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Fraud and Scam Detection

Advanced protection for today's digital brands

Protect your customers and brand with 1Point1’s 24/7 Fraud and Scam Detection. We stop fake accounts, payment fraud,
and other digital threats. Our real-time technology plus human experts spot red flags fast.
We detect scams and block fraud across every user touchpoint.
‍
Serving global ecommerce and high-traffic social platforms, our solution scales with you.
Deliver safer digital experiences and safeguard your reputation.

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Why fraud is getting harder to catch without AI

How modern fraud tactics outpace traditional rule-based detection

Machine learning fraud prevention provides adaptability, pattern recognition, and predictive power that static rules cannot, catching unknown fraud patterns and subtle correlations that rule-based systems miss entirely, while significantly cutting false positives and false negatives.

The financial and reputational cost of undetected fraud on your platform

Static fraud detection using AI-free systems creates operational drag and reputational risk when fraud systems misfire; they don't just miss threats, they flag legitimate transactions, forcing customers through unnecessary verification and damaging trust at scale.

Tailored Fraud Prevention for ‍Your Platform

Every platform faces different threats, so why settle for one-size-fits-all protection?
At 1Point1, we offer a flexible suite of fraud and scam prevention microservices, each designed to match your platform’s specific risk profile. Whether you're battling bots, scams, or suspicious behavior, we have a solution that fits.
Explore our custom-built options below.

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Profile Fraud Detection

  • Detect fake or bot-generated profiles
  • Verify account authenticity at onboarding
  • Block IP addresses related to any malicious activity

Transaction and Payment Fraud Monitoring

  • Real-time transaction analysis
  • Detect suspicious payment patterns and chargebacks
  • Prevent refund abuse and financial manipulation

Referral and Incentive
Abuse Detection

  • Identify multi-account abuse for referral rewards
  • Track suspicious invitation patterns
  • Limit fake engagement and bonus misuse

Content and Video
Scam Moderation

  • Analyze visual content for scam signs
  • Remove phishing links, fake giveaways, and misleading media
  • Ensure safe content across UGC formats

Phishing and
Spoofing Detection

  • Detect fraudulent URLs, mimic pages, and clone content
  • Prevent impersonation scams
  • Use advanced NLP and behavioural analysis

Behavioral Anomaly Tracking

  • AI-based pattern recognition of user behaviour
  • Flag sudden access/location/IP changes
  • Alert and act on high-risk activities

Chargeback Prevention

  • Identifies transaction patterns and user behaviors that indicate chargeback risk before disputes are filed
  • Flags refund abuse, friendly fraud, and policy exploitation in real time across payment flows
  • Reduces chargeback ratios and protects merchant standing with payment processors and acquiring banks

Machine Learning Fraud Prevention & Pattern Detection

  • Deploys machine learning fraud prevention models trained on your platform's historical transaction and behavioral data
  • Continuously learns from new fraud signals, improving detection accuracy over time without manual rule updates
  • Identifies coordinated attack patterns, including bot networks and synthetic identity clusters

Predictive Fraud Analytics & Risk Scoring

  • Uses predictive fraud analytics to assign real-time risk scores to users, sessions, and transactions based on behavioral and contextual signals
  • Enables platform teams to act on risk before fraud is confirmed, flagging high-risk activity for review rather than waiting for a completed fraudulent event
  • Risk scores are configured to your platform's tolerance levels and integrated into existing decision workflows without disrupting legitimate user journeys

Regulatory Compliance & Audit Reporting

  • Maintains structured audit trails of all flagged activity, escalation decisions, and outcomes for compliance and legal review purposes
  • Supports adherence to applicable regulatory frameworks, including AML requirements, PCI-DSS standards, and regional data protection obligations
  • Generates compliance-ready reports on a scheduled basis, ensuring platform operators have documented evidence of fraud monitoring and response activity

How a fraud detection engagement works

Step 1: Threat Assessment & Risk Profiling

We begin by mapping your platform's specific fraud exposure, reviewing historical incident data, transaction flows, user onboarding patterns, and existing detection gaps. This threat assessment defines which fraud vectors pose the highest risk and where AI scam detection should be prioritized first.

Step 2: AI Model Training on Platform-Specific Data

Our machine learning fraud prevention models are trained on your platform's own data, not generic fraud datasets that may not reflect your user base or transaction types. This ensures detection accuracy is calibrated to your actual risk profile from the first day of live operation.

Step 3: Rule Configuration & Threshold Setting

Alongside AI models, we configure rule-based logic and risk thresholds appropriate to your platform's tolerance levels. Thresholds are set collaboratively, balancing fraud catch rates against false positive risk, so legitimate users are not disrupted while fraudulent activity is accurately intercepted.

Step 4: Live Monitoring & Escalation Workflows

Operations go live with real-time monitoring across all defined fraud vectors. Fraud detection using AI flags high-risk activity automatically, with escalation workflows routing confirmed or borderline cases to trained human analysts for final review, maintaining human-in-the-loop oversight where it matters most.

Step 5: Continuous Model Tuning & Reporting

Predictive fraud analytics models are reviewed and retrained regularly based on emerging fraud patterns, false positive trends, and platform changes. Monthly performance reports cover detection rates, chargeback trends, escalation volumes, and compliance metrics, giving platform operators full operational visibility.

Fraud detection across every industry

Telecom & Media

Subscription fraud, SIM swapping, and account sharing manipulation create significant revenue leakage for telecom and media platforms. Our AI scam detection identifies onboarding fraud and unusual account activity patterns early, reducing exposure before fraudulent subscriptions or service misuse compounds into large-scale losses.

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Travel, Tourism, & Hospitality

Travel platforms face loyalty point fraud, fake booking manipulation, and payment fraud with compounding reputational risk. Our predictive fraud analytics flags suspicious booking patterns, account anomalies, and payment risk signals before transactions complete, protecting both platform revenue and the customer experience for legitimate travellers.

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Gaming & Entertainment

Gaming platforms are high-value targets for account takeovers, in-game currency fraud, referral abuse, and fake engagement manipulation. Our machine learning fraud prevention for gaming detects behavioral anomalies in real time, identifying compromised accounts and coordinated fraud rings before they distort platform economics or user experience.

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E-Commerce & Retail

E-commerce fraud spans fake accounts, stolen card transactions, refund abuse, and coordinated bot activity during promotions. Our fraud detection using AI for retail platforms operates across the full purchase journey, from onboarding and checkout to post-purchase dispute management, protecting both merchants and genuine customers.

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BFSI

Financial services platforms face fraud across every touchpoint, such as account opening, authentication, payments, and claims. Our AI scam detection for BFSI covers synthetic identity fraud, authorized push payment scams, and transaction anomalies, within compliance frameworks that meet the documentation and audit requirements of regulated financial environments.

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Reduced fake
account creation by up to 60% in 90 days
Maintained more
than 90% accuracy in scam identification
Enhanced brand
NPS by 15 points in 6 months
Improved user
trust scores by over 40%
CSAT improved
by up to 90%, resulting in improved retention
Reduced legal
claims by over 25% year-over-year
Blocked over 2M
bot interactions in a quarter
100% data
compliance across all client projects
Why companies
choose 1Point1

Our analysts and automation work together to stop financial loss and protect your brand. We flag harmful content, scam attempts, and fraud before they spread. Global platforms trust us for scam detection and fraud prevention.
Here’s why companies partner with us:

AI-Driven Detection
Harness machine learning to identify emerging fraud patterns in real time and take swift action.
Expert Human Moderators
Leverage our in-house team that undergoes rigorous training in fraud recognition and scam detection.
Stronger Brand Reputation
Show customers and investors you prioritize safety with consistent fraud deterrence.
Platform Integrity Protection
Maintain user trust and brand authenticity by eliminating fraudulent behavior.
User Confidence & Retention
Build a safer space where users can engage confidently, increasing platform engagement.
Financial and Legal Risk Reduction
Proactively minimize liability with consistent fraud monitoring and preventive measures.
Resource Abuse Prevention
Detect and stop misuse of platform tools, referral abuse, and fake traffic.
Secure and Confidential Operations
Ensure complete data protection and platform integrity through end-to-end encryption and compliance.
FAQs
1. How does 1Point1 use machine learning for fraud prevention, and how are models trained on my platform's data?

Our machine learning fraud prevention models are trained on your platform's own transaction records, behavioral logs, and incident history, not generic datasets. Models are retrained regularly as new fraud patterns emerge to maintain detection accuracy over time.

2. What is predictive fraud analytics and how is it different from reactive fraud detection?

Predictive fraud analytics assigns real-time risk scores to users, sessions, and transactions, enabling action before fraud completes. Reactive detection responds after the event. The difference is the window: predictive analytics closes it before damage occurs.

3. How does 1Point1's fraud detection handle new scam tactics that haven't been seen before?

Our fraud detection using AI uses anomaly detection models that flag deviations from established normal behavior, identifying unusual patterns even when the specific tactic is new. Models are updated as new tactics are confirmed, creating a continuously learning detection layer.

4. What is the false positive rate, and how does 1Point1 balance catching fraud without flagging legitimate users?

Risk thresholds are calibrated collaboratively during setup. AI scam detection flags are reviewed by human analysts before action is taken on borderline cases, and threshold performance is reviewed monthly to reduce false positives without compromising detection accuracy.

5. How does AI scam detection integrate with an existing CRM or payment platform without disrupting live operations?

Our AI scam detection integrates via API with existing CRM, payment, and case management platforms, configured to run alongside live operations without altering transaction flows or user-facing experiences.

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