Background

In the era of rising financial cybercrimes, mule accounts, money laundering, and complex transaction layering, it is increasingly difficult for law enforcement and financial institutions to trace the origin, flow, and destination of suspicious funds. Traditional manual investigation techniques are time-consuming and lack the capability to process large volumes of banking data efficiently.

Problem Statement

Investigators and compliance officers often deal with thousands of transaction records spread across multiple bank accounts. Identifying unusual patterns such as rapid cash inflow and outflow, circular transactions, links to known mule accounts, or geographic anomalies is critical but tedious. There is a pressing need for a digital tool that can automate the analysis of bank account data and provide investigators with intelligent insights.

Key Objectives

Design and develop a Bank Account Analysis Tool that can:

1.  Ingest and parse bank statements in multiple formats (CSV, PDF, XLS).

2.  Detect suspicious patterns such as:

o Sudden large transactions

o Frequent small credits from unrelated accounts

o Round-trip transactions

o Transactions with blacklisted or high-risk accounts

3.  Map and visualize transaction networks using graph-based analytics.

4.  Provide account risk scoring based on predefined rules or machine learning.

5.  Support linking of accounts across banks via identifiers (phone numbers, UPI IDs, IP addresses, etc.).

6.  Generate a forensic report exportable in PDF/Word summarizing key findings.

Functional Requirements

• Rules engine or ML for detecting anomalies.
• User authentication and access control.
• A working prototype (web/desktop/mobile) of the tool.
• A sample dashboard showing detected anomalies and visualizations.
• Documentation explaining the logic/rules/algorithms used.
• Optional: Integration of machine learning for anomaly detection or pattern classification.

 

Evaluation Criteria

• Accuracy and relevance of detected anomalies.
• Usability and intuitiveness of dashboard.
• Speed and scalability in processing large data volumes.
• Innovation in visualization and analysis.
• Completeness and clarity of reports.

Suggested Tools/Technologies

Python, Pandas, NetworkX / Neo4j (for graph analytics), React.js or Angular, Elasticsearch, TensorFlow/PyTorch (optional for anomaly detection), PostgreSQL/MongoDB, Apache PDFBox (for parsing PDFs)

Bonus Points

• Machine learning-based anomaly detection or fraud scoring.
• Cross-bank network visualization with risk heatmaps.
• Automated suspicious transaction report (STR) generation.

Deliverables

• Working prototype/demo.
• Dashboard with sample visualizations.
• Documentation (algorithms, system architecture).
• Deployment instructions or Docker container.