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Welcome to the First Edition of Pattern Chaser

They say history is written by the winners.

In the age of AI, it’s written by those who can read patterns others miss.

Welcome to the first edition of the Pattern Chaser.

The ink is barely dry on my leaving papers, but I’ve been hard at work putting together a charming selection box for you to enjoy and not an empty calorie in sight!

There are pivotal moments in history, when new technology changes everything and it is time to adapt or die (metaphorically speaking). I honestly believe AI is that paradigm shift of our generation.

Those who become experts at using this new tool will thrive. What about those who don’t?

History teaches us that you move with the flow or get left behind.

Pattern Chaser is about getting on the right side of history, becoming expert and using the new tools available so we are the ones adding value and shaping the narrative.

Join me as we navigate the rapids of a changing audit world and find out what AI can do for you.

The Easter Egg Hunt: Finding Hidden Risks in Your Data

Easter might be over, but the hunt isn’t.

Kids may have packed away their baskets, but we’re still searching for hidden treasures —

only ours come in the form of anomalies, control failures and patterns tucked away in plain sight.

Think of a case where everything looks squeaky clean… until it isn’t.

A mid-sized Financial Services firm thinks their vendor payments are routine—until a junior analyst runs an ad hoc analysis and discovers a sweet little anomaly: one vendor receiving a consistent round-figure payment every Friday afternoon.

Turns out, someone is padding invoices and funnelling funds into a ghost account.

Missed in manual reviews, caught by data analytics.

Voilà—golden egg found.

With machine learning models scanning for subtle patterns, sudden spikes, or behaviour shifts, a modern auditor gains a powerful tool to find hidden corners where fraud may lurk.

What hidden patterns are waiting in your audit data?

Don’t Put All Your Risks in One Basket

Your grandma probably said it first: “Don’t put all your eggs in one basket.”

Turns out, she was giving out internal audit advice before it was cool.

When it comes to risk coverage, relying on a single audit approach—or sampling just a slice of your data—is like inspecting one egg and assuming the rest of the dozen is safe.

Spoiler alert: sometimes it’s the one in the back with a crack.

Thanks to AI and data analytics, we can now test every egg—without breaking the carton.

No more praying that your sample was representative.

You get full-population assurance, richer insights and a more resilient audit plan.

Stick with me in the weeks ahead: I’ll be sharing ways to spread your audit attention wisely using smart, scalable data analytics processes and strategies.

No one wants egg on their face!

From Bunny Trails to Audit Trails

We’ve all been there— What starts as a quick check on one control

suddenly...

turns into a full-blown expedition through policies, permissions and a five-year-old shared drive folder labelled “misc_final_FINAL_v2”.

Auditors are no strangers to going down rabbit holes.

But the good news?

Not all trails lead to confusion. With the right tools, audit trails become clear maps, not chaotic mazes.

Digital audit trails, especially when combined with data analytics, provide a powerful record

of what happened, when and by whom. Instead of chasing down manual logs or cobbling

together spreadsheets, you can track anomalies, approvals and user actions in real time.

It’s not just about hindsight—it’s about getting ahead of the risk.

Because while bunny trails are cute in springtime, your audits deserve a straight path to insight.

Spring Cleaning for Your Audit Data

The bluebells are blooming, the inbox is overflowing and those spreadsheets… yikes.

Spring is the perfect time to shake out the digital cobwebs and freshen up your audit data environment.

Cleaner data means sharper insights, smoother workflows and fewer false positives clogging your findings.

Here are 3 quick wins to tidy up your audit analytics:

1. Retire Redundant SQL Views & Legacy Schemas

Many teams have old SQL views and schemas that no one uses anymore—but still pull system resources, confuse analysts, and (worst of all) lead auditors to incorrect conclusions.

Work with IT or data analysts to review your existing SQL schemas and views.

Archive or remove any that haven’t been accessed in the last 6–12 months.

2. Clean Up Noisy Logs That Add No Value

Blank values are the dust bunnies of your audit trail. Decide early: will you fill them with

estimates, drop them from your scope, or flag them for review? Consistency is

key—especially when feeding data into an AI model.

3. Tidy Up Your Data Dictionary (or Finally Create One!)

What’s the difference between trx_id, txn_id and transaction_id?
If your team can’t answer that confidently, it’s time to clean house.

Pro Tip: Store it somewhere accessible to auditors—not just tech teams.
It’ll become your most underrated time-saver.

Final Thoughts (and a Cheeky Ask)

Here’s to a new season of cleaner audits, sharper reports and insights that spark joy.

If you enjoy this newsletter, please share it with an interested friend and colleague.

👉 Become a regular subscriber here.

Watch out for next week’s fun and valuable insight into all things audit, data and AI.

Have a great week!

Tony
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