Insider and Employee Fraud
Quickly detect anomalies to determine who is a high-risk employee across all vital systems.
Fraud is an increasingly serious threat for businesses around the world, eroding data integrity and security, consumer confidence and brand integrity. When fraud is discussed, hackers or external fraudsters often come to mind.
However, insider fraud is a critical threat that is growing. According to Aite Group, insider fraud was predicted to have a resurgence in 2020, with 43% of financial institutions expecting employee fraud rates to increase in 2020.*
As shown in the chart below, personal pressures and poor market conditions can raise the incidence of internal fraud but organisational culture can reduce the trend.
Motivation or pressure can cause an employee to look for opportunities to steal; the employee may rationalise the actions by thinking it is just a loan or that they deserve the money.
Employee fraud tends to lessen during sound economic times and heighten when the economy worsens.
Consumers are concerned about inappropriate employee behaviours and want to be assured that their financial institution is monitoring employees’ actions to protect their data and accounts.
Employees often fear reporting fraud or suspicious activity out of fear of reprisals.
Employees who are motivated to steal will do so unless there is a strong ethical culture, internal controls that are observed regularly, and deterrents in place to convince the employee the risk is not worth the potential reward.
The economy has been strong in recent years, but when it slows, the rate of employee fraud incidents will rise.
Strong controls and an ethical environment won’t just hinder employee fraud incidents, but are also likely to attract and maintain good employees. Consumers will have greater trust in the financial institution if they know this environment exists.
Financial institutions that offer confidential reporting options available to employees will reap the benefits.
In order to detect potential insider fraud, businesses need to monitor and recognise unusual employee behaviour in real-time. Suspicious behaviour can be detected by using monitoring systems to track when employees perform unusual changes to information systems. Unfortunately, fraud may be detected only after damages have been incurred.
A better way to prevent fraud is to monitor data searches in order to detect when employees may be planning fraud. For example, if an employee is attempting to deplete a dormant account, the first step is to find inactive accounts with high balances.
By monitoring user queries, investigators can recognise when employees are looking for potential targets before a dormant account is re-activated or money is transferred.
Two employees conducting an excessive amount of activity on the same customer accounts can be a clear indication of collusion, especially if they are the only employees to access these accounts.
For example, if a back-office clerk and a bank teller are consistently viewing the same accounts, this can be an indication that they are working together to take over these accounts.
One way to identify and prevent this type of collusion is through comprehensive rules-based detection which will alert fraud investigators. This in combination with intelligent machine learning and behavioural profiling can provide organisations with a comprehensive understanding of the commonalities within their employees’ actions.
Technical countermeasures only address part of the problem.
Training and awareness are also key measures to inform employees on the proper processes. Even more importantly, it informs them that there are security measures in place which can often deter employees who may be considering committing internal fraud.
Employees who attempt to commit fraud are typically familiar with the controls that have been put in place and can circumvent them. For example, employees that know the transaction threshold can siphon off smaller amounts of money over a longer period of time to avoid detection.
An analytics engine — one that uses intelligent machine learning to quickly identify the normal behaviour of individuals and compare with other employees with similar roles — can be more accurate at identifying fraud attempts. For example, a back-office employee makes a query to discover inactive accounts just before they are automatically flagged as dormant; this behaviour can be flagged as suspicious when compared with typical queries conducted by peers.
Organisations typically segregate functions between roles to lessen the opportunities for employees to commit fraud. For example, in banks typically only back-office clerks can reactivate a dormant account, but they cannot transfer funds. Tellers, on the other hand, can transfer funds, but cannot change account status. For detecting such schemes, an anti-fraud system needs to monitor and correlate all activity across back-office, transactional systems, branch offices, e-channels and other systems.
Visual link analysis can be very effective in detecting suspicious events. It can uncover sophisticated scenarios that are difficult to uncover using traditional tables and charts. Using tools that can cluster events and identify trends with a visual display speeds up investigation and resolution.
By using link analysis to monitor and analyse employee activity, organisations can detect suspicious activity before any funds are lost or their reputation is tarnished. This monitoring works best when combined with an internal fraud training program and a reporting hotline for employees to report fraud. Employees are less likely to commit fraud if they know there is a greater chance of being caught.
Regular fraud and compliance audits don’t mean you are safe. The Association of Certified Fraud Examiners cites that internal audits only detect fraud 15% of the time, while external audits are merely 4%.
Thirteen percent of financial institutions admit they have not audited or reviewed employee fraud processes in the last two years, and 22% say they don’t know if they have conducted an audit. Audits clearly have limitations as sampling may not be enough to capture the whole story, and fraudsters who are always on the move may be too clever for inexperienced auditors. Not only that but many audits are heavily influenced by their assessment of internal controls which may or may not be adequate.
"According to Aite Group, insider fraud was predicted to have a resurgence in 2020, with 43% of financial institutions expecting employee fraud rates to increase in 2020."
"The Association of Certified Fraud Examiners cites that internal audits only detect fraud 15% of the time, while external audit merely 4%."
"Thirteen percent of financial institutions admit they have not audited or reviewed employee fraud processes in the last two years, and 22% say they don’t know if they have conducted an audit."
Quadrant Solutions Spark Matrix for Insider Risk Management 2021 reveals the top vendors for Insider Risk solutions. Organisations are trying to stop insider risk by identifying insider threats in real-time and investing in tools to help stop fraud before it happens.
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