Fraudulent Transactions: Hidden in Daily Life Purchases

This is my/ChatGPT’s perspective on discovering patterns of financial transactions that could potentially be fraudulent transaction activity occurring without the notice of consumers.

Although consumers can look at their previous financial transactions and confidently say that they made the purchases, but credit/debit card information is prone to exposure as a result of microchip and tap sensor technology becoming the norm of payment. If a card’s information is found and unlawfully used at ease, consumers cannot completely blame credit/debit card companies for the breach of the consumers’ finances, but get told their cards are blocked and have to open another account. then the question is for credit/debit card companies, what is the threshold for determining a normal transaction from a fraudulent transaction using financial transaction data?

The main credit/debit card companies, such as MasterCard and Visa, would want to prevent financial fraud since they have fraud detection systems implements for these scenarios, and they are essentially the technological building blocks for card payments as they collaborate with banks to process payments. If they can figure out a pattern in normal transactions that can make spotting fraudulent transactions easier, then fortifying their fraud detection systems would cost less instead of relying on advanced fraud detection systems, such as SEON, or artificial intelligence to monitor daily transactions. The following Python code was made using a credit card dataset from Kaggle in which lists completed card transactions from around the world.

https://github.com/Eanton22/INST414Modules/blob/main/Module3Code-2.ipynb

To find similarity in financial transactions, there needs to be patterns in how a credit/debit card information is entered, where there the transaction is made, the merchant group, and the type of transaction done. For a dataset, with respect to credit cards, the above Python Code creates at least three different entities to track similar transactions from the dataset. The first step is using a vectorizer converts the text of the data frame columns into integer values to use for cosine similarity calculation. Also, fetching all indices with a specified column value with the index function sets up the process for tracking transactions with a similar selected index. Then, any similar transactions are compiled and sorted into a list of 10 transactions. Finally, duplicate indices that are found within the getting indices section are tossed using the list and set methods to narrow the indices and print 10 similar transactions with a selected index.

After tracking similarities of financial transactions, using cosine similarity, the threshold for determining a normal transaction from a fraudulent transaction is finding a transaction similarity in the country of residence in which is the origin location of the completed transaction.

In the era of digital transactions and online commerce, such as Amazon and Netflix, the prevalence of fraudulent activities has become a significant concern for consumers and financial institutions alike. Fraudulent transactions can occur through various means, including stolen card information, identity theft, or sophisticated cyber attacks. This post delves into the nature of fraudulent transactions, looking at various financial transactions through a dataset, and explore how credit and debit companies respond when customers report such incidents.

Understanding Fraudulent Transactions:

Fraudulent transactions encompass a broad spectrum of unauthorized activities aimed at accessing a victim’s financial resources. Cybercriminals employ various tactics, such as phishing, skimming, and hacking, to obtain sensitive information like credit or debit card details, personal identification numbers (PINs), and even biometric data. Once armed with this information, perpetrators can make unauthorized purchases, transfer funds, or engage in other illicit financial activities, causing significant harm to the affected individuals. This can involve silent and hidden transactions of small amounts to prevent suspicion of the consumers; thus, the need for conducting research to discover transactions patterns for the fraudulent transactions.

Customer Reporting of Fraudulent Transactions:

The detection of fraudulent transactions often falls on the vigilant eyes of consumers. Upon noticing unauthorized activities in their financial statements or becoming aware of a lost or stolen card, customers are encouraged to promptly report such incidents to their credit or debit card companies. Reporting mechanisms usually involve contacting the company’s customer service helpline, accessing the online banking portal, or using dedicated mobile applications.

Credit/Debit Companies’ Response Mechanisms:

Credit and debit card companies prioritize the security and well-being of their customers. Upon receiving reports of fraudulent transactions, these financial institutions implement a series of response mechanisms to address the issue efficiently:

1. Immediate Freeze or Block:

• The card issuer may promptly freeze or block the compromised card to prevent further unauthorized transactions. This proactive measure helps mitigate potential losses and protect the customer’s financial assets.

2. Investigation:

• Credit/debit companies conduct thorough investigations into reported fraudulent activities. This may involve analyzing transaction histories, reviewing security logs, and collaborating with law enforcement agencies to track down perpetrators.

3. Refund and Reimbursement:

• In cases where customers are victims of fraudulent transactions, credit and debit companies typically offer refunds for unauthorized charges. This not only helps restore the affected individual’s financial standing but also reinforces trust in the financial institution.

4. Issuance of Replacement Cards:

• To ensure the security of the customer’s account, credit/debit companies often issue replacement cards with new account details. This measure is vital in preventing any future unauthorized use of compromised information.

5. Enhanced Security Measures:

• Learning from each incident, credit and debit companies continuously improve their security protocols. This may include implementing advanced fraud detection algorithms, two-factor authentication, and other measures to stay one step ahead of potential cyber threats.

Conclusion:

Fraudulent transactions pose a significant risk to the financial security of individuals in today’s interconnected world. The response of credit and debit companies is crucial in mitigating the impact of such incidents. Through swift action, thorough investigations, and proactive security measures, these financial institutions not only protect their customers but also contribute to building a safer and more secure digital financial landscape. As technology evolves, the ongoing collaboration between customers and financial institutions remains essential in the fight against fraud.