Solution

SME Auto-Decisioning

Fast automated credit decisions for small and medium businesses that stay explainable and manageable. The balance between speed, risk and operational predictability.

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Why SME is a special case

In most banks the SME segment falls into a trap: it is too small for corporate lending tooling and too big for retail tooling. So it ends up served by processes stitched together on the fly — and loses to fintech, which does not carry that historical inertia.

A mature stance on SME means admitting that this is a separate segment with its own decision rhythm, its own data sources and its own risk logic. Auto-decisioning is how a bank regains competitiveness in the segment — without breaking corporate process and without turning SME customers into second-class retail.

How to start without building a monster

The best first SME auto-decisioning project is one that touches one product, one segment and one clear goal. For example: automated decisions on overdrafts for SME customers with twelve months of transaction history with the bank. Such a scope can be launched in a quarter, measured and used as the base for expansion. Big ambitions come later.

CTA

If you need to figure out which part of SME lending you can realistically automate right now, a good starting point is an analysis of current decisions by type and an assessment of where automation will deliver fast effect without losing manageability.

Target Model

How It Should Work

A mature SME auto-decisioning contour rests on three pillars. First, clear borrower segments in which rules behave consistently, and a clear boundary beyond which the decision must go to a human. Second, trustworthy data: account turnover, tax discipline, credit history, signals from 1C and Soliq. Third, explainability: every automated decision must be explainable after the fact, ideally in language that a client and a regulator understand, not just a model analyst.

SME borrower segmentation and product baskets

Decision engine with prioritised rules and models

Integration with alternative data: 1C, Soliq, transactional profile

Fallback to manual underwriting with a clear boundary

Explainability: why this decision and which signals matter

Decision quality monitoring over time

Rule and model review process

Проблемы

Где обычно все ломается

No product segmentation: everything runs through one credit process regardless of size and segment

Decision rules are not formalised — they live in the underwriter's head

Alternative data (transactions, Soliq, 1C) is not used even though it is already available

Models, if they exist, are not validated — nobody knows how they behave over time

Exceptions are the default case, not the edge case — a sign that rules do not reflect reality

Consequences

What This Leads To

⚠ SME customers go to fintech, and the bank loses an entire segment — first by volume, then by service

⚠ Credit risk in SME stays opaque: the portfolio seems to work, but exactly how is unclear

⚠ Operational cost of processing SME applications is out of proportion with their margin

⚠ The regulatory argument on decisions has to be reconstructed manually, case by case

My Approach

How I Approach the Challenge

I do not start with «which model to pick». I start with a breakdown of the SME segments the bank actually has, the decisions already made on each, and which of those decisions could be automated without losing quality. It often turns out that 40–60% of decisions are one short scenario — but they are served by the same heavy process as complex deals. Just separating those two streams is already a big step forward.

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Team

How We Work

My Role

I separate decisions that can be automated from decisions that must remain human. I design a contour in which the automated decision is transparent and exceptions are managed. I help the bank align with risk management on rules the system can actually execute.

Team Role

The team formalises the rules, integrates data sources, configures the decision engine, sets up decision quality monitoring and the process for revisiting rules and models over time.

Important

Key Considerations for Implementation

🔎 Automation without clean segments is automation of chaos, not of decisions

🔎 Speed is only valuable while explainability is preserved — otherwise it is risk, not advantage

🔎 Alternative data without data quality discipline performs worse than classical sources

🔎 Any model decays over time — the review contour should be designed in from day one

Results

What Results to Expect

✓ Time to decision on a typical SME application down from days to hours or minutes

✓ Underwriters relieved from simple decisions — their time goes to complex deals

✓ Transparent explainability of every automated decision

✓ Manageable decision quality over time — models and rules stay alive

✓ Return of SME customers who used to drift to fintech

FAQ

Frequently Asked Questions

We are afraid of issuing loans automatically — that sounds like risk.

The risk is not in automation as such but in a poorly defined contour. If rules are formalised, the automation boundary is well chosen and the human fallback works reliably, an automated decision on simple SME cases usually turns out to be more accurate and more manageable than a manual decision made by a tired underwriter at the end of the day.

Do we need AI for SME auto-decisioning?

In many cases, no. A well-designed rule engine combined with a classical statistical model and alternative data gives an outcome that is enough for the bank. AI becomes useful later, once the base contour works and needs finer differentiation.

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