Andrew Tierney: The elephant in the room

by Andrew Tierney

Many finance providers have very little idea what fraction of their bad debt is fraud.

They will know the number of loans that have stopped making payments and calculate what proportion of their overall book this bad debt amounts to, but, unbelievably, they will not know what proportion of this total is fraud.

If the bad debt ratio hovers between 3-5% they’ll often consider that this is within their risk appetite, so why drill down any further into the data? The CEO will sit down at boardroom meetings and report this figure and everyone will be happy. Fraud could account for the bulk of this total, but the board will not know.

As a credit risk professional I find it amazing the number of companies that broadly fit this rough characterisation.

Many will not even bother to do the most elementary of checks to determine the scale of their fraud problem.

To me this is crazy because fraud is not going away, if anything it is a fast growing problem and becoming ever more sophisticated. There are literally tens of thousands of individuals whose full-time job is to scam finance providers with false loan applications. There are even localities in China, eastern Europe and parts of Africa whose main industry is this sort of fraud. 

The danger for companies that are not looking into their ‘bad debt’ figure is that their systems could have flaws that fraudsters are exploiting, but which they know nothing of. This is a recipe for disaster. 

Allow the criminals to leverage a flaw on a larger scale - over a period of as little as a few days - and your 3% could suddenly become 5%. Over a longer timeframe, it could even become 10%.

Suddenly not only is the board unhappy about your suddenly rising bad debt ratio, but your business model could be in doubt. During a downturn, your 3% issue could morph into something altogether more unpleasant. 

To companies that fit this profile, I often suggest that, as a first step to ID the scale of their problem, they should pull out all those cases that have never made a repayment. The chances are these are fraudulent, as there has obviously been no intention to repay.

Then I tell them to look for common denominators in the cases. This will give them an idea where they need to spend money to crackdown on the issue.

But this is fairly basic stuff. Ideally, lenders in this day and age should be using more advanced technology to find and then eliminate fraud.  

There is a massive array of tools out there that are really effective at tackling the issue. We haven’t got the space here to go into any great detail, so let’s just look at one area: device intelligence.

Often the same phone will be used to make multiple loan applications with a range of lenders. But the phone will have a unique ID in cyberspace that can be identified with the right software. So, despite a fraudster phoning from China and cloaking to appear local, the device can be red flagged and the loan application turned down.

Such tech can detect a range of suspicious features, for example whether an English-language device suddenly has a lot of traffic in a different language. A device may always have been linked to Commonwealth Bank, but then a range of different banks are using it, suggesting that it is compromised.

Beyond specific tools, Open Banking in general is itself opening up a new frontier in fraud detection for lenders wanting to reduce their losses to criminal activity. It allows algorithms to fine-tune lenders’ systems so they can better detect anomalies.

If a consumer is using Open Banking, a comprehensive profile will be built up of their real-time activity over a period of months. Anything unusual will be noticed immediately. Over time, this profile will become more nuanced and better able to ID illegal transactions.

For example, if someone tries to buy 10 TVs on finance (with the intention of selling them online for cash), Open Banking would be able to stop the process by preventing the second TV purchase from going through.

There is a wide array of tools out there for lenders to use that is really effective at driving down their fraud bill. However, the issue with all technology is that it usually comes with a cost, and if you don’t think you have a fraud problem in the first place, then you can’t really build a case for spending money on reducing it.

This head-in-the-sand approach is obviously short sighted. The only way to approach such spending is to work out what proportion of your bad debt is fraud, and then do the maths.

If some software can get rid of 80% of your fraud and thereby save say $0.5m (which feeds back to the bottom line), there is a convincing case for spending say $20,000.

I have enough experience of the industry to know that you can never do enough. The problem is much bigger than it used to be and you have always got to try and stay one step ahead of the game.

I remember Visa saying 10 years’ ago that the new chips then appearing on debit and credit cards were going to stop card fraud in its tracks, but within months of the new cards’ introduction they began experiencing card ‘skimming’ for the first time.

You really should never underestimate the issue.

Presenting an intelligent analysis and costing of your fraud problem to the board is the least you should be doing.

 

Andrew Tierney is a credit risk professional with over 30 years’ experience and is based in Sydney.   Andrew is a senior industry consultant with extensive experience in credit risk and product integration. Andrew consults to financial institutions on credit risk management frameworks through the whole credit lifecycle.