Exploring How India’s Digital Payment Revolution Created a New Class of Fraud Victims: An Analysis of UPI Scams

Abstract

India’s Unified Payments Interface (UPI) has reshaped the country’s financial landscape, establishing India as the global leader in real-time digital payment adoption. However, this rapid expansion has been accompanied by a corresponding rise in digital payment fraud, raising concerns for consumer protection and the effectiveness of structural systems. Applying Routine Activity Theory, the paper analyzes literature to explain this outcome as the product of a convergence between motivated offenders, suitable targets, and absence of capable guardians. Recent studies show that victimization remains consistent despite awareness of digital payment frauds. Educated and technologically aware users continue to fall victim to complex psychological manipulation. Once targeted, victimization largely depends on the exploitation of cognitive biases. Institutional guardianship remains India’s most persistent weak point. Victims recover a fraction of the stolen funds, redressal mechanisms are largely unsatisfactory, and legal loopholes leave a majority of victims outside of the protection system. The paper concludes with recommendations to put a stop to legal loopholes, modernize legal frameworks, and add an additional layer of protection at the point of transaction rather than relying solely on user vigilance.

Introduction

India’s creation and adoption of the Unified Payment Interface (UPI) is an example of one of the most rapid financial digitisation initiatives in recent history, expanding from 20 million transactions in  FY 2016-17 to over 241.62 billion in FY 2025-26 (Ministry of Finance, 2026). This represents a 12,000-fold increase.  In 2012, over 87% of all transactions in India were conducted in cash, and fewer than 10% of the population had ever used any non-cash payment instrument (Iyer, 2017). Today, India accounts for 48.5% of worldwide real-time digital payment transactions, making it the global leader in digital payments (Singh and Agarwal, 2025). Furthermore, of the total retail digital payment transactions, UPI accounted for over 81% of them, emerging as the largest real-time retail payment system globally (Ministry of Finance, 2025). Between April 2016 (at launch) and March 2026, the number of banks live on UPI increased from 21 to 703. These statistics prove that UPI has evolved from merely being a means of payment to a financial infrastructure critical to India’s economy.

However, the same expansion that produced this success also generated a fraud crisis of proportionate scale. According to the Association of Certified Fraud Examiners, fraud is any activity that relies on deception in order to achieve a gain. Fraud becomes a crime when it is a knowing misrepresentation of the truth or concealment of a material fact to induce another to act to his or her detriment. Within the UPI ecosystem, fraud commonly involves social engineering and other deceptive practices that manipulate users into authorising transactions or disclosing sensitive credentials. The number of reported UPI fraud cases rose from 195,000 in 2020-21 to 7,25,000 in 2022-23, while the amount lost increased from ₹111 crore (approximately $15 million as per the exchange rates for FY 2020-21) to ₹573 crore (approximately $72.4 million as per the exchange rates for FY 2022-23) in the same period (Agrawal, 2026). Across all digital payment modes, fraud cases increased from 1,19,699 in 2020-21 to 14,57,000 in 2023-24, recording a compound annual growth rate (CAGR) of 130.03%, which outpaces the 40-105% annual growth in UPI transactions (Kumar and Dharshan, 2026). Iyer (2017) documented that card fraud in India increased in direct proportion to card adoption throughout 2011–2016, with losses doubling from ₹4 billion ($89.58 million as per the 2011 exchange rates) to ₹7.68 billion ($113.56 million as per the 2016 exchange rates) as card usage expanded. Moreover,  according to Wadkar et al. (2025), UPI fraud accounts for 47.25% of all cybercrime cases reported in India since 2020.Understanding why fraud has scaled at this rate requires examining not only the architecture of UPI itself, but the behavioral and structural conditions that enable exploitation. Statistics alone cannot explain why UPI fraud has scaled at this rate or why users remain vulnerable despite high awareness. This paper applies Routine Activity Theory (Cohen and Felson, 1979) to examine the structural and behavioral conditions that enable this crisis.

Theoretical Framework:

According to Routine Activity Theory (Cohen and Felson, 1979), a criminal event occurs when three elements converge in both time and space: a motivated offender who perceives an opportunity for gain, a suitable target, and the absence of a capable guardian who can prevent the crime. Additionally, Cohen and Felson argue that changes in routine activities, such as shifts in work patterns, leisure activities, and household arrangements, impact the convergence of motivated offenders, suitable targets, and the lack of capable guardians. This, in turn, affects crime rates. Scholars have since extended RAT beyond street crime to fraud contexts. Holtfreter et al. (2008) conducted a study that extended the Routine Activity Theory (RAT) to the context of consumer fraud victimization,  using a sample of 922 participants. Their study found that a one-unit increase in the buying routine index (a measure of how frequently respondents engaged in consumer activities such as online shopping) was associated with a 61% increase in the odds of being targeted for fraud.

Holtfreter et al. (2008) also tested Self-Control Theory alongside RAT, finding that while low self-control significantly increased the probability that a targeted individual would become a victim (302% per unit increase), it had no effect on whether the individual was targeted in the first place. This targeting/victimization distinction is directly relevant to the UPI fraud paradox examined later in this paper: why do 52.5% of users become victims despite 96.2% awareness? Once targeted, a user’s capacity for deliberate, cautious decision-making (rather than prior knowledge alone) determines victimization outcomes.

DeLiema (2018) reinforces this by applying RAT to study financial fraud against older adults, examining 53 cases of financial exploitation. The study investigated interpersonal financial exploitation amongst adults aged 65 years or older. The research found that social isolation, which means a lack of capable guardians like trusted friends, family, or oversight from institutions, was the main factor allowing these crimes to happen. The study also highlighted that perpetrators actively worked to create this isolation. They limited the victims’ social interactions to increase dependence before taking advantage of them.

Scale and Nature of the Problem:

Before examining specific fraud techniques, it is important to establish why the architecture of the Unified Payments Interface (UPI) itself creates a structurally suitable target for fraud. Understanding these design characteristics provides the foundation for explaining how offenders exploit the system. The simple and easy-to-use nature of UPI makes it an appealing option for online banking: the user’s phone number is linked to their bank accounts, debit cards, and credit cards through a platform, which allows for instant transactions at no cost. Singh and Agarwal (2025) state that while this attribute provides convenience, it also means that a single phone number becomes the single point of compromise that gives the attacker access to all of the user’s financial information simultaneously.

Sharma, Matharu and Sinha (2026) surveyed 183 UPI users in the Shimla district. As the study was confined to a single district and employed a relatively small sample, its findings have limited generalisability to the wider Indian population. The study found that although 96.2% of respondents reported being aware of UPI fraud, 52.5% had nevertheless become victims. The authors concluded that awareness alone may not translate into safe behaviour, highlighting behavioural factors such as overconfidence and susceptibility to social engineering. By contrast, Singh and Agarwal (2025) surveyed 280 respondents in Varanasi, excluded 30 inconsistent responses, and analysed 250 valid cases. Using the Friedman ranking test, the study identified awareness (mean rank = 6.72) and education (mean rank = 6.42) as the most effective countermeasures against UPI fraud. The two studies are not necessarily incompatible. Rather, when taken together, they suggest that awareness and education are seen as the best defences, but they do not always prevent fraud in practice. The difference in district, sample size, and research design may also contribute to the contrast.

In a comparative study of India, Sweden, and the United States of America, it was found that losses due to card fraud in India grew at an average annual rate of 12.19% between 2011 and 2016 (Iyer, 2017). Sweden, however, despite having the highest percentage of card transactions among the countries studied, demonstrated the lowest average annual fraud growth rate of 4.83%, which is nearly a third of the growth rate in India (Iyer, 2017). According to the study, India’s higher growth rate could be a result of card payment systems being introduced relatively recently, when compared to Sweden and the USA.

Furthermore, Sweden’s digital payment ecosystem functions within a complex regulatory framework managed by Finansinspektionen, the Swedish Financial Supervisory Authority. This agency sets security rules, checks compliance, and inspects payment platforms. Digital payment providers must follow the Payment Services Act, anti-money laundering laws, counter-terrorist financing regulations, the General Data Protection Regulation, and the Payment Card Industry Data Security Standard. They are also required to implement strong authentication, encryption, tokenization, and transaction monitoring systems as necessary security measures (Abraham et al., 2023).

According to Mukhopadhyay and Mukhopadhyay (2024), despite becoming victims, a large proportion of people were unwilling to leave UPI as a means of payment. Only 7.4% of fraud victims switched entirely to cash, 8.8% reduced their use of digital payments, and 51.5% reported no reduction in UPI usage. The Pearson correlation coefficient between fraud victimisation and reduced UPI usage is -0.408, a moderate negative relationship that confirms victims are largely unwilling to exit the platform. Holtfreter et al. (2008) demonstrated that a 1-unit increase in routine consumer activity, such as purchasing something online, translates into a 61% increase in the odds of being targeted for fraud. The data indicates that since victims themselves are substantially reluctant to use different means of payment, people will almost always be exposed to and within the environment in which motivated offenders operate.

It is difficult to determine the true scale of the problem due to issues regarding under-reporting. Mukhopadhyay and Mukhopadhyay (2024) found that 20.6% of respondents who had experienced UPI fraud did not report the incident to the concerned authorities. This finding is based on a survey of 70 valid respondents conducted over a five-day period (20–25 April 2023). Sirajutheen and Abirami (2026) found that, during the “Golden Hour” (the critical period immediately following a cyber fraud during which prompt reporting maximizes the likelihood of freezing fraudulent transactions and recovering funds) of a fraud incident, 40% of fraud victims would incorrectly call their bank’s IVR (Interactive Voice Response) helpline instead of the National Cyber Crime Helpline (1930). Meanwhile, post-fraud grievance redressal mechanisms prove to be largely ineffective, with 33% of people finding them unsatisfactory and only 6% describing the bank’s response as very satisfactory (Sirajutheen and Abirami, 2026). While under-reporting is an issue in and of itself, the low recovery rate of assets further highlights the shortcomings of guardianship. According to Sharma and Singh (2024), ₹2294 crore ($274.14 million as per the 2024 exchange rates) was lost in cyber fraud, but only ₹57 crore ($6.81 million as per the 2024 exchange rates) was successfully recovered, showing a recovery rate of 2.5%. The low recovery rate indicates that the existing institutional mechanisms for recovering funds after UPI fraud are objectively ineffective. Such limited recovery could reduce the effectiveness of formal reporting systems and suggests that current guardianship mechanisms provide insufficient protection to victims.

How Fraudsters Operate and Why People Become Victims

One of the most prominent modalities of UPI fraud is social engineering (the psychological manipulation of the target’s behavior). Sharma and Singh (2024) present social engineering attacks in which fraudsters impersonate trusted authorities and create a sense of urgency regarding issues such as KYC compliance, blocked accounts, or failed transactions. Similarly, Wadkar et al. (2025) studied two UPI fraud cases involving educated and technologically aware victims who were deceived by messages warning of imminent electricity disconnection and directing them to malicious links. These studies demonstrate that digital payment fraud takes advantage of psychological pressure and trust rather than being a result of mere technological ignorance.

Other prevalent techniques include phishing and vishing (voice phishing), fake QR code scams, fraudulent payment request schemes in which fraudsters send payment requests disguised as payment receipts, and SIM swap attacks that redirect incoming OTPs to the fraudster’s device (Sharma and Singh, 2024; Singh and Agarwal, 2025). Sirajutheen and Abirami (2026) report that while awareness of phishing (67%) and vishing (70%) is relatively high among UPI users, awareness of more complex schemes (such as mule account operations, in which victims unknowingly launder money by renting their bank accounts to fraudsters) is relatively low, with only 6% of respondents being familiar with the term.

Singh and Agarwal (2025) point out that the platform connects a debit card, credit card, and bank account to one phone number. This means that one successful manipulation gives the offender access to several financial tools at once. Sharma and Singh (2024) also mention that digitization has not only widened the market for fraud but has also made criminals more skilled. Offenders keep adapting their techniques to keep up with the changes in the platforms they target.

Survey data from Sharma, Matharu, and Sinha (2026), drawn from 183 UPI users in Shimla district, indicates that 68.3% of fraud victims are graduates or postgraduates, challenging the assumption that higher educational attainment inherently protects individuals from digital financial fraud. As established above, 96.2% of the same respondents reported being aware of UPI fraud, yet 52.5% had nevertheless experienced victimization (Sharma et al., 2026). Singh and Agarwal (2025), meanwhile, identified fraud awareness as the highest-ranked preventive measure among surveyed stakeholders, assigning it a mean rank of 6.72. Taken together, these findings expose a structural gap: awareness and education are the most widely recommended individual-level defences against digital payment fraud, yet their presence has failed to prevent victimization at scale. Sirajutheen and Abirami (2026) conducted a correlation analysis that revealed a strong positive relationship between the frequency of digital usage and victimization (r = .825). This finding aligns with the Routine Activity Theory, which suggests that increased routine exposure creates greater opportunities for motivated offenders. Holtfreter et al. (2008), in a study of 922 adults, established a crucial distinction between the determinants of fraud targeting and the determinants of fraud victimization. Their logistic regression models demonstrated that routine consumer activity predicted whether individuals were selected as targets, producing a 61% increase in targeting odds per unit increase in activity. Individual traits, however, did not affect targeting at all.

Holtfreter et al. (2008) established that while RAT explains targeting, individual factors determine whether a targeted person becomes a victim. In the UPI context, fraudsters systematically exploit cognitive biases.

Several cognitive and social biases contribute to vulnerability to fraud. Overconfidence bias leads users to underestimate fraud risk and disregard security protocols and updates. FOMO (Fear of Missing Out) is exploited through fabricated urgency, such as claims of limited offers or imminent account suspension, to push users toward hasty decisions. Reciprocity bias, along with sympathy exploitation, is described as common in romance and distress scams. Confirmation bias, anchoring, authority bias, and groupthink are identified as social influences that interact with these factors to further increase susceptibility to manipulation and fraud (Sharma, Matharu, & Sinha, 2026).[1]  This reveals that exploiting the architecture of human decision-making has a vital role in achieving UPI fraud.

The comparison with DeLiema’s study is instructive here. In elder fraud, cognitive vulnerability arose from neurological decline, which is a form of diminished capacity that reduced the victim’s ability to evaluate threats accurately (DeLiema, 2018). In UPI fraud, cognitive vulnerability is manufactured when fraudsters design scenarios that activate the biases listed above, effectively inducing a temporary incapacity in otherwise competent individuals. The complex nature of the attack is what converts the targeted individual into a victim, regardless of education and awareness.

Victims and non-victims attribute fraud causation differently. Victims emphasize financial illiteracy and technological complexity, while non-victims emphasize cybersecurity vulnerabilities and slow investigation. This divergence suggests that victimization is associated with a shift in how individuals understand the threat (Singh and Agarwal, 2025).

Systematic Failure

The institutional response to UPI fraud victimization has largely been a refusal to accept responsibility. Wadkar et al. (2025) document that when two fraud victims reached out to a pseudonymous Bank ABC, the bank maintained that the transactions were authorized because the victims had shared their UPI PIN or one-time password (OTP). This was the case regardless of whether the victims did so under deceptive circumstances, leading the bank to conclude that the transactions were non-refundable. The RBI’s own framework reinforces this position, specifying that if a transaction occurs as a result of the customer sharing their credentials, the loss rests with the customer until the bank is formally notified (Wadkar et al., 2025).

Sharma and Singh (2024) state that social engineering attacks trick customers into disclosing information by impersonating legitimate authorities and manufacturing crises. However, the victim does not share credentials negligently. Rather, they are deceived into doing so. Yet the legal framework treats engineered deception and careless disclosure as equivalent, and holds the victim equally liable in both cases.

One of the most prominent legal gaps in India’s UPI consumer protection framework is the APP (Authorized Push Payment) loophole. According to Agrawal (2026), as per RBI[1] ’s 2017 Consumer Protection Circular, banks are obligated to cover losses from unauthorized transactions if reported within three days. APP fraud, however, operates by deceiving the victim into authorizing the transfer themselves, so the transaction is technically classified as authorized and falls entirely outside the protection framework.

The failure to provide an appropriate institutional response is evident in the recovery statistics. In 2022, the recovery rate was determined to be 2.5% (Sharma and Singh, 2024). This statistic highlights a significant failure of systemic guardianship. Furthermore, it was revealed that only 6% of respondents rated their bank’s grievance redressal process as “very satisfactory,” while 33% found it “unsatisfactory.” These perceptions align with the recovery outcomes, indicating that the current system is not effectively providing reliable protection  Sirajutheen and Abirami, 2026, p.301).

The 20.6% non-reporting rate among UPI fraud victims is not clear evidence of a lack of action. The authors suggest two possible explanations for non-reporting: that the grievance redressal mechanism may be difficult for victims to access, or that victims may lack sufficient awareness of how and where to report UPI fraud (Mukhopadhyay and Mukhopadhyay, 2024).

Furthermore, in elder fraud cases, capable guardians (financial institutions, social services, family members) consistently failed to identify victimization until losses had already occurred, at which point intervention was too late (DeLiema, 2018). When we look at UPI, the transactions are much more rapid, and the window for intervention is narrower. Therefore, the convenience of quick transactions also brings forth the issue of timely prevention by guardians.

Recommendations

The most significant reform that Indian policymakers can make is to close the APP liability loophole. ​​The United Kingdom introduced a mandatory reimbursement of up to £85,000 (approximately $113,000 as per the 2026 exchange rates) for APP fraud, requiring payment service providers to compensate eligible victims and strengthening incentives for fraud prevention (Agrarwal, 2026). A comparable reform in the Indian context would require banks and payment platforms to compensate victims of social engineering fraud, removing the current presumption that authorized transfer equals voluntary consent.

Legal frameworks governing digital payments must evolve continuously to match the pace of technological change (Ballaji, 2024). Amending the Information Technology Act (2000) and the Payment Settlement Systems Act (2007) is recommended to address evolving forms of digital payment fraud, support a fraud management framework, and implement AI for fraud detection (Banerjee et. al, 2025).

It has been established that a majority of users aware of digital payment fraud have become victims (Sharma, Matharu, and Sinha, 2026). Therefore, policymakers should design interventions that reduce the probability of a person falling victim to fraud at the moment of the transaction. An example of such could be a form of QR authentication before the approval of a transaction.

Furthermore, since 40% of users were unaware of the National Cybercrime Helpline (1930) during a fraud incident (Sirajutheen and Abirami, 2026), a simple display of the helpline in any UPI platform (such as Google Pay) would allow for timely action and early intervention.

Additionally, while many studies support using AI in fraud detection, there is little guidance on how to implement these systems effectively. Future research should focus on building and testing practical AI-based fraud detection frameworks that can be integrated into digital payment systems.

Several methodological limitations in the current literature also need to be addressed. Future research should use nationally representative samples rather than regional ones, as this would allow the findings to be generalized across India. Furthermore, future work should test which specific cognitive biases result in relatively greater victimization, so that prevention efforts can be prioritized. Finally, comparative research across a wider set of countries would help identify which regulatory and infrastructural conditions most effectively curb fraud growth.

Conclusion

This paper explains why India’s digital payment revolution driven by UPI has produced a growing class of fraud victims. The observations suggest that UPI fraud stems from structural factors that create opportunities for fraud, rather than solely from individual negligence. UPI’s architecture, in which a single phone number links a user’s bank accounts, debit cards, and credit cards, creates a single point of vulnerability, while its convenience ensures routine digital activity that places users in the realm of motivated offenders.

Routine activity governs who is targeted, but awareness and education alone do not determine who becomes a victim. Victimization depends on whether fraudsters succeed in manufacturing the cognitive conditions, such as urgency, authority, and overconfidence, that override otherwise informed judgment.

One of the most critical issues is the absence of effective guardianship. Recovery rates for stolen funds remain minimal, and redressal mechanisms are widely regarded as unsatisfactory. Additionally, the Authorized Push Payment loophole allows banks to treat engineered deception and careless disclosure as equivalent, leaving a large category of victims without reimbursement.

Therefore, closing the gap between UPI’s convenience and its vulnerability will require structural changes, not merely individual reforms. Tackling the APP loophole, updating the legal frameworks governing digital payments, addressing methodological limitations, and introducing safeguards at the point of transaction are of utmost importance. As UPI continues to expand as the backbone of India’s digital economy, the strength of its guardianship mechanisms is likely to determine whether this expansion will occur without a proportionate rise in victimization.

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