Written by

Sumeshwar Pandey

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12 min read
India DPDP Act BFSI fraud and risk

Fraud Detection vs Privacy: When Can You Rely on Legitimate Use?

A practical decision frame for Indian BFSI leaders balancing DPDP Act obligations, RBI KYC/AML expectations, and data-driven fraud detection.
Key takeaways
  • Under India’s DPDP Act, you only have two lawful grounds to process personal data: consent or specific “legitimate uses”; fraud work must be mapped explicitly to one of these, plus any applicable Section 17 exemptions.
  • Core fraud, KYC, and AML controls often fit within legal‑obligation and offence‑prevention carve‑outs, but adjacent uses like behavioural profiling for marketing typically require consent and clear notices.
  • Relying on legitimate use is a strategic bet that must be backed by necessity, proportionality, data minimisation, and strong documentation that an RBI inspector or Data Protection Board inquiry can follow.
  • A consent management layer that cleanly separates consent-based processing from legitimate-use fraud workflows makes your legal position more defensible and keeps day‑to‑day operations enforceable.
  • The cost of misjudging the fraud–privacy boundary includes DPDP penalties, RBI scrutiny, system rework, and erosion of customer trust; a 12‑month roadmap can contain this risk while improving operating leverage.

The fraud–privacy dilemma facing Indian finance leaders

Imagine your payments business has just taken a spike in card‑not‑present fraud. Risk wants to roll out aggressive device fingerprinting, behavioural biometrics, and network‑graph analytics before the next festive season. Product is worried about false declines. Legal is asking whether any of this can run without fresh consent once the Digital Personal Data Protection (DPDP) Act and DPDP Rules 2025 bite. Meanwhile, RBI’s KYC and AML directions already expect continuous monitoring and timely suspicious transaction reporting. The tension is not theoretical; it lands directly in your fraud budgets, your audit committee papers, and your customer complaints queue.
For a CXO in an Indian bank, NBFC, or fintech, the real question is no longer “Is privacy important?” It is: for each fraud‑related use of customer data, should you rely on DPDP’s legitimate uses and Section 17 exemptions, or design explicit consent and controls? That choice affects regulatory exposure, time to deploy new analytics, how many journeys your product team must redesign, and ultimately how much operating leverage you can get from your fraud stack.
Market timing raises the stakes. The DPDP Rules 2025 introduce phased implementation from November 2025 through May 2027, bringing obligations for consent, security, and data rights into force over a defined schedule.[5]
This is best treated as a capital‑allocation and risk decision. The task is to understand how DPDP’s lawful grounds actually work, how they intersect with RBI expectations, and how to structure your fraud programme so that each data use has a clear legal basis, a defined business value, and an audit trail your own board can defend.

How DPDP’s legal grounds and exemptions apply to fraud detection

The DPDP Act makes life simple at a high level and demanding in execution. Personal data can be processed only for a lawful purpose and only on two grounds: consent, or certain specified legitimate uses. India has not adopted a broad “legitimate interest” concept; instead, the Act lists finite categories where processing without consent is permitted, such as compliance with law, performance of state functions, handling medical emergencies, or certain employment‑related safeguards.[1]
For fraud detection and financial crime, a few provisions matter more than others. First, processing that is necessary to comply with a law, judgment, or order can qualify as a legitimate use. RBI’s KYC and AML directions, and obligations under the Prevention of Money Laundering Act, create positive duties to conduct due diligence, monitor accounts on a risk basis, and report suspicious transactions.[3]
Second, DPDP’s exemptions, particularly the carve‑out for prevention, detection, investigation, or prosecution of offences or contraventions of law, are directly relevant to fraud. Where that exemption applies, several of the usual DPDP obligations can be relaxed for that processing. This is what allows your internal investigations team to reconstruct flows through mule accounts or suspected collusion between staff and outsiders without first asking the suspected parties for consent. However, exemptions are not a blanket amnesty: they are purpose‑bound, and other duties such as security safeguards and data minimisation continue to apply.[4]
Bringing these strands together, a practical working position for many BFSI players has three planks. One, core KYC, AML, and mandated transaction monitoring can often be anchored in legal obligation as a legitimate use. Two, specific fraud investigations or offence‑related analytics can, in defined circumstances, rely on the exemption for prevention and detection of offences. Three, anything that extends beyond those anchors into marketing, product experimentation, or broader profiling generally needs consent or, at minimum, very clear notice and opt‑outs. The strength of your position will depend on how tightly you can connect each data use to a statutory duty or offence‑prevention need.

Deciding when to rely on legitimate use versus consent for fraud-related data

The hardest executive calls sit in the grey areas: device fingerprinting that doubles as marketing segmentation, behavioural scores reused for credit cross‑sell, or long‑term retention of transaction data to train new AI models. To avoid case‑by‑case firefighting, it helps to classify your fraud and risk uses of data into three buckets and think through the trade‑offs across regulatory defensibility, implementation cost, and customer friction.
The first bucket is clearly mandated controls. This covers customer due diligence, risk‑based ongoing monitoring, screening against sanctions or negative lists, suspicious transaction monitoring, fraud alerting to customers, and regulatory reporting such as STRs and CTRs. These activities map closely to RBI directions and PMLA requirements. For them, relying on legitimate use tied to legal obligation, combined with offence‑related exemptions where applicable, is usually defensible. Requiring separate consent pop‑ups for each rule change would create unacceptable risk of under‑monitoring and could itself raise supervisory concerns.
The second bucket is investigative and internal‑control use cases. Examples include deep‑dive reviews of chargebacks, forensic analysis of suspected mule networks, escalations from whistle‑blower complaints, or analytics to detect unusual employee access to high‑risk systems. Here, the offence‑prevention exemption and, in some cases, employment‑related legitimate uses can apply, but necessity and proportionality become sharper tests. Pulling three months of account data on an implicated merchant may be proportionate; hoovering up years of unrelated customer interactions because one transaction is in question is harder to justify. For these use cases, you usually will not seek end‑customer consent, but you should expect to defend why the data used and the retention period were no more than necessary.
The third bucket consists of adjacent analytics that are not strictly required for fraud or legal compliance but are commercially attractive. Reusing behaviour scores from your fraud engine to fine‑tune loan offers, combining geolocation and device data to personalise marketing, or selling anonymised risk insights to partners all fall in this category. Treating these as “fraud” and relying on exemptions is risky. Here, explicit consent and clear notices are strategically wiser, even if you might argue for a borderline legitimate use. The additional implementation cost of consent flows can be justified as an investment in regulatory resilience and customer trust, especially if those flows are designed to be low‑friction.
Across these buckets, you can think of two archetypal strategies. A “legitimate‑use‑heavy” strategy minimises explicit consent in fraud stacks, keeping customer experience clean but raising the bar on documentation, legal analysis, and DPIA‑style assessments for each model. A “consent‑first adjunct analytics” strategy reserves legitimate use and exemptions for truly mandated controls, while anything that smells like commercial optimisation is gated behind purpose‑specific consent. Many BFSI leaders are landing somewhere in the middle: core fraud and AML under legitimate use and exemptions; all marketing, cross‑sell, and partner sharing under consent; and a small set of ambiguous analytics subjected to tighter governance and periodic legal review.
Strategic trade‑offs between relying on legitimate use and obtaining consent across fraud‑related data uses.
Fraud data use bucket Typical examples Primary lawful ground Consent posture Regulatory and CX risk if mis‑handled
Mandated controls Customer due diligence, sanctions and negative‑list screening, risk‑based transaction monitoring, fraud alerts, STR/CTR and other regulatory reporting. Legitimate use grounded in legal obligation, sometimes supported by offence‑prevention exemptions for specific scenarios. Do not seek per‑transaction consent; explain monitoring and reporting clearly in account‑opening journeys and privacy notices. If treated as consent‑dependent and switched off when consent is absent, you risk under‑compliance with KYC/AML expectations and higher fraud losses.
Investigative and internal‑control use cases Chargeback reviews, mule‑account network analysis, whistle‑blower‑triggered investigations, analytics on unusual employee access to high‑risk systems. Offence‑prevention exemptions, supplemented by employment‑related legitimate uses where staff behaviour is in scope. End‑customer consent is usually not sought, but each investigation should be tightly scoped, time‑bound, and logged. Over‑broad data pulls and indefinite retention can look like surveillance rather than targeted investigation, triggering regulatory concern and employee or customer pushback.
Adjacent commercial analytics Reusing fraud scores for cross‑sell targeting, combining device and location data for offers, selling aggregated risk insights to partners. Primarily consent; legitimate use arguments are weak unless the analysis is tightly tethered to fraud or compliance outcomes. Design purpose‑specific consent and easy opt‑outs; keep these activities off exemptions‑based architectures. Treating these analytics as “fraud” can be seen as back‑door profiling, increasing DPDP penalty risk and eroding customer trust if exposed.

Designing controls, documentation, and audit trails around fraud analytics

Whichever strategy you lean toward, regulators will judge you more by your evidence and controls than by your slideware. Relying on legitimate use or exemptions for fraud without a structured governance spine is a gamble. The starting point is a data‑use inventory that maps every fraud‑related system and model to its purposes, inputs, outputs, legal ground, and retention period. This should include classic rule‑based engines, machine‑learning models, manual review queues, and any shadow analytics sitting in risk or product teams’ notebooks.
From there, necessity and data minimisation need to be more than slogans. Fraud models should be designed to use the smallest set of features that achieves an acceptable level of protection, and retention should align to demonstrable needs and overlapping obligations. For example, if RBI‑ or PMLA‑related rules require a minimum retention period for transaction records, storing them for that period is defensible, but using the same dataset indefinitely to train unrelated product models is not. Where you rely on exemptions for investigations, scoping each project with specific timelines and data domains helps show that the exception is not becoming the rule.
Documentation is your second line of defence. Decision notes from a data protection or risk committee that explicitly weigh DPDP grounds against business need, DPIA‑style assessments for high‑impact models, and clear policies on when offence‑related exemptions may be invoked all contribute to a defensible narrative. Model inventories should record who approved each model, which data sets it touches, how often it is retrained, and how false positives or discriminatory impacts are monitored. These artefacts are what an internal auditor, RBI inspection team, or Data Protection Board inquiry will expect to see.
Finally, technical controls and audit trails make your governance real. Tamper‑evident logs of who accessed which data for which investigation, role‑based access control that limits sensitive fraud tools to trained staff, and segregation of fraud analytics environments from marketing and product experimentation environments help enforce purpose boundaries. When a customer raises a grievance about an allegedly unfair decline or intrusive profiling, the ability to reconstruct exactly which systems were involved, on what legal basis, and under whose authority can be the difference between a manageable complaint and a wider investigation.

Handling common implementation issues at the fraud–privacy boundary

Once you start implementing this split between legitimate‑use fraud processing and consent‑based analytics, a few recurring operational issues tend to surface. Addressing them early avoids last‑minute legal debates and disruptive re‑work.
Focus your troubleshooting on these patterns:
  • Event streams from apps and web properties feed both fraud and marketing tools without clear tagging, so “fraud” models quietly reuse marketing data. Fix this by segmenting pipelines, adding purpose tags to events, and enforcing separate access controls for risk and growth teams.
  • Legacy data lakes mix KYC, transaction, and behavioural data without purpose labels. Fix this by freezing new uses until each dataset is mapped to a lawful ground, then restricting access for non‑fraud use unless consent is in place.
  • Third‑party fraud or analytics vendors deploy generic tags or SDKs that collect more data than your documented purposes cover. Fix this by reviewing vendor scopes with legal, turning off unused data fields, and binding permitted purposes into contracts and technical configuration.
  • Model owners treat DPDP sign‑offs as a one‑time hurdle at go‑live, so subsequent tuning or feature additions are never re‑evaluated. Fix this by requiring material model changes to pass back through your risk or data‑protection committee, with updated documentation.
  • Customer‑facing teams cannot explain to an unhappy customer why a specific decline or review happened. Fix this by building simple internal explanations linked to each decision type, grounded in the underlying legal basis and the main factors your models consider.

The cost of misjudging the boundary between fraud and privacy

Misreading where fraud ends and commercial analytics begins is not just a compliance problem; it is an expensive operational mistake. At the higher end of the DPDP penalty schedule, serious violations can attract fines of up to ₹250 crore for certain categories of non‑compliance, particularly where they involve failure to prevent a personal‑data breach or wilful disregard of data‑principal rights.[2]
On the other side, over‑correcting by putting every fraud activity behind consent can undermine your ability to meet RBI and PMLA expectations. If risk teams feel constrained from tuning rules or deploying new analytics because consent flows are not ready, you may see higher fraud losses, more write‑offs, and potentially questions from supervisors about the adequacy of your monitoring. In a worst‑case scenario, an institution could face parallel pressure: one regulator investigating privacy overreach, another probing weak AML controls.
The hidden cost lies in rework and lost executive time. If a Data Protection Board inquiry concludes that your fraud engine was also quietly powering personalised pricing without proper consent, you may be forced to decouple systems on an accelerated timeline, retrain models with a smaller dataset, and send corrective communications to affected customers. That means refocusing senior product, technology, legal, and risk leaders for months. Taking the time now to draw a clean line between legitimate‑use fraud processing and consent‑based analytics is therefore less about restraint and more about avoiding a disruptive clean‑up later.

A 12‑month action checklist for CXOs

Over the next year, most Indian BFSI organisations will need to move from broad DPDP awareness to specific, defensible positions on fraud and privacy. This sequence gives your teams a clear run‑order.
  1. Put governance and ownership in place
    A pragmatic sequence starts with governance. Putting fraud analytics on the agenda of your board‑level risk or technology committee, designating an executive owner, and agreeing that every material data use must be anchored either in consent or a mapped legitimate use creates the mandate your teams need. Cross‑functional workshops between risk, product, technology, and legal can then catalogue current fraud and AML use cases, classify them into the three buckets discussed earlier, and identify quick wins where risky data reuse can be halted without hurting fraud performance.
  2. Clean up architecture and data hygiene
    The next focus is architecture and data hygiene. Mapping end‑to‑end data flows into and out of fraud systems, aligning retention policies with both RBI and DPDP expectations, and pruning unnecessary feeds reduces your attack surface and simplifies legal analysis. In parallel, you can evaluate your consent and preference management capabilities: whether you build, buy, or extend an existing platform, the aim is a single source of truth for consent that downstream systems, including fraud tools and CRMs, are technically required to check.
  3. Elevate model governance and response readiness
    Finally, you need to harden model governance and readiness for scrutiny. Creating and maintaining a central inventory of fraud and risk models, subjecting high‑impact ones to DPIA‑style assessments, and implementing consistent logging and access controls will put you in a stronger position if an incident or inquiry arises. Playbooks for responding to DPDP grievances, Data Protection Board notices, or RBI inspection queries about your fraud systems can be rehearsed in tabletop exercises. By the time the DPDP Rules 2025 are fully in effect, you want to be able to answer a simple question with confidence: for this particular fraud decision on this customer, do we know what data we used, on what legal basis, under whose authority, and for how long it will be stored?

Using consent management to separate fraud analytics from everything else

The more sophisticated your fraud stack becomes, the harder it is to keep consent‑based processing and legitimate‑use processing from bleeding into each other. Web and app teams want to push data into a common customer data platform. Fraud vendors pitch multi‑purpose tags that can drive both risk scores and marketing segmentation. Without a deliberate architecture, you end up either over‑collecting data under an elastic notion of “fraud”, or paralysing your teams with case‑by‑case legal approvals.
A dedicated consent management layer, such as Digital Anumarti - Service, is one way to operationalise that boundary. In other regulated sectors, Digital Anumarti - Service has been deployed as an API‑driven consent ledger that sits between front‑end channels and back‑end systems, records granular purpose‑linked consent, issues verifiable consent receipts, and supports special flows for statutory exemptions where access is allowed but tightly logged. The benefit is not that a platform automatically makes you compliant, but that it turns your legal positions into enforceable configuration that product, risk, and data teams can rely on. If you are reviewing your DPDP roadmap, it is worth evaluating whether a consent management platform like Digital Anumarti - Service should sit at the heart of your customer‑data architecture; a focused discovery project or proof‑of‑concept can surface the practical fit.[6]

How Digital Anumarti - Service supports DPDP-aligned fraud programmes

Digital Anumarti - Service

1

API‑driven consent ledger in production deployments

In healthcare implementations, Digital Anumarti - Service has been integrated via APIs as a central consent ledger that connects digital consent capture at the front desk with downstream clinical systems, replacing paper forms and making every consent artefact traceable in one place.

Why it matters for you

The same pattern can be applied to core banking or wallet platforms so that fraud engines, CRMs, and data lakes all reference a single, verifiable view of consent rather than scattered UI flags or spreadsheets.

2

Hashed consent receipts for downstream proof

Diagnostic‑lab deployments of Digital Anumarti - Service generate secure, hashed consent receipts that travel with pathology reports so downstream physicians and partners can verify that data was processed on a lawful basis.

Why it matters for you

For BFSI, attaching comparable consent receipts to statements, reports, or partner data feeds can simplify audits and disputes by showing exactly what a customer authorised at the point of data collection.

3

Exemption‑aware bypass flows with full logging

Hospital deployments have configured bypass flows aligned to DPDP medical‑emergency exemptions so that clinicians can access necessary data during life‑saving procedures while every access event is logged for later review.

Why it matters for you

Fraud and investigations teams can adopt a similar pattern for offence‑prevention scenarios, ensuring urgent reviews are not blocked by consent walls yet remain fully auditable for regulators and internal audit.

4

Consent‑linked data mapping for incident response

In high‑volume clinic environments, Digital Anumarti - Service has been used to map data flows to a consent ledger so anomalous activity can be tied to specific consented cohorts within tight breach‑notification timelines.

Why it matters for you

Linking fraud and AML systems to an accurate consent ledger makes it easier to scope DPDP breach notifications, isolate affected customers, and show that non‑impacted transactions were not exposed.

5

Single source of truth for consent across departments

Large hospital deployments use Digital Anumarti - Service as a real‑time, single source of truth on patient consent that all departments can access, including during emergency admissions when rapid decisions are required.

Why it matters for you

Financial institutions with siloed product lines and channels can apply the same approach so that front‑line teams, fraud operations, and analytics squads all refer to one authoritative record of what each customer has agreed to.

Evidence Digital Anumarti - Brand

Common questions on fraud detection and DPDP compliance

Senior leaders and boards tend to circle around a few recurring questions as they digest DPDP’s impact on fraud systems. Clarifying these upfront can help you set expectations internally and avoid both undue optimism and unnecessary paralysis. The perspectives below reflect current readings of the law and regulatory practice, but your institution should align them with its own counsel’s advice and evolving guidance from MeitY, the Data Protection Board, and RBI.
FAQs

No. While DPDP’s legitimate uses and the offence‑prevention exemption are clearly relevant to fraud and AML, they are not a blanket shield. They apply where processing is genuinely necessary to comply with law or to prevent, detect, investigate, or prosecute offences or contraventions. Routine KYC and AML controls, transaction monitoring, and specific internal investigations usually fit within that frame. However, reusing fraud data for cross‑sell, behavioural marketing, personalised pricing, or partner insights is unlikely to qualify. Treating everything that touches a fraud system as exempt will look like back‑door profiling if scrutinised. A more defensible stance is to anchor mandated fraud and AML work in legitimate uses and exemptions, and to require consent or clear opt‑outs for adjacent commercial analytics.

In most cases, you can ground standard transaction monitoring and suspicious transaction reporting in your legal obligations rather than in fresh consent. RBI’s KYC and AML directions, together with PMLA requirements, oblige regulated entities to conduct ongoing due diligence and to report suspicious activity. Using customer transaction data to discharge those duties is therefore a strong candidate for legitimate use linked to compliance with law, and, where applicable, for the offence‑prevention exemption. That does not remove all DPDP duties: you still need appropriate security safeguards, data minimisation, reasonable retention periods, and clear internal policies on how monitoring is conducted. What it does mean is that you do not need to interrupt every payment journey with a consent prompt in order to run risk checks that regulators themselves expect you to perform.

AI does not get a special legal category under DPDP; it is judged by the same principles as any other processing. For fraud models, necessity and proportionality translate into a few practical questions. Are the inputs limited to data that is reasonably connected to the fraud risks you are addressing, or are you pulling in broad behavioural, social, or third‑party data because it is available? Are you retaining raw training data for longer than is needed to meet both fraud‑management and regulatory‑retention requirements? Have you considered the impact of false positives on particular customer groups, especially where declines or additional verification steps may look discriminatory? Documenting these considerations in model design notes and periodic reviews both improves model quality and strengthens your ability to justify reliance on legitimate uses or exemptions if challenged.

Legacy data is often the most fragile part of a fraud stack. A sensible approach is to start with a mapping exercise: identify which legacy datasets feed fraud and AML controls that are clearly tied to legal obligations, and which are used for broader analytics or cross‑sell. For the first category, you can generally retain and use data to the extent needed to meet those obligations, subject to retention limits under RBI, PMLA, and internal policies. For the second, you may need to narrow use, delete data that lacks a defensible purpose, or, in some cases, re‑engage customers with updated notices and consent opportunities. Whatever you decide, record the reasoning: regulators tend to look more favourably on organisations that can show a structured clean‑up effort than on those that ignore legacy systems until an incident occurs.

No one can predict enforcement patterns with certainty, but early experience from other privacy regimes suggests a few themes. Regulators often prioritise cases involving clear harm to individuals, such as discriminatory profiling, opaque high‑impact decisions, or preventable data breaches. They also pay attention to sectors like finance where both systemic risk and data sensitivity are high. At the fraud–privacy boundary, you should expect questions about whether your institution is using DPDP as an excuse to weaken AML controls, or, conversely, using “fraud prevention” as a label to justify wide‑ranging profiling without consent. Institutions that can show a reasoned mapping of fraud uses to lawful grounds, a functioning governance process, and concrete improvements over time are likely to be in a better position than those that treat DPDP as a documentation exercise bolted on after the fact.

Sources
  1. Digital Anumati – DPDP Act Compliant Consent Management - Digital Anumati
  2. The Digital Personal Data Protection Act, 2023 (Act No. 22 of 2023) - Government of India – IndiaCode
  3. DPDP Act Compliance For Physical And Digital Lending NBFCs - Mondaq / King Stubb & Kasiva
  4. Legitimate interests – UK GDPR guidance - Information Commissioner’s Office (ICO, UK)
  5. Boardroom Briefing: RBI’s new guidelines on strengthening fraud risk management - KPMG India

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