Fraud Detection in Banking Transactions

Topics: Data management, Fraud, Data warehouse Pages: 17 (4057 words) Published: August 11, 2008


The purpose of this document is to detail the description of the Real Time (Active) Fraud Detection in Banking Transactions (FDBT) Project. This document is a key project artifact and is used during the design, construction, and rollout phases.


The objective of this project report is to capture the functional and non-functional requirements for the Real Time FDBT project. This report lists out the complete system requirements and design architecture of the project.

The requirements contained herein will include, but not be limited to:

Capabilities or system functionality – What a system does, to include, but not be limited to: Interfaces (internal and external hardware)
Business Rules
Data source and destination
Exact sequence of operations and the algorithms used in those operations Triggers or stimuli to initiate operations or to force a change in state Error handling, recovery and responses to abnormal situations Validity checks

Input/output sequences and conversion algorithms
Frequency of use and update
Constraints- Limitation imposed on the solution by circumstance, force or compulsion to include, but not be limited to: Design constraints based on TrinucInc IT Standards
Control and Governance constraints (internal and external)
Non-functional requirements to include, but not be limited to: Performance Requirements, Usability
Quality Requirements (audibility, reliability, maintainability, etc.) Business Continuity, Operational Support
Security and Control and Training

According to the National Check Fraud Center in Charleston, South Carolina, bank fraud alone is a $10 billion a year problem. This is nearly 15 times the $65 million taken in bank robberies annually.

The Concise Oxford Dictionary defines fraud as ‘criminal deception; the use of false representations to gain an unjust advantage'. Fraud is as old as humanity itself, and can take an unlimited variety of different forms. However, in recent years, the development of new technologies (which have made it easier for us to communicate and helped increase our spending power) has also provided yet further ways in which criminals may commit fraud.

As fraud attempts grow in both number and variety, financial institutions are challenged with the need for comprehensive, yet cost effective, risk management solutions. It is our belief that these fraudulent or suspicious financial transactions can be identified, characterized and red-flagged in real-time providing vital information to reduce their occurrences. For e.g. a check deposit followed almost immediately by a cash withdrawal would be a suspicious activity and warrant a red flag to check the customer’s motives. Banking databases with all the transaction information is readily available. We use this information coupled with our business logic to detect fraud and to develop the real time fault detector.

Types of bank/financial frauds
Check fraud

New Account fraud

Identity fraud

Credit/Debit card fraud

ATM transaction fraud

Wire fraud

Loan fraud


The research regarding the project was done two fold – Business Issues and Technical research. First step was to identify the various ways in which bank fraud occurs and come up with common sense solutions to them based on our technical knowledge base. Next was to come up with the software architecture with technical decisions on choice of RDBMS, ETL tool and OLAP tool.

Business Issues
Detailed list of ways fraud occurs and activities that could red flag the transaction as suspicious are (Note: Activities generally pertain to personal banking and not corporate accounts):

If check deposit is closely followed by cash withdrawal within say 10 hrs. If transaction type is above a specified number in 48 hours. If active more than one session at the same time.
If trying to withdraw more money...

References: Kimball, R., Reeves, L., Ross, M. and Thornthwaite, W. (1998). The Data Warehouse Lifecycle Toolkit, John Wiley & Sons, Inc.
Silverston, L., Inmon, W.H. and Graziano, K. (1997). The Data Model Resource Book, John Wiley & Sons, Inc.
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