CoreScore reads a company’s financial statements and answers three questions for the bank specialist: could this company default on the loan, how likely is it, and why? Lower-risk applications progress faster, higher-risk cases surface early, and specialists can focus their time on borderline cases.
CoreScore · Live System
Reviewing the statements, conducting an on-site visit, interviewing management, and going through committee approval. A single SME application takes a specialist 8–15 hours, even though the outcome of many cases is clear from the start.
As application volume grows, the bank must either hire more specialists or accept longer waiting times. Business owners wait weeks for an answer and may turn to a competing bank.
Two specialists may assess the same company differently. Without consistent criteria, explaining decisions and measuring portfolio quality become more difficult.
The loan amount is small, but the cost of analysis is high. Banks may therefore decline SME applications or serve the segment at a loss.
Upload an Excel file or enter the figures into the form: balance sheet, income statement, and cash flow statement.
Input: three financial statementsFourteen financial indicators are calculated automatically: profitability, debt-servicing capacity, turnover, and cash flow.
14 indicators · zero manual calculationsThe model compares the company with the outcomes of thousands of real businesses and estimates its probability of default.
Probability of default + ratingThe application is assigned to a traffic-light risk zone, and the specialist adds an explanatory credit opinion to the case as a PDF.
Zone · reasons · PDFSimplified procedure. Only 6% of all problem loans fall into this zone.
Standard analysis, but the specialist starts with pre-calculated indicators, benchmarks, and a draft opinion.
This zone contains 61% of all problematic loans. Conduct an in-depth review or decline the application.
Fourteen indicators, a risk score, and a rating are ready as soon as the report is uploaded.
Every company is assessed using the same rules. Results can be compared and tracked.
The system shows which indicators increase risk and where the company stands relative to businesses that repaid or defaulted.
AI converts the result into a clear Azerbaijani narrative. The specialist can edit it and download it as a PDF.
All applications and results are stored in the database. Auditors and the risk team can revisit them at any time.
Routine applications leave the queue, allowing specialists to focus on genuinely borderline cases.
There are two applications on your desk: one company will later default, while the other will repay. The system correctly identifies the problematic one in 8 out of 10 cases.
Nearly half of all defaulted loans—88 out of 198—are found among the 10% of applications the system ranks as riskiest. Closer review of this 10% can help prevent half of potential losses.
If the system says that 5 out of 100 similar companies will default, approximately 5 also default in practice. This makes the estimate useful for planning interest rates and provisions.
Each application is assessed using five separate methods: conventional bank scoring based on logistic regression and four modern models. When all five agree, confidence in the result is higher. When they disagree, the system warns the specialist to review the company more closely.
Probability, rating, risk zone, and reasons.
The specialist may disagree with the recommendation and record the reason.
The figures used, who made the decision, and when.
Model accuracy is monitored over time and the model is updated when needed.
The application flow, document formats, and current process are reviewed.
The model is calibrated using the bank’s historical loan data.
Real applications are processed, while decisions continue to be made exclusively by specialists.
Accuracy, hours saved, distribution across risk zones, and the implementation plan.
