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Real-Time Fraud & AML Risk Scoring for Cross-Border Fast Payment Rails

With real-time payment linkages between Singapore PayNow and India UPI handling thousands of sub-second cross-border transfers daily, anti-money laundering (AML) detection engines must inspect streaming transaction telemetry without introducing perceptible latency to end users. In this challenge, you will construct a real-time AML risk scoring agent using LlamaIndex. The agent retrieves entity risk profiles, evaluates velocity anomalies across linked payment identifiers, and generates explainable risk evidence reports for MAS compliance officers.

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What you are building

Build a real-time AML risk scoring agent using LlamaIndex RAG over graph entity profiles and streaming transaction logs.

How work is evaluated

Evaluation

Evaluates AML anomaly detection accuracy and generation of compliant audit evidence.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

structuring_flag_check

Must flag transaction sequence as suspicious structuring.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

p99_latency_ms

99th percentile query latency in milliseconds • target: 450 • range: 0-2000

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Implement LlamaIndex index structures for real-time entity profile retrieval

  • Compute transactional velocity and structural graph anomalies in fast payment streams

  • Synthesize natural language explainable AML audit trails compliant with MAS Notice 626

  • Optimize agent retrieval latency to meet sub-500ms processing SLAs

Resources

Dataset notes

PayNow-UPI transaction stream logs with injected smurfing, velocity anomalies, and PEP watchlist records.

Challenge setup

Evaluates AML anomaly detection accuracy and generation of compliant audit evidence.

Challenge input

JSON array of consecutive micro-transfers to linked UPI IDs within 10 minutes

LlamaIndex

Required framework for knowledge graph retrieval and transaction stream context ingestion.

Evaluated output

JSON risk score (0-100), flag_str boolean, and rationale summary

Evaluation checks
  • Must flag transaction sequence as suspicious structuring.
  • 99th percentile query latency in milliseconds • target: 450 • range: 0-2000
Configured evaluation evidence
  • Benchmark: FinAML-Bench
  • P99 Latency Ms target: 450
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • RAI · Optional
  • Relicx · Optional

Evaluation contract

  • structuring_flag_check · Weight 1
  • p99_latency_ms · Weight 1

Metrics

  • p99_latency_ms · Target 450

Benchmarks

  • FinAML-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON array of consecutive micro-transfers to linked UPI IDs within 10 minutes

    Output: JSON risk score (0-100), flag_str boolean, and rationale summary

    Execution harness: Python

Evaluation tools

  • LlamaIndex · RAG Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Real-Time Fraud & AML Risk Scoring for Cross-Border Fast Payment Rails