Fraud is an overarching term for any kind of deception that results in the victim losing money and reputation. In fintech, fraud results in financial losses that have ripple effects across the economy of the world. In an alarming trend, a report estimated that the number of online fraud attacks is nearly 4x more than the rate of increase in online transactions.
Fraud detection software has the tremendous responsibility of protecting the integrity of our financial ecosystem from malicious actors while enabling companies to stay compliant with their country's AML/CFT regulations. Fraud is an imminent danger not just during and post-payments but throughout the entire digital customer journey.
Good fraud detection solutions promise protection from individual pain points at a specific part of the customer journey. These solutions are fragmented and often require pre-existing technical expertise to integrate them for a holistic fraud prevention approach.
But a great fraud detection solution like Bureau provides a no-code platform with 150+ risk signals that can protect your entire customer journey from fraud, and also cater to your risk and compliance needs.
Here's how it all integrates:
- During Pre-boarding: Proactively detect a potentially fraudulent actor by analyzing their digital footprint using alternative data and stop them even before they enter your KYC process.
- During Onboarding: Detect fake identities, and synthetic identities using signals from device behavior and ensure identity verification in line with AML/CFT regulations
- During Login: Use silent authentication that does passive identity verification checks using device and behavioral signals.
- During Transactions: Monitor transactions for fraudulent patterns and anomalies.
- During Ongoing Engagements: It will ensure secure account management and detect fraudulent refund claims or account takeovers.
Types of fraud in a digital customer journey
The types of fraud victims fall prey to differ from stage to stage in a customer journey. If identity thefts are common during onboarding, payment-related frauds like credential stuffing are common during the transaction stage.
Here’s a walkthrough of the different types of fraud that you can see as you move through the customer journey. (Bonus: We also tell you the specific signals that help detect fraud)
Let's get a deeper insight into how exactly these frauds cause damage and how do certain signals help circumvent them. Do you think your business is at risk? Is your business at risk? Schedule a free consultation with us!
1. User Journey Stage: Pre-onboarding/ Pre-registration
This is the stage when a user is targeted by fraudulent actors even before they have been onboarded onto any digital service.
Most prevalent types of fraud:
- Phishing/Social engineering: Fraudsters trick potential customers into providing personal information or clicking on malicious links through fake websites, emails, or messages.
- Bot Fraud: Creating automated scripts (or bots) that attempt to gain unauthorized control over someone else's account
- Stolen identities/fake sign-ups: Stealing the identities of unknown victims or dead individuals
Bureau’s fraud detection methods
- Voice Call Detection: Identifying suspicious voice calls during OTP submissions
- Screen Sharing Detection: Detecting unauthorized screen-sharing activities
- Remote Attack Detection: Spotting remote access threats and intrusions
- Behavioral Coercion Detection: Identifying forced or manipulated user behavior by detecting anomalies in typing velocity and other behavioral biometrics
- Digital Trust Score: Assessing user trustworthiness based on their digital footprint (social media activity, e-commerce activity etc.)
- Behavioral Bot Detection: Identifying non-human, automated user activities
- Scraping Prevention: Blocking unauthorized data extraction attempts
- Proxy IP Address and VPN Detection: Identifying use of proxy IP and VPN services
- Multiple Account Identification: Detecting multiple accounts from the same user on the same device
- Digital Injection Prevention: Identifies fake biometric spoofing attempts. Also read: How are digital injections an imminent threat to liveness detection solutions.
- Location Spoofing Checks: Detecting falsified user location data
- Fraud Ring Detection: Identifying coordinated fraudulent activities network
2. User Journey Stage: Onboarding
In this stage, customers are entering the ecosystem of the digital service and it becomes the responsibility of the service provider to verify the identity of the customer. This is where companies also need to stay compliant with AML/CFT guidelines like conducting KYC processes. Most fraud in this stage aims at bypassing these identification verification barriers. Fraud experts agree that this stage is the most critical stage for preventing fraud further down the customer's lifecycle.
The digital onboarding stage also faces another complication. There is a 68% drop-off rate while onboarding, mostly due to an over-complicated and time-consuming document submission process. Fraud detection companies must strike the right balance between consumer user experience and holistic identity verification for new customers.
Most prevalent types of fraud:
- Synthetic Identities: Combining real and fake information to create new, fraudulent identities to open multiple accounts
- Mule accounts: Financial accounts are created to store or transfer illicit funds
- New to credit fraud: Falsifying identity or information to obtain credit for the first time, exploiting the lack of existing credit history
- Age fraud: Falsifying age to meet eligibility criteria or to bypass age-related restrictions.
- Promo Abuse: Logging in with different identities to exploit promotional offers and referrals
Bureau’s fraud detection methods
- Device Integrity Checks: Analyzes the device's software and hardware for signs of tampering, jailbreaking, or rooting
- Emulator Detections: Detects anomalies in device behavior and identifies use of emulators
- Location Checks: Uses GPS, IP address, and other location data to verify the user's physical location
- Device Fingerprinting: Assigns a unique ID to the device that cannot be changed even after attempts at rooting. This also helps identify if multiple SIMs or app clones are present on the device
- Liveness Solutions: Verifies the biometric data of an individual and detects spoofing methods like deepfakes
- Money Mule Score: Aggregates various alternate data points to score and identify accounts that show a higher propensity of becoming mule accounts. Related read: Bureau launches it Money Mule Score to proactively stop mules from onboarding
- Signals from Telco Networks: Leverages data from telecommunications networks, such as SIM card information and call records, to verify user identity and detect inconsistencies.
- Age Band Predictor: Estimates a user's age based on their behavior patterns and demographic data, ensuring compliance with age-related regulations.
- Link Analysis: Maps and analyzes relationships between various entities (e.g., accounts, transactions, devices) to uncover hidden connections that may indicate organized fraud networks.
Get ahead of these! Schedule a free consultation with us.
3. User Journey Stage: Login
This is the stage where either existing accounts or new accounts are misused for fraudulent purposes.
Most prevalent types of fraud:
- Account takeover: Taking over someone else account using stolen PII data, or coercing someone into giving over account information
- Credential Stuffing: Using stolen credentials from data breaches to log into accounts.
- Geolocation spoofing: Using technology to falsify the physical location of a device to bypass regional restrictions.
Bureau’s fraud detection methods
- Behavioral biometrics: Derives insights from the way a user interacts with their device; such as the way they hold the phone, typing patterns or how they scroll or toggle between fields, fingerprint velocity, Mock GPS detection, emulator detection
- Identity Association: established the association of a particular user with its multiple email IDs, phone numbers, bank acounts etc.
- Identity Vintage: Assesses the number of days from which the device is identified on Bureau's network.
- Link Analysis: Provides a fraud score based on an in-depth analysis of a user’s digital footprint, social media and e-commerce presence
- Aggregate Rules: Identities phone sequence velocity, email clustering, sequenced detection
- Email Clustering: Identifies anomalies in emails and helps distinguish genuine emails from bot-manufactured emails
- Location Intelligence: Pinpoints city, country, latitude, longitude, region
4. User Journey Stage: Transactions
In this stage, existing accounts attempt fraud while completing certain transactions. This is the stage where transactional monitoring becomes crucial to identify anomalies in the digital behavior of the user.
Most prevalent types of fraud:
- Marketplace fraud: Fake storefronts are set up on legitimate platforms or fake products are sold.
- Chargeback fraud: The act of making a purchase and then falsely claiming the transaction was unauthorized or the product was not received to get a refund.
- Identity impersonation: Using stolen or synthetic identities to engage in certain actions that benefit the fraudster
- Geolocation spoofing: Using technology to falsify the physical location of a device to bypass regional restrictions.
Bureau’s fraud detection methods
- Device intelligence: Detection of multiple accounts on the same device and detecting automated sessions Device integrity checks: Analyzes the device's software and hardware for signs of tampering, jailbreaking, or rooting
- Multiple accounts but lower number of users: A combination of insights from device and app usage
- Emulator detection: Detecting the use of emulators
- Locations checks: Location intelligence detects proxy IPs, VPNs and can pinpoint city, country, latitude, longitude, region
- Device Fingerprinting: Assigns a unique ID to the device that cannot be changed even after attempts at rooting. This also helps identify if multiple SIMs or app clones are present on the device
Related read: Understanding Device Intelligence: How It Works and Its Importance in Fraud Prevention
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