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How Your Data Can Stop Fraudsters From Costing Us Billions Each Year

Up to 2% of all transactions could be some form of fraud – Getty

In the latest Serious and Organized Crime Strategy, the UK government reported organized fraud costs businesses and the public sector at least £5.9 billion each year. However, the gray area between credit loss and fraud loss could mean this number is much higher. The existence of fraud within an organization not only results in a loss of revenue and reputation damage, but also has a potential impact on company growth. For example, if a bank fails to detect fraudulent activity or manage an acceptable risk tolerance, this could interrupt the customer experience for their good customers, while the bad customers remain unnoticed.

But it’s not just businesses suffering. In the year ending in June 2018, 3.3 million fraud incidents were reported, accounting for almost a third of all crimes. Every organization has an ethical obligation to protect their customers against financial crime. So how can businesses better spot the risk of fraud and, more importantly, prevent it from occurring?

The Most Common Types of Fraud

Around 2% of all transactions could be some form of fraud, equating to huge amounts of fraudulent activity. But with 164 million payment cards in the UK making billions of transactions, banks and vendors are left trying to find a needle in a haystack, especially when they don’t know what they are looking for.

These are some of the most common types of fraud that all businesses and consumers should be aware of:

Bust-Out Fraud AKA Sleeper Fraud

These are not the ‘can’t pay’ customers, but the ‘won’t pay’s. Sleeper fraud is the biggest type of fraud and is often mistakenly categorized as bad debt, with estimated figures of up to 20% of all credit loss in fact being sleeper fraud. Unfortunately, the defenses on the onboarding are quite weak as banks are trying to grow their footprint. Instead, banks need to be able to improve how they assess risk to drive better business decisions.

Lending Fraud

Lending fraud is when a criminal borrows money with no intention of paying it back. There are two types:

1) first-party, when criminals misrepresent information in order to obtain a loan, and;

2) third-party, AKA identity theft, where a criminal has stolen a fully legitimate identity to apply for lending. This is often enabled by details stolen through data breaches. Identity theft accounts for 14% of all fraud.

Mule Fraud

Mules are a mechanism used to create distance from the fund origin by transferring the money through multiple accounts before landing back in the hands of the fraudster. A mule account is one that has either been taken over (with or without the owner’s knowledge) or created for the sole purpose of being used for mule fraud. Over 90% of mule fraud is conducted through cybercrime.

Criminals often target vulnerable people, especially those financially struggling, and tempt them with cash to allow accounts to be used. A great example of this is international university students selling their student accounts when they leave the UK.

Internal Fraud

This type of fraud is committed by an individual against an organization and includes: collusion, when a criminal gets help from someone on the inside; theft, when someone steals from an account; and credit abuse, when someone uses identity theft or a direct debit to gain credit illicitly. Almost half of all organizations globally have been a victim of fraud.

Retail Fraud

In the build up to Black Friday, Cyber Monday and the Christmas season, retail fraud is rife. Vulnerable consumers are targeted through fake sites and phishing scams, resulting in their card and/or PIN details being stolen. Retailers have a certain degree of responsibility to educate their customers and warn them of potential scams.

However, it is also the retailers, who suffer a revenue loss through chargeback fraud. This could consist of a customer purchasing a product, using it then returning it, or claiming they never received a product that had in fact been delivered. Return fraud is also a commonplace where fraudsters will steal products then return them to make a profit.

How to Protect Your Business from Fraud Using Data

If a fraudster is committing third-party fraud, it is highly likely that they are also committing mule and first-party fraud, so it important to identify and put an end to illicit activity as soon as possible. All businesses, from banks to retailers, have access to huge amounts of transactional data which, when used in the correct way, can reveal hidden fraud activity.

However, data on its own doesn’t deliver value – you must derive intelligent insights from the data using network analytics to prevent fraud. Fraud is a series of behaviors and relationships that can be flagged and prevented before money is lost or a reputation is damaged. Many businesses have several disparate data sources, so it is crucial to centralize and enrich all this data, then analyze the generated networks. For example, collusion is very easy to see using networks by similar layering application information against an employee ID.

Leveraging transactional data enables businesses to spot inconsistent behavior. High-risk activity can be identified through network analysis using a combination of business-driven scenarios and artificial intelligence-based methods. Banks, for example, should notice a sudden change in spending patterns, especially with different locations or high-value purchases. Retailers have some responsibility to make sure stolen cards aren’t being used. Larger retailers can capture information such as IP address or delivery location, which could also flag unusual behavior.

Better customer insights enable organizations to make more informed decisions. By getting fraud detection right, businesses can drive growth and protect their customers at the same time. In the case of banks, understanding the different fraud typologies within your data allows you to reward your good customers by giving them the best customer journey, while identifying the bad to stem fraud losses. In the same way, all business can delve into their data to spot illicit activity and stop fraud in its tracks.

Source: on 2018-11-15 14:40:50

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