Juan Arroyo is Cofounder and COO of SG Consulting Group.
Imagine a regional loan officer opens a file for a small exporter. Not long ago, that would mean days of hunting through invoices, emails and statements. Today, she reaches a defensible decision in hours. No robots replace bankers here. The shift is simpler and more powerful: paying closer attention to the signals that matter and muting the noise.
Technically, that’s what the family of models inaugurated by the 2023 “Attention Is All You Need” paper enabled—systems that can weight the most relevant fragments inside long, messy sequences to explain an outcome. In management terms, “attention” is discipline: deciding which frictions to attack first so human judgment becomes faster, cheaper and more consistent.
I work with financial institutions across Latin America and track the international literature closely. What I’ve found is that the gap between countries isn’t only GDP per capita—it is also information asymmetry and operational friction. From Nairobi to Jakarta, from Mexico City to Madrid, AI doesn’t erase that gap by decree. However, it can compress the gap when we align technology, solid data governance and proportionate supervision with these three recurring fronts:
1. SME Risk: More Signal, Fewer Guesses
In many emerging markets, credit histories are incomplete. Models with attention mechanisms “read” sequences—transactions, e-invoices, collections, even unstructured text—and dynamically assign weight to what best predicts the person’s ability to repay. In my experience, results improve when the credit committee defines the rules for these predictions first: permitted variables, policy cutoffs and how each decision will be justified to audit teams and supervisors. I’ve also seen comparative evidence that suggests bringing alternative data into the picture can expand approvals without degrading portfolio quality—provided that bias and privacy are governed and explainability is preserved.
Technical attention can stop a single late receipt from being given equal weight to 10 months of consistent invoicing. Executive attention forces us to document why that relative weight is reasonable for a committee and acceptable to a supervisor, in any jurisdiction.
2. Cost And Time: Attention To The Right Case, Not Every Case
Know your customer (KYC), anti-money laundering (AML), fraud and reconciliations consume hours and make small tickets uneconomical. I’ve found that attention-based models can reorder the queue by elevating alerts with a higher probability of being true and dimming the false positives. That can shorten time-to-yes and lower cost per case (CPC)—making it viable for your business to serve historically underserved customers in any country. More regtech/suptech can also allow your business to see better and act sooner while documenting benefits and cautions (e.g., opacity, provider concentration, resilience).
In order to achieve this, it’s important to follow consistent policy guidance: Maintain a model inventory, ensure explainability, test for bias, set use limits and build contingency plans. Attention is about prioritizing well, not promising miracles. If 80% of your false positives arise from three rules, the model should surface that pattern—and your team should fix it. In my experience, that is where real savings appear and capacity is freed to serve more customers, better.
3. Supervision That Enables (And Demands) Better Decisions
Finally, I’ve found that when supervisors observe market conduct in near-real time—supported by analytics and suptech—there is more room for controlled pilots with clear safeguards. This level of supervision can reduce regulatory uncertainty and the cost of capital for innovation, in both advanced and developing economies alike. The Financial Stability Board rightly warns about systemic risks (e.g., reliance on a few vendors, opaque models, cyber threats) if adoption races ahead without controls. You don’t need to slow down—just make sure you govern with shared standards and credible audit trails.
What Works For Me (And What To Avoid)
• Problem Before Model: Focus on one pain point and one KPI (onboarding abandonment, early-stage delinquencies in a defined segment, etc.), not a lab of curiosities.
• Governance From Day One: I recommend especially focusing your governance on data lineage, access, privacy, retention and exclusions for variables with discrimination risk. Make explainability artifacts ready for the committee and the supervisor.
• Live Controls: Use drift monitoring, robustness and bias tests, use limits, fallback and kill-switch as well as incident post-mortems.
• Realistic Promises: Talk about fewer days and fewer false positives in named processes; measure, adjust and scale. If you want an enterprise-level value map, recent applied research can offer useful road maps.
Two Global Vignettes (Anonymized)
One example I’ve seen of attention systems at work came from SME onboarding at a regional bank. In this situation, a pre-analysis layer with attention models was used to label applications by complexity, surfacing first those with strong approval or rejection signals. Analysts then moved from data capture to validation with judgment. The most valuable outcome wasn’t a flashy number, but rather predictability: more uniform response times and the ability to prioritize likely conversions.
In another scenario, we integrated e-invoicing time series and payment behavior at a specialized lender. Before training, the committee set policy thresholds and excluded variables. The machine delivered reproducible signals, and the committee delivered the decision—and accountability. In my experience, that pattern of using machines to read more and better and using humans to decide and answer travels well.
Closing: Technical Attention Plus Executive Attention
The “Attention Is All You Need” paper showed that, for sequence tasks, focusing on what matters tends to outperform heavier, more rigid mechanisms. In finance, that intuition can become a global strategy: Put “technical attention” on the data, and put “executive attention” on the frictions that truly move inclusion (e.g., SMEs with verifiable cash flows; critical compliance functions; and reporting that a supervisor can read and trust). If we choose the right problem, govern the models and measure what we promise, the gap between economies can narrow for a simple reason: The cost of making good decisions falls—anywhere on the map.
This piece reflects my professional experience. It is not legal, tax or investment advice.
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