Case Study on Fraud Detection

According to the Association of Certified Fraud Examiners (ACFE), the detected fraud cases are around 10% of all cases. But, there are more than 85% accounted cases in which the median loss is approximately $950,000. Moreover, nearly one-third of the fraud cases occurred because of insufficient internal controls. Only 895 fraud cases are reported in the United States and Canada which is about 46% of all. More than 300,000 frauds were reported which costs over $ 1.4 billion. Cybercrime costs approximately $ 600 billion of the global economy, which translates to 0.8% of total global GDP.

• In 2017, United Kingdom fraud cases hit ￡2.11 billion. Cases rise from 212 in 2013 to 577 in 2017, with every case worth more than ￡50,000. UK loses over ￡190 billion per year which is approximately 9% of the UK’s GDP.

• In Australia, identity crime costs $1.6 billion each year, with the majority of about $900 million being lost by individual companies through credit cards.

• A major fraud happened by breaching systems at TJX companies that exposed 45.6 million credit card information between 2005 and 2007.

• In 2012, Adobe Systems was compromised by a hacking act that cost 40 million of the payment card information.

• Unauthorized financial fraud losses across payment cards and remote banking totaled ￡844.8 million that was acted by a third-party group. Though banks and card companies prevented more than ￡1.6 billion using fraud detectors in 2018.

Nowadays a fraud can be performed anywhere, anytime, from any place using modern technologies. Bank money laundering, credit card fraud, etc. are now very simple for digital thieves to perform far away from the crime scene. Most fraud occurs through the internet by performing unauthorized uses of private information such as bank account details, credit card details. Hence, fraud detection AI (Artificial Intelligence) is introduced to prevent those frauds. In other words, fraud detection is a way to protect money from false pretenses.

Mainly the problem of not using a fraud detector is huge financial risks. Financial risk ends at losing money or hampering one’s reputation in the market. So, one can prevent any sort of financial risk by just adding a fraud detector as a solution to their system.

A fraud detector can detect frauds occurring often through the internet. Fraud could be anything. Such as credit card fraud, bank money transferring fraud, insurance fraud, etc. involving exaggerating losses of money. There are many ways a person can perform a fraud activity that can be difficult to detect. Activities can be reorganization, downsizing, moving to new information systems, or encountering a cybersecurity breach. Frauds are actually a typical act that repeats simultaneously, making searching for patterns. A data analyst can prevent insurance fraud by applying algorithms to detect patterns and anomalies.

Rather, there is one type of fraud that happens most. Which is bank account takeover fraud, where someone steals access to the victim’s bank account using bots. Other banking-related frauds occur using malicious applications, use of false identities, money laundering, credit card fraud, mobile fraud.

Fraud in insurance includes diversion fraud, which is premiums’ embezzlement, churning fees, that are executed by stockbrokers for extra commissions. Frauds in government federal agencies such as departments of health and human services, transportation, education. These frauds are executed during the unnecessary billing procedure and overcharging in every step.

Though fraud detection is a hard job to accomplish by any sort of algorithms, there are some AI (Artificial Intelligence) techniques that are used to detect fraud in recent times.

• Data mining – This can classify groups and segments of data to search through millions of transactions to find patterns and detect fraud.

• Pattern recognition – That can erect patterns, clusters, and classes of suspicious behavior.

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