The rapid expansion of digital financial services is being accompanied by a growing fraud threat for businesses and financial institutions across India. According to a 2025 joint study by Experian and Forrester Consulting, around 62% of businesses in India reported an increase in overall fraud attacks, while 66% of organisations said their financial losses from fraud had risen year-on-year. The study surveyed 109 senior fraud decision-makers in India.
Which Digital Fraud Threat Is Growing the Fastest?
Account takeover has emerged as the most widespread and rapidly increasing fraud threat, according to the study, with around 77% of respondents reporting an increase in such incidents. Money muling and identity theft followed closely, with 71% of respondents reporting a rise in each category.
Synthetic business fraud and first-party fraud also recorded significant increases, with 39% of respondents reporting growth in both categories. Synthetic identity fraud, in particular, involves combining genuine and fabricated personal information to create identities that can potentially be used to obtain financial services, credit, or other benefits, a genuinely tricky form of fraud to catch since it blends real and fake data together.
The study found that the challenge isn't limited to the growing volume of fraud alone. Detecting and preventing increasingly sophisticated attacks is also becoming considerably more difficult. Authorised Push Payment (APP) fraud was identified by respondents as the most challenging type of fraud to detect and prevent, followed by identity theft and money mule activity.
Why Are Fraud Attacks Becoming Harder to Detect?
First-party fraud, synthetic identities, and deepfakes are adding further complexity to an already challenging fraud landscape. Criminals can use fabricated digital identities and artificially generated content to create the appearance of legitimate customers or businesses, making it increasingly difficult for conventional, rules-based fraud detection systems to distinguish genuine activity from malicious behaviour.
The report identified limitations in data, technology, system agility, operational processes, and model performance as factors making fraud prevention more difficult overall. Traditional systems that depend heavily on predefined rules and established patterns can struggle significantly when fraudsters rapidly change their methods, staying one step ahead of static defences.
The study emphasised the need for financial institutions and other organisations to shift toward more adaptive fraud prevention strategies. This is exactly the kind of situation where sound business advisory support becomes genuinely valuable, helping organisations move beyond static, rules-based controls toward systems capable of analysing data, behavioural signals, and relationships between accounts and transactions, ultimately improving risk assessment during customer onboarding and helping identify suspicious activity earlier.
Which Lending Products and States Show Higher Fraud Risk?
Analysis of application anomalies across lending products showed that fraud risk varies significantly by product type. Credit card applications consistently recorded the highest anomaly rates, though these rates stabilised after an initial decline. Business loans showed a gradual downward trend overall, with a temporary increase around the first quarter of financial year 2026.
Auto loans showed steady improvement, with anomaly rates declining across most quarters, while personal loans remained comparatively stable with only minor fluctuations. Two-wheeler loans consistently recorded the lowest anomaly rates among the lending products covered in the analysis.
The study also identified significant geographical differences in these application anomalies. Delhi, Haryana, Rajasthan, Uttar Pradesh, and West Bengal recorded catch rates above 10%, while Kerala, Tamil Nadu, and Karnataka reported comparatively lower rates below 8%. Application anomalies and mule activity have emerged as major concerns because criminals can exploit weaknesses in identity verification and customer onboarding processes, once fraudulent or compromised accounts are established, they can be used as part of networks designed to receive, move, and withdraw proceeds from financial crimes.
How Are Deepfakes and AI Changing Fraud Prevention?
Cybersecurity experts said financial institutions can no longer rely on identifying individual suspicious transactions in isolation. Organisations increasingly need to analyse customer identities, devices, transaction behaviour, account activity, and links between connected accounts as part of a broader, more integrated risk assessment framework.
The growing use of deepfakes and AI-generated content makes this shift even more urgent. Fraudsters can increasingly create convincing identities, documents, and communications, making conventional verification methods considerably less reliable than they used to be. Financial institutions therefore need stronger identity controls, behavioural analytics, and network-based monitoring to identify suspicious activity before significant losses actually occur.
These findings indicate that fraud prevention is genuinely undergoing a fundamental transformation. As digital financial activity continues expanding, data-driven and adaptive detection systems are becoming an increasingly important layer of protection for institutions seeking to limit financial losses and maintain customer trust in an environment where fraud tactics evolve faster than traditional defences can keep up.
FAQs
Q1. What percentage of Indian businesses reported an increase in fraud attacks?
According to the Experian and Forrester Consulting study, around 62% of businesses in India reported an increase in overall fraud attacks, while 66% said their financial losses from fraud had risen year-on-year.
Q2. Which type of fraud is growing the fastest in India?
Account takeover emerged as the fastest-growing fraud threat, with 77% of respondents reporting an increase, followed by money muling and identity theft at 71% each.
Q3. Which states showed the highest fraud application anomaly rates?
Delhi, Haryana, Rajasthan, Uttar Pradesh, and West Bengal recorded catch rates above 10%, while Kerala, Tamil Nadu, and Karnataka reported lower rates below 8%.