Fraud in the financial services industry has hit peak levels, costing Australians over $2B and affecting more than a million people.
But despite this bleak situation, the good news for Australian financial institutions is location-based analytics can now be used to rapidly sort through millions of live financial transactions, isolating suspicious behaviour and detecting fraudulent activities in near real-time.
With new advancements to location-based analytics technology, large datasets involving millions of transactions across Australia can now be culled down to focus on suspicious patterns.
Identifying a pattern is the first step to understanding the data in front of you. By analysing a transaction’s origin, timing, amount and recipient, we can detect hotspots for further investigation.
For example, a series of money transfers originating from neighbouring locations and being sent to multiple recipients in a small area acts a strong predictor of transfer fraud.
If you have a spare five minutes, I recommend watching this great video which shows this capability in action.
While using location-based analytics – also known as location intelligence – to identify fraudulent activity is a relatively new concept for financial services companies, the approach has been widely used by the industry to make sense of business information for more than a decade. And it’s little surprise, with a bank’s transaction data alone offering a rich trove of customer behaviours and insights.
Advanced location intelligence models transform vast and varied data into actionable intelligence, instantly highlighting patterns and trends that would otherwise remain hidden.
Distribution network channels can be measured and refined based on customer behaviour and usage. Products and messaging can be tailored according to location and channel preference, and competitor influence, creating a truly integrated OMNI Channel experience, focused on the localised needs of the customer.
I think one of the most compelling examples of this sits with the Bank of America (BOA), which effectively used location intelligence to determine the value of its bricks and mortar assets.
Following the global financial crisis, BOA was forced to re-evaluate its entire network of 12,000 branches and ATMs. Traditionally, this task may have been seemingly impossible – but by using location-based analytics technology, BOA was able to analyse literally billions of transactions quickly and easily.
As a result, they were able to identify previously hidden customer trends to put in place a network and channel optimisation strategy that reduced its annual expenses by US$800M.