Deterring Fraud, not Clients

Introduction

In attempting to facilitate convenient payment methods for their clients many businesses are faced with continued exposure to fraudulent cheque transactions. Still, the extent of damage caused by rejecting valid cheques is difficult to estimate: the immediate loss of revenue is probably less significant than the offence taken by the customer. Using optimization techniques based on past transactions and currently supplied data ChequeMate facilitates a positive payment experience for the client yet reduces the retailer's exposure to fraud.

Unlike Procrustes, ChequeMate won't shoehorn your business to match design limitations.

Configuration

ChequeMate is initially configured using a flexible rule-based system with all conditions carrying tunable weightings. A typical rule might state: If the Account number was supplied and it passed the Check Data Verification test confirming that it is a legitimate account number, then add a positive value towards immediate cheque acceptance. Another rule might state: if this is the third time an account number is being used within a day, then add a negative value towards cheque acceptance, perhaps resulting in a POS instruction to seek supervisor intervention, or to decline the cheque outright. Once transaction history grows, ChequeMate also makes use of neural network technology to further optimize decision-making.

Features

Intelligent Credit Limit

The transaction history and information supplied by the POS allows ChequeMate to determine a credit limit for each transaction. For instance, should a particular account provide a positive payment transaction history then, based on the value of those transactions and a tuning factor, the credit limit is determined. This results in customers being treated with individual "recognition" and rewards those with positive records by pre-empting a routine, time-consuming and sometimes embarrassing supervisor call to "clear" cheques above the fixed floor limit.

ChequeMate Suggested Actions

Predicting cheque fraud will never be an exact science. For this reason appropriate recommended actions are not always black and white (accept or decline), so ChequeMate returns a value within a tunable range of suggested actions to the POS. This value may result in a suggested action from "Approve" to "Call Supervisor" to "Contact Call-Center" to "Decline" etc.

Heuristic Engine Room

In keeping with this flexibility, each criterion used by ChequeMate is assigned a tunable weighting to ensure a response appropriate to the organization's culture and customer service ethos. As there are many criteria, with the option of easily adding others, determining appropriate values requires careful attention. To facilitate this task ChequeMate provides a GUI interface with sliders representing each criterion next to a sample of bar charts representing typical transaction profiles (VIP, High Risk etc). As the sliders are adjusted the implied results are immediately apparent as the suggested action shifts between categories.

Using past transaction data the H.E.R. can be used as a modelling tool, suggesting optimal slider values to minimize the acceptance of potentially bad cheques. An additional slider is used to tune between erring on the side of accepting a few too many bad cheques (being more "customer forgiving") or to minimize immediate financial risk by rejecting a larger number of "suspicious" cheques.

Artificial Neural Networks

Although the rule based criteria system does a good job of identifying a significant class of fraudulent transactions it may flag an unacceptably large number of false alarms. As a final option to calculate the optimal recommended action the H.E.R. uses Neural Network technology. Neural Networks are especially useful for problems which are tolerant of some imprecision and which have lots of training data available, but to which hard and fast rules cannot always be applied. As the transaction history grows the Neural Network option becomes more feasible and, indeed, powerful.

In its simplest terms, an artificial neural network is a model of interconnected nodes inspired by the densely interconnected structure of the brain. Each connection between nodes is assigned a weight that adjusts as the model learns. These connection weights store the knowledge necessary to solve problems. The distinction between a neural network and regular computer models is the ability to perceive new patterns and learn from data fed to the model.

Hierarchical System Architecture

ChequeMate works through a configurable hierarchy of servers that can be tailored to an existing IT infrastructure. Each node within the hierarchy can operate independently providing a resilient solution in the event of network instability. Each intermediate node (NT or Unix based) runs a server process requiring minimum system resources. ChequeMate utilises industry standard protocols TCP/IP and XML.

The Retailer's Dilemma

IT solutions to business problems frequently involve a compromise: customer friendliness is sacrificed for safety guarantees. ChequeMate maximises fraud detection yet provides the business with a mechanism to enhance and build up customer trust.

Screen Shots

The Engine Room
The Account Management Menu
The Purchase History Screen