Fraud is a criminal offence where a perpetrator intentionally deceives a victim for an illegal gain or for depriving the victim of a legal right. According to the legal dictionary, fraud is “the intentional use of deceit, a trick or some dishonest means to deprive another of his/her/its money, property or a legal right.” Apart from monetary gain, there are other purposes of fraud such as obtaining a passport, travel document, or driver's license or qualifying for a mortgage.
In general, fraud involves the misrepresentation of facts, either by intentionally withholding relevant information or by providing false statements to another party for the specific purpose of gaining something. There are many types of fraud such as forgery, counterfeiting, tax fraud, credit card fraud, wire fraud, securities fraud, bankruptcy fraud, and internet fraud. These criminal activities are carried out by an individual, a group of individuals or a business entity. Fraudulent activities cost the global economy billions of dollars every year.
Financial fraud happens when a perpetrator deprives a victim of his/her money or harms the victim’s financial health through deceptive practices. There are different types of financial fraud:
The increasing use of the internet and other wireless communication methods have opened new avenues for fraudsters. In general, internet fraud or online fraud involves the use of the Internet and hiding of information or providing incorrect information for the purpose of tricking victims out of money, property, and inheritance. Some commonly found internet and wireless fraud types are:
A banking scam or bank fraud is the use of illegal means to obtain money, assets or other property held by a financial institution or to obtain money from a depositor by posing as a financial institution. Often referred to as white-collar crime, bank fraud usually requires some sort of technical expertise to commit.
The banking fraud types include accounting fraud (where organisations use fraudulent bookkeeping to seek loans from a bank), demand draft fraud (where corrupt bank employees write fake demand drafts which are payable at a distant city), uninsured deposits and (uninsured or non-licensed bank soliciting deposits). Bill discounting fraud, card skimming, cheque kitting, document forgery, cheque forgery, bank inspector fraud, impersonation, payment card fraud, stolen cheques, identity theft and wire transfer fraud are other forms of bank fraud.
Many industries such as banking and insurance, which are more vulnerable to fraud, use various methods to prevent it. In general, fraud prevention is a set of procedures and activities to prevent money or property from being obtained through false representations. In order to do successful detection, financial institutions must have efficient systems that can screen financial transactions, locations, devices used, initiated sessions and authentication systems.
Traditionally, financial institutions use rules-based systems to detect fraud. These systems perform several fraud detection scenarios, manually written by analysts. Once a transaction complies with these rules or scenarios, they are approved. Often, these rules-based systems require adding/adjusting scenarios manually and they may not be able to detect implicit correlations, making them both inefficient and ineffective in modern times. They cannot process real-time data streams that are critical for the digital space.
The artificial intelligence (AI)-based approach to fraud detection in financial institutions has received a lot of interest in recent years. They are different and more efficient than the traditional rules-based approaches, which detect fraud by looking at on-surface and evident signals. AI-based fraud detection digs out subtle and hidden events in user behaviour that may not be evident, but still signal possible fraud. Technologies such as machine learning help create algorithms that can process large datasets with many variables and find hidden correlations between user behaviour and potential fraudulent actions. Machine learning systems can do faster data processing with less manual work.
As fraud is typically an act involving many repeated methods, statistical data analysis techniques and artificial intelligence (AI) techniques that can search for patterns and anomalies in data can be used as effective ways to detect fraud. The statistical data analysis techniques of fraud detection include the use of:
Common AI techniques that are used to detect fraud are:
Fraud often comes as a predicate offence for money laundering. The proceeds generated from fraud must be laundered to conceal their illegal origin and to incorporate them within the legitimate financial system. Money laundering detection in financial institutions often collides with fraud detection as well. Therefore, financial institutions are required to coordinate their anti-money laundering (AML) and anti-fraud operations to prevent criminal activities and avoid reputational damage.
There are AML software that can effectively detect fraud. The Tookitaki Anti-Money Laundering Suite, an end-to-end, AI-powered AML/CFT solution, is helping financial institutions help detect fraud among many other predicate offences.
To know more about our AMLS solution and book a demo, please get in touch with us.