Solution

Early Warning System (EWS)

A contour for early identification of borrower deterioration through signals in data and behaviour — long before formal delinquency. Time is the main resource in problem portfolio work.

Discuss Your Setup

Why time is the main resource of problem work

The earlier a bank sees that a borrower is starting to struggle, the more tools it has to react. At an early stage there is room to have a conversation, clarify, offer a consultation, consider a moderate restructuring. At a late stage, when a payment has already been missed and the client’s cash gap has already grown, the room for manoeuvre shrinks fast. Both the bank and the client end up in a situation where every available solution is worse than the one that was possible a couple of months earlier.

In that sense EWS is an investment in time. It is not a separate risk model or a new reporting platform. It is the bank’s ability not to miss the moment when a problem still has a clean solution.

Start with what you already have

The fastest way to fail an EWS project is to start by hunting for new data sources. It always feels like the more «interesting» task, but almost never delivers fast effect. Fast effect comes from something else: carefully gathering what already sits in the bank’s data. A settlement account turnover that has dropped by a third; reporting that arrived late; tax arrears showing up in public registries; a deferral request that was discussed verbally — these are all signals the bank already knows but does not use systematically.

A first version of EWS built on the bank’s internal data delivers more value than the most advanced external sources rolled out without discipline.

CTA

If you want to understand which early signals are already sitting in your data and could work right now, a good starting point is a walk-through of transactional behaviour and reporting for one borrower segment. This usually surfaces three to five signals that can be wired into a contour within the first quarter.

Target Model

How It Should Work

A mature EWS collects signals from several sources at once: transactional behaviour (revenue drop, atypical outflows, late payments to suppliers), borrower reporting data (changes in key metrics), external signals (news, sanctions and tax databases, court records), loan behaviour (requests for payment deferral, schedule changes). All these signals are tied to the borrower, weighted by importance, and form an early state profile of the client. A signal on its own does not decide — a human does. But the human now has an argued reason to look at the client earlier than delinquency would force them to.

Transactional signal collection on corporate accounts

Reconciliation of borrower reporting against previous periods

Deadline and event monitoring on covenants

Integration of external sources: courts, tax, news

Signal aggregation and weighting model

A single borrower early-state card

Escalation: who sees the signal, who decides

Link to provisioning and problem debt work

Проблемы

Где обычно все ломается

Transactional signals exist in the bank but are not used for credit quality monitoring

Borrower reporting is collected but not automatically compared to previous periods

External signals (news, registries, courts) are handled through manual monitoring of key clients

There is no single client card where all signals appear together

Weak early signals get lost in general noise and do not reach decision-makers

Consequences

What This Leads To

⚠ Problems become defaults instead of becoming restructurings

⚠ The bank reacts to deterioration months late, losing the window for early intervention

⚠ Provisioning is reactive rather than proactive

⚠ Problem debt work becomes more labour-intensive than it could be

⚠ The regulator sees the absence of an early warning system as a maturity gap in the bank

My Approach

How I Approach the Challenge

I start with the question of which signals the bank already has in its systems but does not use for early warning. Usually half of an EWS project is not about creating new data but about extracting value from data that already exists but sits in isolated places. The other half is about building a contour in which signals turn into actions.

Recognize your situation?

Discuss Your Setup

Team

How We Work

My Role

I help the bank examine signals already available in its data and build a first early warning contour that delivers real effect in the first quarter. Separately, I work on the question of who in the bank is responsible for reacting to signals — without this, even an ideal EWS becomes decoration.

Team Role

The team implements signal collection from transactional systems, the core banking system, reporting and external sources; the aggregation and weighting model; a single borrower early-state card; escalation and links to the decision-making contour.

Important

Key Considerations for Implementation

🔎 An early signal is only valuable if it leads to action — otherwise it is just another report

🔎 The best EWS works on data the bank already has, not on hunting for new sources

🔎 Responsibility for reaction must be defined before implementation, not after

🔎 Too many signals are the same noise as too few

🔎 EWS must be part of the credit contour, not a separate risk island

Results

What Results to Expect

✓ Time to detect borrower deterioration cut from months to weeks

✓ A higher share of cases restructured before formal delinquency

✓ Proactive provisioning by signal rather than by fact

✓ Lower share of problem portfolio that has to be handled through heavy tools

✓ Readiness for regulatory expectations on early warning

FAQ

Frequently Asked Questions

Do we need an AI model for EWS?

Not in the first round. Most of the value in EWS comes from a disciplined aggregation of signals the bank already has — transactional, reporting, covenant. AI models become useful later, once the base EWS is working and needs finer signal differentiation.

We are afraid of false positives. How should we handle them?

False positives are unavoidable in any early warning system. The key is not to eliminate them entirely but to manage their proportion. The EWS contour must have an explicit path «signal → check → close without action» with capture and model learning. False positives handled systematically are not harmful — they make the contour smarter.

→

What else is worth exploring

Topics from the same area we usually explore together

Solution

These are not just articles

I do not just write about this. I can come in, examine your situation and design a solution for your specific landscape.

Discuss applying this →

← All Solutions

Ready to discuss your setup?

Tell me what's not working. I'll review the situation and suggest a concrete path forward.

Discuss Your Project Write on Telegram

Usually respond within a few hours