Fraud prevention

Use real-time data to outpace fraud

To model advanced fraud you need to employ different data models, algorithms, rules engines, machine learning, or artificial intelligence. Aerospike delivers real-time data to all of these various fraud data pipelines at any scale on a fraction of the infrastructure of other offerings.

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Real-time fraud detection at scale

Online fraud requires a real-time response. That’s why Paypal, Barclays, and TransUnion look to Aerospike for fast data for fraud.

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Enterprise graph data for advanced fraud

Modern fraud systems need a graph database that can deliver 100k QPS with 5ms latencies and 99.999% uptime.

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Build your 5-year fraud stack

Will increasing your data sources swamp your fraud infrastructure? You need a real-time fraud stack with unlimited headroom.

Leading businesses rely on Aerospike to act in real time and serve as their foundation for the future.

See all customer stories
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Experian logo
TransUnion
Lexis Nexis Risk Solutions logo
PayPal logo
Experian logo
TransUnion
Lexis Nexis Risk Solutions logo
PayPal logo
Experian logo
TransUnion
Lexis Nexis Risk Solutions logo

Minimizing customer friction for successful digital fraud prevention

Attacks happen across the customer lifecycle, and every type of transaction is at risk. With more digital payments and accounts for any online application now being made in real-time, fraud attacks are growing in sophistication and prevalence.

Organizations need to stop transaction/payment and application/identity fraud, account takeover and money laundering, before they result in financial and reputational damage.

Benefits

Benefits of the Aerospike real-time data platform for combating fraud payment

Only Aerospike is able to process the amount of streaming data combined with system of record data that fraud prevention machine learning algorithms need – without causing undue burden on the end customer, nor the organization’s budgets and staff.

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30x fraud exposure reduction

Fraud calculations get completed faster, with 30x tighter adherence to SLAs.

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Reduction in false positives

Employ 10-times the number of attributes in your same calculation time window.

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De-risk ML model testing

Conduct challenger/champion testing with proven 30 day playback.

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Employ more historical data

Merge system of record data in real-time with streaming transactional data at the edge.

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Save millions per day

Prevent more fraud with more data processed, faster.

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Reliably scale infrastructure

Future-proof your solution with the ability to scale from 10's of terabytes up to petabytes without degradation in system performance nor the undue burden of server sprawl.

Use cases

Fraud prevention is a 24x7x365, global activity

Real-time fraud prevention at its core relies upon fast data processing with machine learning. Organizations should always be looking to minimize their fraud losses. However, they often are unable to combine data silos to populate and conduct meaningful machine learning in real time.

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Transaction (payment) fraud

Today’s Industry 4.0 manufacturing ecosystems are heavily data-driven, as companies are looking to leverage IoT sensors and monitoring devices to measure and analyze vast amounts of streaming data in real-time.

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Application and identity fraud

Prevent fraudsters from using stolen or synthetic identities to open accounts, without stopping good customers.

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Account takeover (ATO) fraud

Protect organization and customers from fraudsters who try to take over accounts to steal from or abuse them.

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Anti-money laundering (AML) fraud

Financial organizations need to analyze vast amounts of transactions for patterns of illicit behavior - typically within seconds. Firms need to comply with AML regulations for: Know your customer (KYC) requirements, currency transaction reporting (CTR), and transaction monitoring.

Testimonials