Predicts if a player is likely to abuse bonuses so offers may be cutback
AI MODEL
BONUS ABUSE
Predicts if a player is likely to abuse bonuses so offers may be cutback
MISUSE OF BONUSES
50% of all frauds in the iGaming, Online
Casinos and Sports Betting businesses involve Bonus
Abuse.
Bonus abuse disrupts it's intended purpose!
Unfair advantages or disadvantages among players. This problem manifests in various
ways, undermining the integrity of games and diminishing the overall player experience.
High risk of financial and reputational damage for operators
BONUS ABUSERS ARE SHREWD
Over 90% of bonus abusers have never been
involved
in a data breach
Most of bonus abusers use a
free email
provider
Bonus abusers do not have any social
media
presence attached to their email address
A major problem for iGaming operators, bonus abuse costs an average of 15% of annual gross
revenues.
Identifying bonus abusers would require a consolidated effort of multiple teams cutting across
functions: Marketing, Promotions, Customer Service, Risk & Compliance, Legal, Business
Intelligence,
etc.
Reliance on other teams can impede the efficient processing of large data sets and
hinder
the rapid and accurate identification of bonus abusers, primarily due to several factors
such as:
Coordination Delays
Knowledge Silos
Resource Allocation
Complexity in Collaboration
Inconsistent Data Handling
Accountability Issues
Increased Error Risk
Importance of not flagging genuine players as Bonus Abusers
Incorrect identification of Bonus Abusers penalizes genuine players, effecting their
experience,
trust and ultimately Business Reputation and Revenue.
It's crucial to differentiate between legitimate gaming activities and fraudulent
exploitation
of promotions.
This distinction ensures that genuine players are not adversely affected by strict
anti-fraud
measures.
By precisely targeting fraudulent behavior, iGaming, Online Casinos and Sport Betting
operators
can safeguard their reputation and financial assets while upholding player experience, trust
and
the effectiveness of their marketing efforts.
Incorrect identification of Bonus Abusers penalizes genuine players, effecting their
experience,
trust and ultimately Business Reputation and Revenue.
It's crucial to differentiate between legitimate gaming activities and fraudulent
exploitation
of promotions.
This distinction ensures that genuine players are not adversely affected by strict
anti-fraud
measures.
By precisely targeting fraudulent behavior, iGaming, Online Casinos and Sport Betting
operators
can safeguard their reputation and financial assets while upholding player experience, trust
and
the effectiveness of their marketing efforts.
GAMWIT’s Approach to Solve These Complex Challenges
Behavior Based - Not Rule Based
Instead of using rule based methods, GAMWIT uses
advanced
AI ML algorithms
to identify behavioral patterns of bonus abusers
Multiple Iterations For Accuracy
It checks multiple instances of these patterns ensuring we
don’t
flag genuine players as bonus abusers.
Behavior Based - Not Rule Based
Instead of using rule based methods, GAMWIT uses
advanced
AI ML algorithms
to identify behavioral patterns of bonus abusers
Multiple Iterations For Accuracy
It checks multiple instances of these patterns ensuring we
don’t
flag genuine players as bonus abusers.
Large Volumes Of Data Analyzed Instantly
GAMWIT checks player-wise, pre and post bonus variations in
withdrawals or deposits, betting patterns, activity and a host of other proprietary
features developed in-house from decades of experience in the iGaming industry.
Specific And Targeted Predictions Without Needing PII
Since the model trains on behavioral patterns of your players
and
patterns displayed
in your games, it does not need Personally Identifiable Information (PII) to
accurately
flag
bonus abusers.
Player Behaviors are
Dynamic
They vary from:
Game to
Game
Company to
Company
Demography to
Demography
Geography to
Geography
The set of
behaviors that flag a player as a 'Bonus Abuser' on your platform may not necessarily be
the
same for another platform.
Since our model trains on your data, it identifies those behaviors that are specific
and
relevant to your players.
GAMWIT's AI Adapts Dynamically
The cognitive capabilities of the GAMWIT's AI immediately detect changes in behaviors of Bonus Abusers over
time.
It then adjusts the model to efficiently adapt and improve the accuracy of prediction.
The cognitive capabilities of the GAMWIT's AI immediately detect changes in behaviors of Bonus Abusers over
time.
It then adjusts the model to efficiently adapt and improve the accuracy of prediction.
This is almost impossible for a human to do without any bias.
Business Benefits
No free lunch for Bonus Abusers!
Safeguard from Bonus Bagging, Bonus Hunting, Bonus Duping,
Multi-accounting, Arbitrage
betting and Chip dumping
Eliminate unnecessary bonus spends
Huge Cost Savings
Improved ROI of campaigns
Boost in NGR and Hold Percentage
Plug and
Play
Solution - No Technical Expertise Necessary!
Our Bonus Abuse Model is a Plug and Play
Solution
that your Marketing, Finance, Risk, and Compliance team can use without any experience
or skill in data analytics or technical know-how.
This allows you to identify and categorize these potential bonus abusers early in your
risk and mitigation program, saving time and resources.
Works with
all Data Formats
Our dedicated
team of experts, helps integrate your gaming data with GAMWIT, no
matter
where, how and in
what format you are collecting or storing it.
Runs
Automatically, so you can Focus on Growth!
Once integrated, GAMWIT is automatic! It delivers
individual player-wise
VIP predictions to your
Risk, Marketing, and Customer Assistance team, scientifically and consistently.
We can also help you integrate GAMWIT with your CRM Systems.
You completely eliminate human bias, or guesswork, because this is data driven - data that is specific to you!
