Advanced Fraud Detection in Financial Services with ArangoDB and AQL

Estimated reading time: 3 minutes

Advanced Fraud Detection: ArangoDB’s AQL vs. Traditional RDBMS

In the realm of financial services, where fraud detection is both critical and complex, the choice of database and query language can impact the efficiency and effectiveness of fraud detection systems. Let’s explore how ArangoDB – a multi-model graph database – is powered by AQL (ArangoDB Query Language) to handle multiple, real-world fraud detection scenarios in a much more seamless and powerful way compared to traditional Relational Database Management Systems (RDBMS).

Graph-Powered Insights with ArangoDB and AQL

ArangoDB’s graph database capabilities, coupled with the expressive power of AQL, provide key advantages in fraud detection, specifically in scenarios involving complex relationships and patterns such as unusual behavior analysis.

Scenario: Advanced Fraud Detection in Financial Transactions

Imagine a scenario in a financial institution where we want to detect potential fraud by identifying patterns that involve rapid sequences of transactions across multiple accounts, which could indicate money laundering or complex fraud schemes. The goal is to find accounts that have received transactions from a flagged account and subsequently made transactions to other accounts within a short time frame, indicating possible layering of transactions to disguise the origin of funds.

AQL for Advanced Fraud Detection

In ArangoDB, utilizing its graph capabilities, the query could traverse through transactions and accounts efficiently:

FOR account IN accounts
FILTER account.status == ‘flagged’
FOR v, e, p IN 2..4 OUTBOUND account transactions
FILTER p.edges[].timestamp ALL < 10 // Assuming timestamp is the time difference in minutes COLLECT suspiciousAccount = v INTO pattern
LET transactionDetails = pattern[].e
FILTER LENGTH(transactionDetails) > 3 // More than 3 rapid transactions
RETURN {
suspiciousAccount,
transactionDetails
}

This AQL efficiently identifies accounts involved in a suspicious chain of transactions originating from a flagged account, considering the rapidity and sequence of these transactions.

Equivalent in SQL (Hypothetical and Simplified for Illustration)

Achieving a similar outcome with SQL in a relational database might involve complex joins, subqueries, and potentially recursive CTEs to analyze transaction chains across multiple levels, which is inherently verbose and less efficient:

WITH RECURSIVE TransactionChains AS (
SELECT
t1.account_id AS start_account,
t1.recipient_account_id AS next_account,
t2.recipient_account_id AS final_account,
t1.amount AS amount1,
t2.amount AS amount2,
t1.timestamp AS timestamp1,
t2.timestamp AS timestamp2,
2 AS depth
FROM transactions t1
JOIN transactions t2 ON t1.recipient_account_id = t2.account_id
WHERE t1.account_id IN (SELECT account_id FROM accounts WHERE status = ‘flagged’)
AND t2.timestamp – t1.timestamp < 10
UNION ALL
SELECT
tc.start_account,
tc.next_account,
t.recipient_account_id,
tc.amount1,
t.amount,
tc.timestamp1,
t.timestamp,
tc.depth + 1
FROM TransactionChains tc
JOIN transactions t ON tc.final_account = t.account_id
WHERE t.timestamp – tc.timestamp2 < 10
AND tc.depth < 4
)
SELECT
tc.start_account,
ARRAY_AGG(DISTINCT tc.next_account) AS involved_accounts,
SUM(tc.amount1 + tc.amount2) AS total_amount,
MAX(tc.depth) AS max_depth
FROM TransactionChains tc
GROUP BY tc.start_account
HAVING COUNT(DISTINCT tc.next_account) > 3;

This SQL tries to mimic the graph traversal by recursively joining transactions to follow the chain from flagged accounts, applying time constraints to identify rapid sequences, and aggregating results to find suspicious accounts. It’s notably more verbose and complex, requiring explicit joins and recursion, which can be less performant and harder to maintain than the AQL approach.

This example showcases AQL’s ability to succinctly address complex, interconnected data scenarios typical in fraud detection, which are inherently more verbose and challenging to express with SQL in a relational database.

Conclusion: The Superiority of ArangoDB and AQL for Fraud Detection

In summary, there is no doubt that traditional RDBMS can handle basic fraud detection tasks.  They fall short, however, in scenarios requiring complex relationship analysis and real-time data processing.

ArangoDB, with AQL, offers a more intuitive, efficient, and scalable solution for the dynamic requirements of fraud detection in financial services. By enabling simpler query structures, faster insights from graph data, and more effective real-time analysis, ArangoDB and AQL streamline the detection process while also enhancing the capacity to uncover and respond to sophisticated fraud schemes.