Fraud Detection with Uni (Rust)¶

Detecting money laundering rings (cycles) and shared device anomalies using Uni's native Rust API.

In [ ]:

:dep uni-db = { path = "../../../crates/uni" }
:dep tokio = { version = "1", features = ["full"] }
:dep serde_json = "1"

:dep uni-db = { path = "../../../crates/uni" } :dep tokio = { version = "1", features = ["full"] } :dep serde_json = "1"

In [ ]:

use uni::{Uni, DataType, IndexType, ScalarType, VectorMetric, VectorAlgo, VectorIndexCfg};
use std::collections::HashMap;
use serde_json::json;

// Helper macro to run async code in evcxr
macro_rules! run {
    ($e:expr) => {
        tokio::runtime::Runtime::new().unwrap().block_on($e)
    };
}

use uni::{Uni, DataType, IndexType, ScalarType, VectorMetric, VectorAlgo, VectorIndexCfg}; use std::collections::HashMap; use serde_json::json; // Helper macro to run async code in evcxr macro_rules! run { ($e:expr) => { tokio::runtime::Runtime::new().unwrap().block_on($e) }; }

In [ ]:

let db_path = "./fraud_db";

// Clean up any existing database
if std::path::Path::new(db_path).exists() {
    std::fs::remove_dir_all(db_path).unwrap();
}

let db = run!(Uni::open(db_path).build()).unwrap();
println!("Opened database at {}", db_path);

let db_path = "./fraud_db"; // Clean up any existing database if std::path::Path::new(db_path).exists() { std::fs::remove_dir_all(db_path).unwrap(); } let db = run!(Uni::open(db_path).build()).unwrap(); println!("Opened database at {}", db_path);

1. Schema¶

In [ ]:

run!(async {
    db.schema()
        .label("User")
            .property_nullable("risk_score", DataType::Float32)
        .label("Device")
        .edge_type("SENT_MONEY", &["User"], &["User"])
            .property("amount", DataType::Float64)
        .edge_type("USED_DEVICE", &["User"], &["Device"])
        .apply()
        .await
}).unwrap();

println!("Fraud detection schema created");

run!(async { db.schema() .label("User") .property_nullable("risk_score", DataType::Float32) .label("Device") .edge_type("SENT_MONEY", &["User"], &["User"]) .property("amount", DataType::Float64) .edge_type("USED_DEVICE", &["User"], &["Device"]) .apply() .await }).unwrap(); println!("Fraud detection schema created");

2. Ingestion¶

Creating a cycle A->B->C->A and a shared device scenario.

In [ ]:

// Users with risk scores
let users = vec![
    HashMap::from([("risk_score".to_string(), json!(0.1))]),  // A
    HashMap::from([("risk_score".to_string(), json!(0.2))]),  // B
    HashMap::from([("risk_score".to_string(), json!(0.3))]),  // C
    HashMap::from([("risk_score".to_string(), json!(0.9))]),  // D (Fraudster)
];

let user_vids = run!(db.bulk_insert_vertices("User", users)).unwrap();
let (ua, ub, uc, ud) = (user_vids[0], user_vids[1], user_vids[2], user_vids[3]);

// Device
let devices = vec![HashMap::new()];
let device_vids = run!(db.bulk_insert_vertices("Device", devices)).unwrap();
let d1 = device_vids[0];

// Money transfer cycle: A -> B -> C -> A
run!(db.bulk_insert_edges("SENT_MONEY", vec![
    (ua, ub, HashMap::from([("amount".to_string(), json!(5000.0))])),
    (ub, uc, HashMap::from([("amount".to_string(), json!(5000.0))])),
    (uc, ua, HashMap::from([("amount".to_string(), json!(5000.0))])),
])).unwrap();

// Shared device: User A and Fraudster D share device
run!(db.bulk_insert_edges("USED_DEVICE", vec![
    (ua, d1, HashMap::new()),
    (ud, d1, HashMap::new()),
])).unwrap();

run!(db.flush()).unwrap();
println!("Fraud data ingested");

// Users with risk scores let users = vec![ HashMap::from([("risk_score".to_string(), json!(0.1))]), // A HashMap::from([("risk_score".to_string(), json!(0.2))]), // B HashMap::from([("risk_score".to_string(), json!(0.3))]), // C HashMap::from([("risk_score".to_string(), json!(0.9))]), // D (Fraudster) ]; let user_vids = run!(db.bulk_insert_vertices("User", users)).unwrap(); let (ua, ub, uc, ud) = (user_vids[0], user_vids[1], user_vids[2], user_vids[3]); // Device let devices = vec![HashMap::new()]; let device_vids = run!(db.bulk_insert_vertices("Device", devices)).unwrap(); let d1 = device_vids[0]; // Money transfer cycle: A -> B -> C -> A run!(db.bulk_insert_edges("SENT_MONEY", vec![ (ua, ub, HashMap::from([("amount".to_string(), json!(5000.0))])), (ub, uc, HashMap::from([("amount".to_string(), json!(5000.0))])), (uc, ua, HashMap::from([("amount".to_string(), json!(5000.0))])), ])).unwrap(); // Shared device: User A and Fraudster D share device run!(db.bulk_insert_edges("USED_DEVICE", vec![ (ua, d1, HashMap::new()), (ud, d1, HashMap::new()), ])).unwrap(); run!(db.flush()).unwrap(); println!("Fraud data ingested");

3. Cycle Detection¶

Identifying circular money flow.

In [ ]:

let query_cycle = r#"
    MATCH (a:User)-[:SENT_MONEY]->(b:User)-[:SENT_MONEY]->(c:User)-[:SENT_MONEY]->(a)
    RETURN count(*) as count
"#;

let results = run!(db.query(query_cycle)).unwrap();
println!("Cycles detected: {:?}", results.rows[0]);

let query_cycle = r#" MATCH (a:User)-[:SENT_MONEY]->(b:User)-[:SENT_MONEY]->(c:User)-[:SENT_MONEY]->(a) RETURN count(*) as count "#; let results = run!(db.query(query_cycle)).unwrap(); println!("Cycles detected: {:?}", results.rows[0]);

4. Shared Device Analysis¶

Identifying users who share devices with high-risk users.

In [ ]:

let query_shared = r#"
    MATCH (u:User)-[:USED_DEVICE]->(d:Device)<-[:USED_DEVICE]-(fraudster:User)
    WHERE fraudster.risk_score > 0.8 AND u._vid <> fraudster._vid
    RETURN u._vid as uid
"#;

let results = run!(db.query(query_shared)).unwrap();
println!("User sharing device with fraudster: {:?}", results.rows[0]);

let query_shared = r#" MATCH (u:User)-[:USED_DEVICE]->(d:Device)<-[:USED_DEVICE]-(fraudster:User) WHERE fraudster.risk_score > 0.8 AND u._vid <> fraudster._vid RETURN u._vid as uid "#; let results = run!(db.query(query_shared)).unwrap(); println!("User sharing device with fraudster: {:?}", results.rows[0]);