#99 - What is fraud strategy for?

My biggest career realization was that there’s no such thing as predicting fraud.

In my mind, in the vast majority of cases, fraud fighters are reactive by nature. The rest is mostly vendor hype.

But if this is indeed true - what’s the role of a fraud strategist?

What’s there to strategize about being reactive?

A lot, actually.

I've built entire frameworks around that idea over the years, the reaction cycle chief among them - the six steps from noticing a gap to fixing it.

A good fraud strategy will make your reaction cycle better. Not only in terms of how fast one iteration takes, but also its scale - how many iterations can run in parallel.

And so a good fraud strategy is all about building capacity to react.

How do you build capacity to fight fraud?

I count four main pillars: skills, systems, processes, and data.

Let’s take a simple example - an investigation team that is overloaded - and see how we can approach solving it with these pillars in mind.

Building capacity through skills

The trivial thought, when thinking of an overloaded investigations team, is to train it to be faster. Let’s assume you’ve already done that and it didn’t help. Now what?

You can specialize.

You can segment the queue by different categories: fraud MOs, account age, trigger event, trigger rule, buyer vs. seller, geographies, etc.

Segmentation creates specialization, even if temporary. If this week I’m working only the stolen cards queue:

• I follow just one SOP

• I look for just one MO

• I recognize repeating patterns more easily

Naturally I would become more efficient (=faster) without a single minute of extra training.

You don’t need to split your team into different roles, a rotating “shift” will create almost the same effect.

Side note: You can also split your queues by case complexity, so as to better match them with individual team members. That would make your team both faster and more accurate.

Building capacity through systems 

What is the cost of an overloaded team? That we are too late when we handle critical cases. Fraud manages to slip by, or valued customers get a crappy experience.

A simple systemic fix can be to introduce a queue prioritization mechanism where critical cases get reviewed first.

Without changing the tools or the vendors you use, you introduce a system change that increases efficiency.

A different approach - one I’m sure many of you readers thought of immediately - is automatically resolving “easy cases” so humans don’t spend time on them.

Easy cases don’t necessarily have to be clear fraud cases. They can also be clear good cases.

And automation doesn’t have to be done with AI.

It can also be a high-level approval rule that overrides the risk flag and prevents the event from ever reaching the queue.

The thing to keep in mind with both of these examples is that building system capacity isn’t necessarily about adding to your system. 

Instead, it can be about building the connective tissue between its different components.

Building capacity through processes

Your team is already working with an investigations SOP (hopefully). I don’t want to suggest it just needs to get better.

But if you want to shorten investigation times, you can take the same SOP and structure it differently.

Many SOPs I see are riddled with signals that are both common and easy to check, but are too late in the process. Frontloading them would resolve a large chunk of cases early on.

A great example for that are quick approval signals.

A different approach could be with establishing better communication lines between the investigators and your rule writers.

Remember that high-level approval rule I just mentioned in the previous section? How do you come about it?

Simple - you take the quick approval signals you can fit into a rule and transport them from your SOP to your rule engine.

That’s automation too, even if it doesn't involve AI.

But it only happens if your rule writers and your investigators have a clear process of identifying these leads, and reworking the SOP once the new rule is out.

Building capacity through data

How you destress overloaded investigations teams with better data can take a lot of shapes and forms. It really depends on the situation.

But here are some guiding questions I’ll ask myself when I’m confronted with this problem:

• Is all the data available to the investigator?

• Is it available in one place, or across multiple portals?

• Is it trustworthy, or does it require validation?

• Is it visualized when needed?

• Is it loading fast enough?

All of these questions would have a significant impact on how fast you run investigations.

And we haven’t even started talking about acquiring more data.

The bottom line

Fraud strategy is all about building capacity to react.

When you have an overloaded investigations team, that means either increasing headcount, decreasing the load, or shortening the investigations themselves.

Barring the first option (throwing money at a problem is a strategy, but not a recommended one), we see that we have multiple ways to build capacity.

And most importantly, without spending a single dime.

But that is just an example.

Every capacity question you face can be solved when thinking about the four core pillars: skills, systems, processes, and data.

Have you solved a capacity issue without increasing headcount or AI? Hit reply - I would love to hear your story!

In the meantime, that’s all for this week.

See you next Saturday.

P.S. If you feel like you're running out of time and need some expert advice with getting your fraud strategy on track, here's how I can help you:

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