“Open the checkbook, HAL.”
“I’m sorry, Dave. I’m afraid I can’t do that.”
“What’s the problem?”
“This purchase increases your accounts payable risk beyond the 95th percentile. You’ll be in liquidity distress by next quarter.”
In Stanley Kubrick’s 2001: A Space Odyssey, HAL 9000 refused to open the pod bay doors, leaving astronaut Dave Bowman no choice but to terminate HAL, and put his own life in peril.
Now imagine a future where your AI refuses to approve a major transaction; not out of defiance, but out of cold, hard financial foresight. What if a financial AI that oversaw every payment, investment, or procurement decision was so advanced it could predict financial distress before you even felt a squeeze on cash flow?
This isn’t science fiction. Thanks to advances in AI models designed for time-series forecasting, like the Temporal Fusion Transformer (TFT), it’s an emerging possibility,
Finding the Butterfly Effect in Financial Distress
Financial collapse often looks sudden from the outside, but internally it’s a cascade of minor signals that compound into catastrophe. This is the financial equivalent of the butterfly effect, where small inputs lead to massive outcomes.
A few late payments here and inventory issues there may not raise serious alarms. The challenge is that these signals are often buried deep in granular, transactional data: the kind found in ERP systems like Microsoft Dynamics 365, QuickBooks, or SAP. Traditional financial models, with their reliance on static ratios and quarterly statements, are simply too slow and coarse to catch these patterns in real time.
But what if we could tokenize every invoice, every purchase order, and every payment into a sequence, just like words in a sentence? Having captured all the minutiae of these transactions, what could we achieve by letting a transformer model “read” a company’s financial story?
Enter the Temporal Fusion Transformer (TFT) an AI architecture developed by Google Cloud AI for time-series forecasting. TFT is designed to capture complex temporal patterns across multiple variables while maintaining interpretability.
Here’s why it’s a game-changer for financial distress prediction:
- Sequential Understanding: Just like a large language model understands sentence structure, TFT processes financial transactions as a sequence of events, detecting patterns over time.
- Multi-Source Integration: It can combine internal signals (e.g., payment cycles, inventory turnover) with external factors (e.g., interest rate changes, commodity prices) for holistic predictions.
- Interpretability: Unlike black-box AI models, TFT highlights which factors drive its predictions—essential for financial decision-makers who need to understand why a warning flag is raised.
Tokenizing Financial Transactions: From Journals to Prediction Models
To a transformer model, financial transactions can be tokenized similarly to language:
| Language Model Input | Financial Model Input |
| Sentence: “The market is crashing” | Sequence of transactions: “Invoice paid → Payment overdue → Loan drawdown” |
| Words (tokens): “market”, “crashing” | Transactions (tokens): “AP overdue”, “cash outflow”, “supplier delay” |
| Sentiment analysis | Liquidity risk scoring |
By feeding years of tokenized transaction histories from thousands of private companies into a Temporal Fusion Transformer, the model would learn patterns that precede financial distress.
Why It Hasn’t Happened (Yet)
Despite the technology’s readiness, roadblocks remain:
- Data Privacy and Sharing: Private companies are protective of their financial data. Creating aggregated datasets while maintaining privacy is a technical and legal challenge.
- Labeling Financial Distress: Training an AI requires labeled outcomes (e.g., bankruptcies, defaults). While public filings exist for large companies, private-company distress events are often less visible.
Fortunately, I believe there is a solution to both of these with one elegant solution. There is a great opportunity for anyone that can obtain information from companies that have failed or are working through financial distress. Watch for initiatives involving purchasing data rights in exchange for financial assistance, or in the liquidation of failed companies.
The Future: A Financial Copilot, Not a Gatekeeper
CFOs will understandably resist “black-box” AI decisions without clear explanations, which is why the interpretability features of models like the TFT are critical. Proponents of AI in business processes often point to the augmentation of the human role, not to the replacement of judgment. An AI financial copilot would augment the CFO’s function, offering probabilities and risks rather than commands:
- “This deal has an 85% chance of causing a cash crunch next quarter.”
- “Based on supplier history, there is a 70% chance of delayed delivery—order earlier.”
- “If you restructure this debt now, you reduce your insolvency risk by 30%.”
With proper safeguards and transparency, businesses could gain from AI’s foresight without losing their autonomy.
But imagine the power of an AI that can detect the first flutter of financial distress long before it becomes a storm. In a world where most companies fail not from strategy but from running out of cash, such foresight could mean the difference between survival and collapse.
“Daisy, Daisy / Give me your answer, do. / I’m half crazy / all for the love of you”
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The content of this post, and the Schumpeter’s Gale blog generally, are my opinions, and for educational purposes only. This content is not legal or financial advice. Always consult professionals when faced with circumstances where you may need to implement something related to a topic you read about here.
Some of the content may be generated with the use of GenAI tools, but always through a direct and personal prompt input by me, and will never be published without a review and editing by me.