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  • Is CIBIL enough to detect the financial credibility of the candidate?

    Is CIBIL enough to detect the financial credibility of the candidate?

    The CIBIL score has always been considered the ultimate measure of creditworthiness of a person in the financial ecosystem of India. Is it, however, a real gauge of the financial soundness of an individual? Numerous voices in the industry, and increasingly public, claim that one three-digit figure cannot tell the entire tale of reliability of a borrower. It tends to ignore changing behaviors, alternative cues and context of financial behaviors. According to a 2025 report by Edelweiss, 20–25% of personal loans, credit card accounts, and consumer durable credit in India are being issued to borrowers with CIBIL scores below 650.  Consequently, the use of CIBIL can create bias and curtail the chances of worthy credit seekers. 

    How CIBIL score Influences Credit Score? (Use Cases) 

    Regardless of its shortcomings not being to act as the fraud detection solutions, the CIBIL score still strongly impacts the risk scoring of any individual: 

    1. Loan & Credit Approvals  

    CIBIL is commonly used to determine eligibility by financial institutions. An increase in the score (750 and above is an excellent score) tends to be associated with the easier availability of the loan and preferential interest rates.  

    2. Interest Rate Determination  

    High-score borrowers tend to incur reduced costs of borrowing, because lenders equate a high score to a reduced risk of default.  

    3. Employment & Rentals  

    In parallel sectors:  

    They do credit checks on employers up to 60 percent during hiring. Credit information is occasionally used to determine risk by auto insurers, telecoms and landlords.  

    4. Financial Products & Segmentation  

     CIBIL also frequently determines access not only to loans but also to credit cards, increased credit limits, and custom financial products that augment its position at the heart of the credit ecosystem. 

    Does CIBIL Act As A Fraud Detection Solution During Customer Onboarding? 

    CIBIL-driven customer  onboarding introduces both benefits and challenges:  

    Pros  

    • Speed & Standardization: Flagging of applicants in thresholds is done automatically, making decisions straight forward.   
    • Regulatory Clarity: It is easy to explain credit results when they are related to numeric scores.  

    Cons   

    • Superficial Evaluation: Does not consider new-income users, gig workers, micro-entrepreneurs, or thin credit files.  
    • Exclusion Risk: This is a risk that could prevent access to services by underserved individuals who qualify.  
    • Opaque Rejection Reasons: Borrowers do not usually get a reason why an application has been rejected, the score itself disguises individual rationale. 

    Thus CIBIL works soley based on the Credit profile of the interests. A person can have a high credit score and still be a fraudster. Thus CIBIL does not act as the fraud detection solution 

    What’s “Above” CIBIL Score? Alternative Explanations of Credibility 

    As of December 2024, approximately 451 million Indians had limited or no formal access to credit, highlighting the potential reach of alternative scoring methods. There were many synthesized IDs on the rise and the credit score was not alone adequate enough to determine the genunity of the candidate. Thus, the lending sector was on the look for the best fraud detection solution. Going beyond the numeric scoring requires assessment of credit on a multi-dimensional basis:   

    1. Alternative Credit Scoring   

    • Leverages GST returns, bank statements, and cash flow data, especially helpful for first-time borrowers.   
    • Predictive models are also informed by digital footprints and behavioral signs (e.g., mobile use, pattern of digital transactions).  

    2. Machine Learning & Big Data Analytics  

    • Social network analytics and call-detail records, app usage and others can be combined with AI algorithms to increase the statistical accuracy and profitability of risk models.  

    3. Behavioral and Psychometric Signals  

    • Even when no traditional credit history is available, behavioral credit models follow repayment patterns, online actions, and psychometrics in order to calculate risk more effectively. 

    How the Financial Sector Can Leverage Fraud Detection Solutions?  

    Composite risk assessment methods: Financial institutions are benefiting by incorporating composite risk assessment methods:   

    1. Inclusion and Financial Deepening  

    • Providing other credit models to unbanked or underbanked populations opens new markets and grows the financial inclusivity. This includes analyzing their social credit and AML compatibility as a part of their credit scoring. Simultaneously it identifies any forged or synthesized documents acting as the fraud detection solution as well 

    2. Enhanced Risk Precision   

    • Greater datasets and enhanced analytics enhance the prediction of defaults acting as the fraud detection solution, and reduce non-performing loans. 

    3. Faster and Fairer Onboarding  

    • Incorporating onboarding behavioral and contextual data in real-time can enhance the speed of onboarding and at the same time bias reduction that is a characteristic of conventional scoring.  

    4. Transparency and Explainability  

    • Models that use artificially intelligent (AI) and various signals will better explain credit decisions, and allow borrowers to understand and change their financial behavior. 

    How Atna Has A Unified Risk Intteligence Platform

    A standout in this holistic risk paradigm is Atna, which provides the next-gen credit + onboarding tools to the financial sector along with fraud detection solutions that combats today’s brute-force techniques:  

     1. Atna Score  

    AI-driven, unified risk measure combining identity and behavioral analytics, plus document analytics, to make decisions with confidence and speed.  

    2. Digital Footprinting  

    Checks device, location, user-behavior signals to indicate anomalies and suspicious patterns in real-time -improving fraud detection and reducing onboarding friction.  

    3. AI-driven KYC / KYB  

     Verifies documents (KYC and KYB) (including GST, ownership structure, and regulatory compliance) using automation.  

    4. AML Intelligence & Deepfake Detection  

    Couples anti-money laundering checks and deepfake detection to verify authenticity and integrity of the identities submitted by users.  

    5. Predictive Risk Scoring  

    Integrates behavioral indicators, document confidence, and digital footprint into a predictive scoring model- allows BFSI players to dynamically measure risk and can adjust thresholds based on use cases.  

    Atna’s Parameters vs. the Credibility They Uncover 

    Parameter Checked by Atna Credibility Insight Uncovered 
    Identity Verification ( KYC/KYB)Confirms authenticity of individuals & businesses, reducing impersonation and fake onboarding. 
    Document Confidence Scoring Ensures submitted IDs, GST, PAN, and ownership docs are genuine and tamper-proof. 
    Digital Footprinting Reveals behavioral consistency, location, device, IP usage, exposing fraud or bot-led applications. 
    Behavioral Analytics Tracks repayment patterns, income flows, spending habits, signaling long-term financial discipline.
    AML & Sanction Checks Detects links to money laundering, financial crimes, or restricted entities. 
    Deepfake Detection Validates biometric and video KYC, ensuring applicants are who they claim to be. 
    Atna Score Combines all signals into a dynamic risk index that predicts probability of default or fraud. 

    Conclusion 

    The CIBIL score is still a helpful benchmark in credit checking today- but not a comprehensive or adequate measure of credibility or can acts as the fraud detection solution. It smears over true borrowers who are credit history deficient, and its opaqueness can aggravate customer onboarding. The future looks in the combination of conventional metrics and the analysis of alternative data, AI-related behavioral expectations, and comprehensive risk indicators.  

     In that future, BFSI sectors will be able to improve:  

    • Expand inclusive lending responsibly,  
    • Improve underwriting precision,  
    • Frictionless onboarding  
    •  Strengthen fraud and identity assurance.  

    An example of this new generation of credit and onboarding intelligence is Atna, where identity, behavior, document analysis, and predictive risk are combined in a single platform, acting as the best fraud detection solution. It provides financial institutions with more than a means to gauge credit, but a real appreciation of credibility in context.  

     Having gone beyond relying on a single score, institutions can open to new, underserved markets, make smarter decisions, and build a more equal financial ecosystem. 

  • Top DeepFake Threats To Businesses And Its Combat Solutions

    Top DeepFake Threats To Businesses And Its Combat Solutions

    With the development of generative AI, the rise of deepfakes has become huge and has become more hazardous in sectors like banking and insurance. Financial institutions are facing a significant increase in deepfake fraud attempts, which have grown by 2137% in the last three years. The impact of deepfakes on the surface, such as impersonating a celebrity, goes far deeper in enterprise levels. All the deepfake threats, such as mimicked faces, forged voices, fabricated documents, and generated entire digital personas, can easily bypass the scanners and can result in money laundering processes. It not just drops the reputation but also imposes huge financial and operational losses.  

    This blog explores all the patterns of how deepfake is happening and how big businesses can crumble if they do not equip themselves with the necessary risk intelligence techniques for deepfake detection.  

    1. Synthetic Identities in Onboarding

    Onboarding customers or even employees is a serious process in the deepfake threats era. People can synthesize their IDs and promote a fake one to bypass KYC. Fraudsters use deepfake tools to create entirely synthetic profiles with: 

    • High-resolution fake IDs 
    • AI-altered selfies that pass face-matching 
    • Pre-recorded deepfake video clips mimicking live gestures 
    • Cloned voice responses for audio verification 

    With remote onboarding processes on the rise, deepfake threats are best using the situation. With generative scripts and automation, a single user can log into the account and create multiple fake accounts. A single attacker can onboard hundreds of fake profiles across multiple institutions, aided by generative scripts and AI automation. 

    2. Deepfakes threats in Insurance Claims 

    The onboarding is the entry point for the deepfake threats; the insurance claims are the exit point where the synthetic identities cash in.  

    Fraudsters now submit: 

    • AI-generated videos of supposed hospital stays or staged car accidents  
    • Fabricated police reports or discharge summaries  
    • Manipulated evidence of property damage using image synthesis  

    As far as it is approved, such claims are paid with real money. The deepfake threat is committed by destroying the identity, with little trace to be followed, as the identity verification and burner accounts involved in the onboarding process are usually false, creating consumption fraud committed with deepfake threats

    3. Deepfake Threats In Job Applications  

    The menace does not end with customers. Moreover, deepfake threats are also becoming a mainstream method of attackers getting into an organization as employees 

    • Pre-recorded deepfake videos to pass HR interviews  
    • Cloned voices to attend onboarding or training  
    • AI-fabricated resumes and certifications to match job criteria  

    When hired, the following activities can be performed by such synthetic employees: 

    • Approve fraudulent claims  
    • Leak sensitive customer data  
    • Manipulate internal systems to enable large-scale fraud.  

    4. Deepfake Threats Through Voice Cloning

    Voice biometrics is another authentication mode that banks and insurers thinks to be safe. Pindrop’s 2025 Voice Intelligence & Security Report Reveals +1,300% Surge in Deepfake Fraud.  One of the advanced deepfake threat is the voice technology that may reproduce the mannerism, accent, and tone of a person with as little as 30 seconds 

    This leads to attacks where fraudsters: 

    • Bypass voice-based IVR systems  
    • Call support centers pretending to be customers.  
    • Request sensitive actions like password resets or fund transfers.  

    Such deepfake threats are usually very deceptive, to the extent that human agents also fail to realize the fraud. 

    5. When Synthetic Identities Breach Compliance  

    Technologically, onboarding a deepfake identity is a regulatory nightmare. When onboarding someone who does not exist, the person will be exposed to 

    • Fines for failure to comply with KYC norms  
    • AML breaches if the account is used for illicit transactions  
    • Audits and reputation loss due to systemic lapses  

    In extreme deepfake threat cases, foreign partners can blacklist the institution, therefore having cross-border consequences and limiting the operation of the institutions in the long run.  

    6. The Path Forward for Institutions  

    Financial establishments have to comprehend that deepfake threats are not a once-in-a-lifetime situation. To pass this wave, they must adopt key defense strategies include 

    • Liveness detection during video KYC to spot synthetic video playback  
    • Behavioral biometrics to monitor unnatural user interactions  
    • Multi-factor verification beyond facial and voice data  
    • Device and location intelligence to spot anomalies in onboarding patterns  
    • AI-driven anomaly detection to flag suspicious claims or actions  

    Insurance and banks need to develop an awareness culture as they cultivate teams to detect social engineering, video abnormalities, inconsistency in documentation, and so on. 

    The War on Deepfake Fraud Has Already Begun 

    It is no longer the age when we can talk of the deepfake threat being a potential risk but a real one already. The deepfake threats are not just reputational risks to the bank or insurance company but also regulatory, financial, and systemic. 

    Deepfake detection platforms play a crucial role in combating the rising threat of AI-generated synthetic media. By leveraging advanced algorithms and machine learning models, these platforms can identify manipulated visuals, falsified identities, and spoofed audio with high accuracy.  

    The Hawkings Of Deepfake Combat 

    As deepfake threats continue to evolve, detection platforms are becoming essential for digital trust and security. Atna leads the way with its robust deepfake detection solutions, empowering businesses to verify authenticity, safeguard operations, and stay ahead of synthetic fraud.

    Atna is at the forefront of this transformation. Its AI-powered deepfake detection system offers businesses an edge by providing early warnings, actionable insights, and automated flagging mechanisms that block bad actors before any damage is done. Whether it’s a bank verifying a new account or an insurer checking a claimant’s identity, Atna ensures authenticity is never compromised. 

  • How to Reduce Drop-offs in KYC Without Compromising Risk

    How to Reduce Drop-offs in KYC Without Compromising Risk

    The Problem: KYC Kills Conversions

    KYC is critical—but also painful. Every additional step in identity verification increases user friction, which directly impacts conversion rates, especially in onboarding-heavy industries like fintech, BNPL, and neobanking.

    • Users abandon when upload fails
    • They quit when video liveness takes too long
    • They hesitate when asked for too much too soon

    But removing checks is risky. So the real question is: How can platforms improve completion rates without compromising fraud detection or compliance?

    The Solution: Intelligent, Friction-Less KYC

    Here’s how smart platforms are reducing drop-offs while still managing identity risk:

    1. Replace Liveness Video with Image-Based Verification

    Videos take time to record, fail in poor networks, and intimidate users. ATNA uses image-only checks—verifying document integrity and facial consistency without requiring a selfie video.

    Impact:

    • 30% reduction in dropout during selfie stage
    • Works well in Tier-2/3 markets with low bandwidth

    2. Pre-Fill What You Can from the Document

    Instead of asking users to manually type their name, DOB, and address, extract those directly from the uploaded ID and auto-fill the form. ATNA’s AI-KYC does this in real time.

    Impact:

    • Reduces user effort
    • Prevents mismatches due to typos

    3. Skip Extra Documents Using ATNA Score

    Instead of asking for additional documents when you’re unsure, use ATNA Score to calculate real-time onboarding risk based on document quality, digital footprint, and passive behavior signals.

    Impact:

    • 25% reduction in document re-request
    • Better user experience without cutting risk coverage

    4. Start with Passive Signals First

    Before even asking the user for a document, analyze device, IP, network, and behavioral traits using ATNA’s Digital Footprinting.

    Impact:

    • Identify suspicious users before asking for verification
    • Personalize the level of KYC required

    5. Use Conditional Workflows Based on Risk Tier

    All users don’t need full KYC. ATNA lets you adjust flows:

    • Low-risk: Document + image validation
    • Medium-risk: Add footprinting + extra ID
    • High-risk: Redirect to manual review

    Impact:

    • Right-sized effort for every user
    • Reduces over-verification and under-verification risks

    Conclusion

    Reducing KYC drop-offs isn’t about removing checks—it’s about making them smarter, faster, and invisible where possible. ATNA delivers adaptive KYC without compromising.

  • Building a Risk Scoring Model That Actually Works

    Building a Risk Scoring Model That Actually Works

    Why Most Risk Scores Fail

    Risk scoring is often seen as a magic formula. But in reality, many scoring models fall short because they:

    • Rely on outdated or siloed data
    • Are hardcoded and inflexible
    • Lack transparency and explainability
    • Don’t adapt to evolving fraud patterns

    What Makes an Effective Risk Scoring Model?

    An effective model isn’t just a number it’s a decision enabler. It should:

    • Combine multiple risk dimensions (not just one source)
    • Offer tunable sensitivity based on your use case (e.g., lending vs. onboarding)
    • Provide explainable breakdowns of why a user was scored high or low
    • Be easy to integrate, monitor, and update

    Step-by-Step: Building a Real-World Risk Score with ATNA

    1. Start with the Signals You Trust

    Identify the risk signals you already collect or can plug in:

    • Document Signals (via AI-KYC): ID validity, layout inconsistencies, tampering indicators
    • Behavioral Signals (via Digital Footprinting): IP reputation, device anomalies, location mismatches
    • Business Signals (via KYB): registration status, UBO mapping, GST number match
    • External Signals (via AML Intelligence): watchlist presence, media flags

    Tip: The broader the signal set, the more resilient your score will be.

    2. Assign Weights Based on Context

    A lending platform might weigh behavior and ID quality more heavily. A marketplace might favor KYB and AML flags.

    With ATNA Score, you can:

    • Define custom weights per signal category
    • Apply different scoring logic to different user types or flows
    • Adjust thresholds as you gather real-world feedback

    3. Normalize and Aggregate

    ATNA automatically normalizes raw signal data into a standard format (0–100 scale), making it easy to compare and combine.

    Your scoring engine should:

    • Penalize strong risk signals (e.g., fake ID) heavily
    • Allow positive offsets (e.g., verified GST, clean AML)
    • Be explainable at every step

    4. Automate Decisions, Not Just Scores

    What matters most is <b> what you do with the score:</b>

    • 0–40: Auto-approve
    • 41–70: Escalate to additional checks
    • 71–100: Manual review or reject

    ATNA Score lets you embed rules directly in your system—or pipe results into your workflow engine, CRM, or onboarding UI.

    5. Monitor, Adapt, and Evolve

    No score is static. As fraud evolves, your model must too.

    ATNA gives:

    • Real-time dashboards to monitor score distributions
    • Signal-level feedback for failed approvals
    • Logs for auditing and machine learning refinement

    Pro Tip: Start Simple, Then Optimize

    You don’t need a perfect model to launch. Start with core signals, go live, gather data and then iterate.

    Conclusion

    A great risk score doesn’t just tell you who to trust it gives your platform the power to act, adapt, and scale.

    RATNA’s modular scoring engine was built exactly for this. No black boxes. Just signals that speak your language.

  • What Is Digital Footprinting and Why It Matters in 2025

    What Is Digital Footprinting and Why It Matters in 2025

    The New Frontier of Risk Intelligence

    In a world where IDs can be faked and forms can be filled by bots, the real question is: Can you trust the person behind the screen?

    Digital Footprinting gives you the answer—without asking the user a single extra question.

    What Is Digital Footprinting?

    Digital Footprinting is the science of analyzing the invisible traits of a user like their device, network, and behavior—to assess trust or risk in real time.

    It runs passively in the background and feeds high-signal data into fraud detection, onboarding, and compliance decisions before users upload a document or fill a form.

    Think of it as the digital body language of your users.

    Why It’s a Must-Have in 2025

    In 2025, fraud looks different:

    • Synthetic identities are indistinguishable from real ones
    • Bots and emulators mimic human interaction
    • Static checks like ID verification aren’t enough

    Digital footprinting adds a contextual, real-time layer of defense that legacy systems miss without slowing down genuine users.

    What You Can Detect with ATNA’s Digital Footprinting

    Signal Type Examples

    • Device: Jailbreak/rooted devices, screen resolution mismatches, font fingerprints
    • Network: IP reputation, TOR/proxy detection, geolocation drift
    • Behavior: Mouse movement anomalies, typing cadence, click patterns
    • Session: Shared devices, returning risk profiles, emulator environments

    Real-World Use Cases

    • Fintech Onboarding: Detect fake borrowers before asking for documents
    • Marketplace KYC – Device/IP: Flag merchant accounts using the same device/IP
    • Marketplace KYC – Risk Scoring: Adjust ATNA Score based on passive risk
    • Insurance Claims: Catch bots or replay frauds during claims submission

    Business Impact

    • 30% fewer fraudulent signups
    • Up to 70% fewer manual reviews
    • 100% passive — no added friction

    Seamless Integration

    • Lightweight JS or SDK embed
    • Feeds directly into ATNA Score or your own workflows
    • Real-time API access + dashboards

    Summary: Trust the Behavior, Not Just the ID

    In 2025, static checks won’t keep you safe.

    Digital Footprinting tells you what kind of user you’re dealing with before they type a single word.

    Know who’s real. Know who’s risky.