Home Solutions Data science & ML Fraud detection and prevention

Use Cases


Detect fraudulent activities in your applications

Our fraud detection models analyze customer data at the level of individual events using both supervised and unsupervised methods to reliably detect fraudulent activities and transactions. This lowers your fraud risk because the analysis of sequential event patterns helps to detect fraud in early stages, detects patterns that are not identifiable using aggregated metrics, and reveals complex patterns that involve multiple metrics. This approach works particularly well for detecting fraudulent activities in video games, mobile applications, and other settings with rich telemetry data.


Safeguard customer accounts

Automated attacks on customer accounts, gaining access to accounts using stolen credentials, and creating fake accounts are major concerns for virtually any online business. We combine device fingerprinting and artificial intelligence methods to protect your business and customers against bots, avoid losses, and improve customer engagement. The risk management department will be able to stop new fraud in as early stages as possible using our software.


Prevent abuse of services

Abuse of a service is a serious problem for many industries, including retail, technology, and media services. We help you to protect your business from fraud related to pay-as-you-go services, free trials, refunds, and product returns using specialized models that score individual risks as well as generic anomaly detection solutions that can spot new fraud patterns and activities.


Visualize and investigate fraud cases

Our risk-scoring solutions provide convenient interfaces for investigating cases of potential fraud. These interfaces help to quickly pull all pieces of information related to the case, such as users, transactions, merchants, and devices. Machine learning fraud detection algorithms combined with powerful case analysis tools help to reduce manual review expenses and improve operational efficiency. Investigating fraud using our detection software can make the job of your fraud analysts easier.


Determine fraud origination

Our fraud prevention solutions build a transaction graph that allows us to detect fraudulent patterns involving multiple entities. For example, our machine learning algorithms can relate the accounts with common fraud origination points, which are typical for credit card fraud and organized fraud groups. This can help with credit card fraud and fraudulent transactions, so you can ensure that revenue is not lost due to fraud.


Integrate with leading fraud protection services

Grid Dynamics is an official partner of Microsoft Fraud Protection products. We help our clients to integrate with the advanced fraud protection ecosystem created by Microsoft that includes a global entity graph and partnership network with banks. Integration with Microsoft services is the best way to quickly establish fraud protection in small and medium-sized enterprises as well as to improve established fraud protection capabilities using global data and partnerships that are not available at the level of individual enterprises. Our software is trusted by Microsoft, and it can help your company.

Our clients

Google logo
Paypal logo
macy's brand logo


Neiman Marcus logo
Grandvision logo
macy's brand logo
Lowes logo
Logo of American Eagle


Google logo
Verizon logo
IAS logo
2k logo
curiositystream brand logo


Jabil logo
Stanley Black&Decker logo
Levis logo
Boston Scientific logo
Tesla logo


Paypal logo
SunTrust logo
logo of travelers brand
Raymond James logo
risers logo
Marchmilennan logo


align logo
Rally logo
talix logo
Vertex logo
Merck logo

How our fraud detection solutions work

How our fraud detection solutions work

Risk evaluation and control

Our algorithms are design to find optimal trade-offs between false positive and false negative rates to minimize the effort spend on manual case review and investigation, minimize revenue losses, and reduce the impact on customer experience.

Wide range of algorithms for different use cases and industries

We are instrumental in building solutions that use or combine multiple algorithms for transaction analytics. We use deep learning algorithms to analyze non-aggregated event sequences and detect suspicious activities in early stages. This approach works bets for in-app and in-game fraud. We use graph-based machine learning methods to detect fraud patterns that span multiple entities or devices. This approach is typical for credit card fraud. We use anomaly detection algorithms when labeled transaction examples are not available.

Detection of rare events

One of the challenges with automatic fraud detection is a relative rarity of fraud events. We use specialized machine learning algorithms that handle rare events properly, and use the information about historical cases of fraud in the most optimal way.

Detection of classic and emerging patterns

Our solutions combine models that learn on large volumes of historical data and models that analyze ongoing data in real time. This approach helps to accurately detect classic fraud techniques, but also detect new patterns and types of fraudulent activities as they appear.


We develop fraud detection solutions for several industries such as retail, video games, and insurance. For thee industries, we address use cases related to virtual currency fraud, credit card fraud, and insurance claims.

Accelerate the Journey to AI

We provide flexible engagement options to design and build a fraud detection and prevention solution for your company. Contact us today to start with a workshop, discovery, or POC

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