How Fraud Analytics Can Keep Your Money Safe

How Fraud Analytics Can Keep Your Money Safe

We’ve previously written about how data analytics can help save your business money, but what about protecting the funds and resources your company already has?

It is widely reported that cyberattacks are rising as are incidences of fraud. Indeed, a 2020 PwC study found that 47 per cent of businesses had at least one incidence of fraud in the past two years, with an average of six instances per company. The losses from these incidents for the 5,000+ businesses surveyed amounted to $42 billion – approximately $8.4 million per company.

As consumer costs rise and businesses find their budgets stretched more than ever, losing any funds through fraud has become all the more damaging. Because of this, leaders need to pull out every stop to prevent it. Here’s how fraud analytics can help.

What is Fraud Analytics?

Companies have been using anomaly detection and rules-based methods to combat fraud for decades. While these methods are effective, they have their limitations.

This is because rules-based tools only detect abnormalities based on explicit, pre-written rules, whereas advanced analytics uses a company’s existing data to spot patterns, learn trends, and eventually detect outliers on its own through the use of artificial intelligence, machine learning, and predictive analytics.

These advanced analytics tools can be used to automate and speed up some of the labour-intensive work, which reduces operational costs and leaves others free to concentrate on the arguably more powerful, preventative activity.

One sector that’s been heavily leveraging fraud analytics is finance. Traditionally, such organisations have relied heavily on manual, human intervention in the regulatory reporting process. However, with large swathes of data moving in and out of systems, the capabilities for humans to keep up are simply untenable.

How is Fraud Analytics Useful?

Financial data can be scrutinised in numerous ways to identify anomalies in patterns of consumer and/or employee behaviour that might indicate financial wrongdoing–both internal and external.

For example:

  • Ledger entries can be scrutinised for potential fraud or errors, using data analytics to identify suspicious entries.
  • Expenses in areas such as travel are often where unscrupulous employees could fudge numbers. This could be tackled by monitoring department spending over time to understand the average range for each division, and setting up an alert triggered if the department deviates from that range.
  • Contractor payments are common areas for fraudulent behaviour. Vendors may submit the same invoice multiple times, either by accident or to follow up on unpaid bills. You may pay the same invoice twice if you don’t have a system for tracking and flagging duplicates.

Financial data analytics can also be applied to a range of companywide performance indicators, such as monitoring company goals and objectives, building dynamic profit and loss statements, or streamlining budgeting and forecasting.

By evaluating historical data alongside forward-looking financial statements, analytic techniques can help to form an evolving forecast, which gives finance teams a greater understanding of the current and future financial health of the business. And, unlike the static reports used for accounting, data analytics offers dynamic analysis, allowing the user to ‘ask’ the data questions.

Humans Versus Machines

Despite strides in technological development, human intervention remains paramount in data analytics practices. While analytics techniques offer a fool-proof way of identifying issues, humans are needed to provide vital context, investigate suspicious activity and give it business relevance.

There will always be a high number of anomalies from the data analytics process, but very few will transpire to be errors and even fewer fraudulent transactions. Data professionals with an understanding of the business can use their judgment and intuition to weed out irrelevant information, explain most anomalies that appear, and further investigate those that warrant extra attention.

Interested in using your skills to help businesses to remain secure against fraud? The world of fraud data analytics is a fast-paced industry full of opportunities across countless sectors – check out our roles today.

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