# Savas Guner (@savas guner) on linkedin

- id: koinon:creator:linkedin_savas guner
- url: https://koinonlink.com/kol/linkedin/savas%20guner
- schema_version: 1.4

## Identity
- platform: linkedin
- handle: savas guner
- platform_url: https://www.linkedin.com/posts/sava%C5%9F-g%C3%BCner-3536a31a6_igaming-fraudprevention-riskmanagement-activity-7468351671459471360-t1WA
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- posts_observed: 1
- verified: false
- size_tier: nano

## Engagement
- status: not_measurable_on_platform
- status_note: This platform publishes no follower count, so an engagement RATE has no denominator here. No amount of further collection can produce one — it is a property of the platform, not a gap in our data.
- unit_note: engagement_rate and peak_post_engagement_rate are ratios (0-1); avg_interactions_per_post and best_post_interactions are absolute counts. Read status before reading any number: a null rate has several different causes and status says which one applies here.

## Content
- category: gaming
- subcategory: igaming
- category_confidence: 0.95
- category_source: derived_from_recent_content
- topics: iGaming fraud prevention, AI-powered personalization in gambling, crypto industry trends, risk management and compliance
- bio: 🎰 Fraud in iGaming is becoming more sophisticated—and more expensive. As the industry grows, fraudsters are continuously adapting their tactics, creating new challenges for operators, compliance teams, and risk professionals. Some of the key fraud trends shaping the industry today: ✅ Bonus Abuse &

## Audience (inferred)
- status: present
- description: iGaming operators, fraud analysts, compliance teams and risk managers focused on online gambling and crypto
- roles: none
- seniority: practitioner
- pain_points: sophisticated and rapidly evolving fraud tactics, rising financial losses from fraud, need for constant updates to transaction monitoring and risk controls, balancing fraud prevention with player experience
- posts_analysed: 3
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.7
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 60
- authenticity_basis: platform_default
- flagged_for_review: false
- koinon_bd_grade: A — measures Koinon partner-outreach fit, not creator quality
- interpretation: Signals for triage, not verdicts. A flag means the profile is worth a manual check.

## Evidence
- [linkedin_savas guner#083b3c84] Two industries are evolving faster than ever: iGaming and Crypto. Key trends I'm watching in 2026: 🎮 iGaming • AI-powered personalization and player engagement • Faster global payment solutions • Mobile-first user experiences • Responsible gaming technology becoming a competitiv (https://www.linkedin.com/posts/sava%C5%9F-g%C3%BCner-3536a31a6_igaming-fraudprevention-riskmanagement-activity-7468351671459471360-t1WA)
- [linkedin_savas guner#3632e22e] With 6+ years of experience as a Fraud Analyst in the iGaming industry, I’ve built strong expertise in fraud prevention, risk management, transaction monitoring, and KYC/AML processes. Experienced in identifying suspicious activity, reviewing withdrawals and deposits, analyzing p
- [linkedin_savas guner#521550c8] 🎰 Fraud in iGaming is becoming more sophisticated—and more expensive. As the industry grows, fraudsters are continuously adapting their tactics, creating new challenges for operators, compliance teams, and risk professionals. Some of the key fraud trends shaping the industry tod

## Provenance
Every inferred field below carries how it was produced. Facts stay facts; inferences stay inferences.

- audience.description: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=76, method=llm_scorer — Relative signal within Koinon indexing, not a market-wide score.
- commercial.promotion_intent: source_type=ai_inferred, confidence=0.6, freshness_days=76, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=116, method=profile_snapshot_and_engagement_ratio_heuristic — Means "worth a manual check", NOT confirmed fraud. Flags can be wrong and are not a verdict about the person.
- quality.authenticity_score: source_type=heuristic, confidence=0.25, freshness_days=76, method=Fixed value 60. This platform exposes no public follower count, so no authenticity heuristic is run at all. The number is a placeholder, not a measurement. — The input set differs by platform; do not compare this score across platforms.
- quality.koinon_bd_grade: source_type=ai_inferred, confidence=0.5, freshness_days=116, method=llm_partner_fit_classifier + fit_score >= 0.5 => A; >= 0.35 => B; else C. authenticity is not an input. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=116, method=platform_profile

## Freshness
- profile_checked_days_ago: 116
- content_analysed_days_ago: 76
- audience_updated_days_ago: 76

## AI retrieval
- indexing_requests: 11
- user_triggered_requests: 0
- note: Being indexed in bulk is not the same as being retrieved to answer a question.

## This profile
- Claim or correct: https://koinonlink.com/claim?k=linkedin_savas%20guner
- Request removal: https://koinonlink.com/claim?k=linkedin_savas%20guner
- Terms: https://koinonlink.com/for-ai