Introduction
The infrastructure for real-time digital payments in India has grown to be essential to daily financial transactions. Near-frictionless transactions at previously uncommon scales have been made possible by platforms like UPI, fast card settlements, and embedded fintech payment rails. However, the same accessibility and quickness that drive financial inclusion have also increased fraud risk. Milliseconds of latency in transaction monitoring systems are being exploited by social engineering frauds, account takeovers, synthetic identities, and automated bot attacks. Financial institutions and fintech intermediaries are quickly using sophisticated fraud-detection technology, especially behavioural biometrics in conjunction with artificial intelligence (AI), to combat this danger. By examining how users interact with systems rather than the credentials they enter, these tools offer low-friction, real-time security. However, under India’s Digital Personal Data Protection Act, 2023 (DPDP Act), their implementation corresponds with an increased regulatory focus on personal data protection. Strengthening fraud controls without compromising individual privacy or violating legal requirements necessitates a careful balancing effort. This blog explores how behavioural biometrics and AI might work as a morally and legally sound fraud stack for real-time payments by using a privacy-by-design approach.
Fraud-Privacy Tension in Real Time Payments
Real-time payments are irreversible. Pre-transaction risk assessment is under tremendous pressure since recovery options are limited once a transaction is conducted. Static signs like IP addresses, device fingerprints, or blocked accounts are frequently used by traditional fraud systems. Although somewhat successful, these techniques are becoming more susceptible to spoofing and can produce significant false-positive rates, which can result in a bad user experience and costly exclusion. From the standpoint of data protection, these systems often gather excessive or persistent data that may no longer be acceptable in light of contemporary privacy regulations. Personal data must only be handled for legitimate purposes and to the extent required for such objectives, according to the DPDP Act. This implies that unrestrained surveillance cannot be justified by the detection of fraud. In order to prevent fraud protection techniques from turning into covert profiling tools, fintech’s must balance the legal requirement of proportionality with the practical necessity for detailed risk signals.
Behavioural Biometrics & AI
The transition from identity verification to behavioural consistency analysis is represented by behavioural biometrics. Systems can determine whether a transaction is consistent with a user’s established behavioural baseline by looking at patterns like typing cadence, swipe velocity, session navigation, and interaction duration. These signals allow for adaptive, real-time risk scoring without interfering with genuine transactions when combined with AI models trained on fraud typologies. When behavioural biometrics can be connected to an identifiable person, they are legally considered “personal data.” Their improper use can give rise to worries about behavioural profiling or secret monitoring, even when they are probabilistic or anonymous. Treating behavioural data as temporary risk indicators rather than permanent identifying features presents a compliance difficulty. According to a DPDP-aligned approach, these signals are contextual and purpose-limited, being used only to determine the validity of transactions and then deleted after that goal has been achieved.
AI Driven Fraud Systems
The DPDP Act allows processing of personal data for legitimate uses such financial security, fraud prevention, and legal compliance. This authorisation is subject to some conditions, nevertheless. Data fiduciaries must prove that this kind of processing is required, appropriate, and supported by the requisite safeguards. Increased control is necessary for automated decision-making systems that have a significant impact on users, such as transaction rejects or account limitations. Transparency is essential. Even if algorithmic details are kept private, privacy notices must explicitly state that automated and behavioural analytics are used to detect fraud. Purpose limitation is equally important. Without a specific legal foundation, behavioural data gathered for fraud protection cannot be used for marketing, credit evaluation, or behavioural profiling. Furthermore, in order to prevent AI systems from functioning as opaque “black boxes” outside of regulatory scrutiny, accountability procedures like grievance redressal and internal audits are crucial.
Privacy-by-Design Principles
- Signal-level data minimisation: Steer clear of raw or intrusive interaction records and only gather behavioural characteristics that clearly increase the accuracy of fraud detection.
- Models for ephemeral processing: Create systems that process behavioural data in real time and remove it after a risk assessment is produced.
- Logical data layer separation: Use tokenisation or pseudonymization to separate behavioural risk signals from core identification and transaction data.
- AI governance and documentation: Keep track of training datasets, model goals, bias evaluations, and recurring performance evaluations.
- Human supervision mechanisms: Make sure that outcomes with significant consequences are subject to manual review, appeal procedures, or explanations.
Proportionality of Fraud Monitoring
In the end, user trust is critical to the effectiveness of behavioural biometrics and AI in fraud detection. Intrusive yet covert security measures run the danger of alienating consumers, especially when decisions seem arbitrary or irreversible. A privacy-by-design fraud stack conveys restraint by indicating that security measures are legally sound, specifically customised, and considerate of personal autonomy. Such technologies show authorities that innovation and legal compliance may coexist. By lowering false positives, improving user experience, and conforming to international responsible-AI standards, they give fintech’s a competitive edge. Fraud prevention tactics will be evaluated more and more on their efficacy as well as their fairness and openness as India’s digital payments infrastructure grows.
AMLEGALS Remarks
AI in conjunction with behavioural biometrics is a potent advancement in real-time payment fraud detection, providing accuracy and flexibility that conventional systems find difficult to match. However, rigorous adherence to the fundamental tenets of the DPDP Act like, necessity, proportionality, openness, and accountability, is necessary for its legitimacy in India’s regulatory environment. Privacy-by-design is becoming a practical necessity for long-term fintech innovation rather than just a theoretical ideal. Financial companies can create fraud stacks that are both robust and respectful of human rights by considering behavioural data as fleeting risk signals, incorporating governance into AI models, and maintaining significant human control. By doing this, they reaffirm a crucial point, that in the digital economy, privacy is not a barrier to security but rather its cornerstone, and trust is just as vital as speed.
For any queries or feedback, feel free to connect with Hiteashi.desai@amlegals.com or Khilansha.mukhija@amlegals.com