This is the first in a series of four articles detailing Transparently.ai’s approach to harnessing artificial intelligence and machine learning to build its tools to detect accounting fraud.
Transparently.AI's product acts like a highly skilled financial detective, but one that works incredibly fast and never misses a detail. Its core function is to uncover hidden signs of potential accounting manipulation within a company's financial records.
For investors, it solves the problem of uncertainty and risk. It helps them identify companies that might be presenting an overly optimistic or misleading financial picture, allowing them to make more informed investment decisions and avoid potential losses.
For companies, it helps them understand their own financial health from an external, objective perspective. It can highlight areas where their accounting practices might be perceived as risky or opaque, allowing them to address these issues proactively, improve transparency, and build greater trust with investors and stakeholders.
In essence, it helps everyone see a clearer, more truthful picture of a company's financial reality.
What kind of detective work does our AI do?
Imagine our AI system is like a super-smart financial detective, and every company's financial reports are like a very long, detailed diary they keep about their money.
Now, a regular person might just read the diary and take everything at face value. But our AI detective doesn't just read; it investigates. With that analogy in mind, here's broadly how it works:
- It looks for odd handwriting or erased entries: It's not just checking if the numbers add up, but if they look too perfect, or if there are unusual changes in how things are recorded from one year to the next. Like if someone suddenly changes their writing style in a diary, it might make you wonder why.
- It compares diaries: Our detective doesn't just look at one company's diary in isolation. It compares it to thousands of other companies' diaries, especially those in the same "neighborhood" (industry). If everyone else in the neighborhood is talking about slow sales, but one company's diary boasts booming numbers, our detective raises an eyebrow.
- It connects the dots: It doesn't just look at one page; it sees how different parts of the diary relate. If a company says it's making a lot of sales, but its cash account isn't growing much, that's a clue. It's like if someone writes they're eating a lot of cake, but their weight is going down – something doesn't quite add up.
- It remembers past behaviors: Our detective has a long memory. It knows how this particular company usually behaves with its money. If a company suddenly starts doing things very differently without a clear explanation, that's a red flag. It's like a friend who suddenly starts acting completely out of character.
So, instead of just saying "this company made X profit," our AI detective says, "This company made X profit, but based on how it's recording investments, managing its expenses compared to others, and how its cash flow relates to its sales, there are some unusual patterns here that suggest we should take a closer look."
It's about finding subtle clues that indicate something isn't quite as transparent as it seems.
How does our AI learn to spot accounting manipulation?
Our AI learns to spot potential accounting manipulation much like a seasoned detective learns from experience, but on a massive scale. It actually does a bit of both: it looks for specific patterns and it pieces together a kind of "story" from the numbers.
Think of it this way:
First, for patterns, we've shown our AI detective countless financial "crime scenes" – real historical cases where accounting manipulation did happen, and also many cases where everything was perfectly legitimate. Over time, the AI starts to notice common threads and unusual fingerprints left behind in the manipulated cases.
For example, it might learn that companies engaging in certain types of manipulation often show a very specific, unusual relationship between their sales growth and their cash flow, or a sudden, unexplained change in how they value certain assets. It's like learning that a certain type of lock-picking tool is often found at a particular kind of robbery.
Second, for the "story" behind the numbers, the AI doesn't just see isolated patterns. It understands how different financial pieces should fit together in a healthy company. If a company's "diary" (financial report) tells a story where profits are soaring, but its cash reserves are shrinking, and it's constantly selling off assets, the AI recognizes that this story doesn't make logical sense. It's like reading a novel where the hero is supposedly rich, but keeps borrowing money and selling their possessions – the AI spots the inconsistency in the narrative.
So, the learning process involves:
- Seeing many examples: The AI is trained on a vast library of financial data, both good and bad.
- Identifying anomalies: It picks up on deviations from what's considered normal or healthy financial behavior, both in isolated figures and in how different figures relate to each other.
- Building a mental model: It develops an internal "understanding" of what a typical, healthy financial story looks like, and what kinds of twists and turns might signal a problem.
This combination of pattern recognition and understanding the logical flow of financial events allows our AI to become very good at flagging potential manipulation, even when it's subtle.
What makes our approach to risk assessment unique?
Transparently.AI's approach to identifying accounting risk stands out because it brings several powerful advantages that traditional methods simply can't match. Imagine the difference between a single human detective meticulously sifting through paper files versus a super-computer analyzing an entire library of digital records in seconds.
Here's what makes our AI-driven method unique and more effective:
Unmatched speed and scale: Traditional methods often involve manual review, which is slow and can only cover a limited number of companies or data points. Our AI can instantly analyze vast amounts of financial data from thousands of companies simultaneously, covering many years of history. This means we can spot issues much faster and across a much broader universe of companies.
Spotting the invisible patterns: Human analysts are brilliant, but they can only process so much information. Our AI can detect incredibly subtle and complex patterns, correlations, and anomalies across different financial statements and over time that would be virtually impossible for a human to notice. It's like finding a tiny, almost invisible thread connecting seemingly unrelated events in a complex puzzle.
Consistent and unbiased analysis: Humans can get tired, have biases, or overlook things. Our AI applies the same rigorous, objective analysis every single time, without fatigue or preconceived notions. This ensures a consistent and fair assessment of risk, free from human error or subjective judgment.
Early warning system: Instead of reacting to problems after they've become obvious (and often too late), our AI acts as an early warning system. By identifying subtle red flags and evolving risk patterns, it can alert investors and companies to potential issues long before they escalate into major crises. This allows for proactive decision-making rather than reactive damage control.
Holistic storytelling: Our AI doesn't just look at individual numbers; it understands how all the different pieces of a company's financial story fit together. It can connect a strange investment decision from years ago to a current struggle with profitability, building a comprehensive narrative of how risks evolve. This provides a much deeper and more insightful understanding than simply flagging isolated metrics.
The key benefits of using AI for forensic accounting analysis
Using AI for forensic accounting analysis offers users several critical benefits:
- Early detection: It identifies potential manipulation risks much sooner than traditional methods, allowing for proactive decision-making.
- Enhanced accuracy: The AI uncovers subtle, complex patterns and anomalies that human analysis often misses, leading to more precise risk assessments.
- Unbiased insights: It provides consistent, objective evaluations, free from human error or subjective judgment.
- Comprehensive understanding: Users gain a deeper, holistic view of a company's financial health by connecting various data points into a coherent risk narrative.
- Improved decision-making: Ultimately, these insights empower investors to make more informed choices and help companies improve transparency and trust.
Accounting risk clusters
Our AI uses pattern recognition to find potential accounting risks. Imagine our AI is like a highly trained medical diagnostician, and a company's financial data is like a patient's complete medical history – blood tests, X-rays, symptoms, lifestyle habits, everything.
When a doctor diagnoses an illness, they don't just look at one symptom in isolation. If a patient has a fever, a cough, and body aches, the doctor groups those symptoms together and recognizes a pattern that points to, say, a flu infection. They don't just say "you have a cough," they say "you have the flu," which is a cluster of related symptoms.
Our AI's pattern recognition works in a very similar way with financial data. Instead of just flagging individual "symptoms" like "this number is a bit high" or "that ratio is a bit low," it's constantly looking for groups of related financial clues that, when seen together, form a recognizable pattern of potential risk.
These groups of related clues are what we call risk clusters. So, the AI doesn't just see an unusual investment figure; it sees that unusual investment figure along with a lack of transparency in related transactions and a sudden change in capital expenditure. When these three "symptoms" appear together, the AI recognizes that pattern as a "risk cluster" related to Investing Activity.
So, pattern recognition is the process by which the AI identifies these specific combinations of financial behaviors and anomalies, and then it labels them as a particular risk cluster, much like a doctor labels a set of symptoms as a specific illness. It's about seeing the forest and the trees, but understanding how the trees form the forest.
There is a lot more to be written about risk clusters, and we will save that for the second installment in this series.
