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Game Changer in Real-time Fraud Detection

Every year enterprises and governments are losing hundreds of billions of dollars due to fraud. As more and more transactions are conducted over the internet, online fraud detection, anti-money laundering and decision makings have become a necessity. Traditional RDBMS or Big-data-n-AI based fraud-detection or decision-making systems are not capable of online real-time fraud detection or decision making.

Graph database offers a graph natural storge and efficient processing of the complex interconnections among massive data points, making it possible to detect anomalies hidden many layers behind seemingly normal behaviors in real time.

SCENARIO 1

Synthetic Identity Fraud Detection

Synthetic identities that combine real and fake personal information are often used by fraudsters to obscure their activities and obstruct investigations into these accounts.

By storing the personal information as nodes and establishing edges to connect them, it is no longer a challenge for graph database to find ring-structure paths which often appear in synthetic identities.

Download Solution
finding-ring-structure-paths
similarity-check

SCENARIO 2

Loan Application Fraud Detection

Information contained in each application is extracted and modelled as different type of nodes, waiting for the similarity check.

With the real-time graph architecture, the anti-fraud model can be done in genuine real-time, even hundreds of millions of similarity checks have to be run.

Download Solution

SCENARIO 3

Networked Behavior Pattern-Matching

Abnormalities often manifest in the historical network behaviors of transactional counterparties.

After forming a transaction network comprising nodes representing accounts and edges depicting recent transactions between these accounts, lots of suspicious behavioral patterns can be inspected when one account initiates a transaction request to another account.

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transaction-network

CASE STUDY

Identify Fraudulent Risks Associated with Debit Cards

A leading joint-stock retail & commercial bank has been exploring ways to identify fraudulent risks associated with their debit cards, looking for a graph database to handle supernodes with a 30ms latency for each transaction analysis.

With the help of Ultipa, for the first time ever, the bank’s IT sees a complete graph data network with all supernodes included, and no backlog is piled up when tons of transactions are rushing in during peak hours. The ROI and performance gain is in the range of 100 to 1,000 of times, and each model takes only 0.1 millisecond (100 microseconds) in decision-making for each transaction.

That’s the beauty of real-time graph computing, only from Ultipa.

Download Solution
credit-assessment

Combatting Fraud with Ultipa

Ultipa graph database offers a higher-level overview of interconnected data points and comes with comprehensive analytical suite and real-time graph computing capability, empowering financial institutions to stay one step ahead of the fraudsters. With the ability to ingest and integrate new data points in real-time, its utility is enhanced in highly dynamic financial environments.

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