Ibm Business Analytics-case Analysis Analysis Essay Example

Automated Clearing House Transactions.

The system scans every transaction for indications of fraud and detects questionable or high-risk transactions using predefined guidelines. When fraud is identified, analysts receive an alert and temporarily suspend the transaction until a representative verifies its legitimacy. In case of fraud, the sender is refunded. Once the true identity is confirmed, the system can analyze relationships between individuals and apply advanced event processing to evaluate all transactions related to the entity and its affiliated parties.

The rules engine allows for alerting staff in cases where a transaction or the aggregation of a customer's transactions exceeds a specific threshold. It also enables Monogram to respond promptly to new types of fraud. An example of this is in 2010 when Monogram observed fraudsters receiving transactions in California, prompting analysts to add a rule in early November 2010 that would flag transactions above a designated dollar amount sent to California.

By making a simple change, IIS$I prevented $7 million in suspected fraudulent transactions in just three months. The ability to react quickly and easily to new patterns of behavior has helped increase customer satisfaction and stay ahead of fraudsters. Complaints of fraud in January 2011 compared to January 2010 decreased by 72%, with the largest reductions in Canada, Nigeria, the United States, and the United Kingdom. The IBM Global Center for Smarter Analytics saw a 40% increase in its ability to detect and interrupt potentially fraudulent transactions. Being able to detect and respond to fraud faster is crucial to protect consumers and our global network of agents against more sophisticated financial fraudsters.

The solution successfully halted IIS$30,000 worth of fraudulent transactions on the initial

day. Within 17 days of its implementation, it put a stop to IIS$1 million of fraudulent transactions. Overall, it has prevented a total of IIS$37.7 million in fraudulent transactions and safeguarded numerous customers from financial losses due to fraud. The main objective of the business was to detect indications of fraud and money laundering. To achieve this, predictive analytics has been employed. The functioning of the predictive analytics solution should be explained.

Money Gram International, present at 230,000 locations in 197 countries, is a dominant global money transfer service. As a result of its extensive reach, the company faces challenges related to preventing fraud and ensuring compliance with international business transaction regulations. Money Gram's business model solely relies on computer systems to facilitate speedy transactions. Therefore, there is no specific data element that links an individual with multiple transactions within the system.

The firm faced challenges in determining the total amount wired by each customer within a specific time frame. However, by implementing Predictive Analytics, the company improves its operations to gain a comprehensive understanding of its customers and effectively predict and prevent fraudulent activities. This involves establishing a system that primarily detects the true identity of Money Gram's customers and detects and halts redundant transactions, unauthorized money transfers, and similar activities. These measures prioritize the security and well-being of the customers while also benefiting Money Gram.

Monogram's fraud detection system identified the transaction mentioned earlier as suspicious due to a telephone scam. As a result, a Monogram representative reached out to the customer to inform her that the wire transfer had been halted and her money was being returned. Monogram's extensive fraud and anti-money

laundering program covers 230,000 locations across 190 countries and territories.