human intervention for the bulk of its tasks, including model tuning, updates and verification of flagged transactions. This quantum of work could lead to fatigue, opening the door for sophisticated fraud attempts to go through the cracks.
- There is a lack of context in traditional systems, limiting its effectiveness in detecting complex or subtle fraud attempts. On the other end of the spectrum, this could also lead to lots of false positives that hinder the customer experience.
- As transaction complexity and volume increases, traditional systems may struggle to efficiently scale.
- Lack of adaptability in these systems makes your organization constantly reactive in the face of evolving fraud challenges.
- Because fraud transactions are far less compared to valid transactions, this could adversely impact traditional training data models, making them imbalanced and skewing their ability to accurately detect fraud.
As a result, we are seeing a much-needed enhancement in Fraud Prevention and Detection strategies – and again, AI at the forefront of this, too. Organizations are fighting AI-powered fraud attacks with AI-enabled countermeasures that have the added benefit of also improving customer experiences. This shift is seen across all kinds of industries in India.
Nandan Nilekani recently revealed that AI is already being used to detect fraudulent activities within India’s Income Tax and GST systems, through measures like scrutinizing GST registrations and preventing fraudulent claims for input tax credits.
Recently, the Department of Financial Services (DFS) directed banks to adopt advanced technology, including AI and Machine Learning, to tackle the increasing number of mule accounts used to facilitate illegal transactions. They were asked to explore MuleHunter.AI, an AI/ML-driven solution developed by the RBI that offers enhanced capabilities in detecting fraudulent activities and tracking suspicious accounts.
HDFC Bank recently released an automated dispensation platform where bots reach out to customers to auto-confirm transactions without any kind of human intervention. This is done by studying variables like the device being used for the transaction, IP address, etc.
So, considering where the trends seem to be heading, how does your organization effectively integrate AI in your fraud detection and prevention processes?
How AI Strengthens Enterprise Fraud Prevention
It all starts by taking all the numerous workflows that exist for different stages of the customer journey, and integrating them all into a comprehensive, unified oversight system crucial for combating fraud. Then, you can integrate AI into all these various components:
- Pattern Recognition + Anomaly Detection: AI can comb through all your transaction data and use pattern recognition to establish baselines of normal activity relative to every customer. That sets the table for effective anomaly detection that immediately flags transactions and activities deviating significantly from this baseline. These anomalies can include unusually high transaction amounts, quick transactions from two completely different locations, strange time intervals between transactions and more.
- Real-Time Monitoring and Automated Response: The longer fraud incidents stay undetected, the greater the potential business impact. AI can process and analyze much faster than humans, enabling faster real-time fraud detection and response. It continuously analyzes incoming data streams and immediately blocks suspicious fraudulent activity if you feed the right parameters into it.
- ML-Enabled Continuous Model Enhancements: In the face of increasingly advanced attacks, you must shift from a reactive fraud prevention posture to a proactive one. Machine learning can significantly enhance your predictive models, with AI’s iterative nature allowing you to continuously refine your fraud algorithms and seamlessly adapt to the evolving tactics used by fraudsters.
- Natural Language Processing (NLP) To Uncover Text-Based Phishing: As phishing attempts become more sophisticated and realistic, they are likely to escape trained human eyes. NLP, on the other hand, can be used to interpret massive amounts of language related data through word and text analysis by processing different patterns like numeric, time and causal. Through this deep learning, it helps uncover typical keywords or subtle text trends related to fraudulent activity.
- Deepfake and Synthetic Identity Detection: Deep learning can also help you accurately detect deepfakes and synthetic identities. Techniques like convolution neural networks (CNNs) and biometric liveness detection ensure that your tech is reading genuine biometric sources (human face, actual eye, thumbprints) as opposed to recreated images. Additionally, deep learning detection algorithms can help expose subtle signs of deepfake fraud. For visual media, the signs can be things like twitchy and unnatural motions, out-of-sync audio-to-video (even at a millisecond level) and variations in lighting and skin tone. For audio, it can note subtle voice pitch differences between real and fake files.
- Customer Interaction Automation: In situations where it is hard to distinguish between false positives and real fraud, quick customer confirmation could prove to be key. Rather than wait for your staff to reach out, automated bots can immediately notify and verify with your clients. Moreover, should the customer want to report a fraud, self-service, automated dispute handling systems can be deployed using a straight-through approach from complaint submission to processing. Such measures also help with customer satisfaction, as it showcases how swiftly and efficiently your organization responds to fraud.
Building a Long-Term, AI-Driven Fraud Management Strategy
For these AI Fraud Detection and Prevention strategies to keep successfully working in the long run, a cross-functional fraud management team in your organization must continuously evaluate and evolve all the AI models and workflows. If that is carried out, you will see transformative benefits stemming from this AI integration:
- Improved incident response through AI’s real-time detection and prevention
- Reduced costs from lower direct cost of fraud, less customer attrition and lower operational costs
- Modern AI scalability that allows you to analyze billions of transactions daily and adapt automatically to changes in traffic patterns
- Increased accuracy through identification of subtle patterns and anomalies, leading to less false positives
To bring all these transformative benefits into your organization, contact iValue today to discover our AI solutions for fraud prevention and risk management.
