Automating Credit Analysis at Banks: How Data Management for Creatio Covers the Full Cycle — from Application to Committee Decision

Credit decisions are only as good as the data behind them. But in most banks, a significant gap exists between receiving a borrower's financial statements and delivering a structured conclusion to the credit committee. That gap is typically filled by an analyst with a spreadsheet — manually entering figures, calculating ratios, tracking quarterly trends, and assembling reports that need to be rebuilt from scratch every cycle. Sales'Up implemented a project for a banking client that closes this gap entirely. Using Data Management for Creatio, the full chain — from data import to the automated financial profile of the borrower — now runs inside Creatio. Below is a detailed account of the business problem, the solution architecture, and the outcomes for both front-line staff and management.

5 core modules automated 0 manual calculations in financial reports 4 quarterly periods in a single view 100% audit trail for all balance edits

Credit Analysis: Where the Real Costs Are Hidden

Credit analysis is one of the most labor-intensive processes in banking operations. According to McKinsey Global Banking Review, banks spend 30 to 40 percent of the credit cycle on data collection, verification, and formatting — leaving less than two-thirds of the cycle for actual risk assessment and decision-making.

30–40% of the credit cycle is spent on collecting and verifying data — not on assessing risk McKinsey Global Banking Review

The problem is not analyst competency. The problem is the tool. Spreadsheets are powerful for one-off calculations. They were never designed for a repeatable industrial process with hundreds of applications, hierarchical formula dependencies, and mandatory quarterly updates across an active borrower portfolio. Here is what actually happens in a bank where credit analysis runs on spreadsheets:

Every new application means a new file or a copied template. At scale — 50+ active borrowers — this creates dozens of disconnected files with no unified structure. Formulas are manually carried over between reporting periods. A single copy-paste error can corrupt the creditworthiness coefficient across the entire chain. Materials for the credit committee are assembled from scratch before each meeting — pulling figures from multiple files, formatting into a presentation. This takes hours, not minutes. There is no audit trail. If a number changes in a spreadsheet, who changed it, when, and why is impossible to determine. Quarterly updates across all borrowers require a full manual operation every cycle — a task that can take days.

Deloitte's Global Risk Management Survey finds that over 60% of financial institutions identify manual processes in analytics and reporting as one of their top three operational risks. Not cyberthreats, not regulatory pressure — manual data entry and spreadsheet-based workflows.

60%+ of financial institutions rank manual analytical processes among their top 3 operational risks Deloitte, Global Risk Management Survey

The cost of this risk is concrete. IBM Institute for Business Value research puts the average cost of a single operational error in bank financial reporting at $10,000 to $100,000 when regulatory consequences, rework, and delayed decisions are factored in. In corporate lending, where transaction volumes are large, an error in a single balance line can carry costs an order of magnitude higher.

$10,000 – $100,000 average cost of a single operational error in bank financial reporting, including regulatory consequences IBM Institute for Business Value

The question CFOs and CROs are increasingly asking: why is credit analysis — a process with direct P&L impact — consistently the least automated link in the chain? PwC reports that 73% of banks plan to increase investment in credit process automation over the next three years. The question is no longer whether to automate. It is how, and on which platform.

73% of banks plan to increase investment in credit process automation within the next three years PwC, Banking & Capital Markets Outlook

The Solution: Data Management for Creatio as a Unified Credit Analytics Platform

The mandate from the banking client was clear: migrate the financial analysis logic from spreadsheets into Creatio. Preserve the full calculation methodology. But make the process automatic, traceable, and scalable — so that a relationship manager sees a complete picture of any application without manual aggregation. Five core modules were implemented.

1. Credit Application with Automated Financial Indicator Calculation Previously, a manager opened a spreadsheet and manually entered a borrower's assets, liabilities, trends, and indices. Now these indicators are calculated inside the system automatically: data is imported from the bank's core banking system directly into Creatio. The manager sees a complete financial picture of the counterparty in the application card — whether assets are growing, what the balance trend looks like, where deviations appear — with zero manual data entry.

"The primary goal was to simplify the manager's work when reviewing credit applications for legal entities and individuals. The manager needs to see the client's assets and liabilities, and the balance trend — whether it is growing or whether the client is losing assets. This directly influences the credit decision." — From a working session with the banking client's team

2. Automated Financial Reports with Hierarchical Structure The financial report structure in the system mirrors the logic the bank used in spreadsheets: counterparty → application → sections → categories → indicators. Each hierarchy level plays a role in the calculation: deltas, trends, and forecasts are computed automatically. A key technical challenge: the bank uses quarterly reporting, but each application has its own individual reporting dates that do not align with Creatio's standard calendar-based date grouping. Sales'Up resolved this by building a custom period reference book — reporting displays correctly for every application without empty rows or null values distorting trend visualizations.

3. Editable Balance with Full Audit Trail Banking data is not always clean at the point of import. Figures may arrive with errors or require adjustment after additional information from the borrower. The manager can manually edit a balance value — and the system automatically recalculates all dependent formulas from the adjusted figure. Every edit is logged: the original value, the new value, and the timestamp are preserved. The credit committee sees not only the current figures but the full audit chain — including which numbers were manually adjusted and when.

4. Indicative Financial Report from Three Data Dimensions A complete borrower analysis requires data from three directions simultaneously: assets and liabilities (balance sheet), profitability (P&L), and cash flow. These three dimensions consolidate into a single indicative financial report, and the resulting indicators flow automatically into the final credit application planning stage. Managers no longer aggregate data from multiple files. Everything is in one place, within the context of a single application.

5. Quarterly Reporting and Creditworthiness Assessment Borrower creditworthiness is assessed quarterly. Ratios — current assets to liabilities, equity share, net profit dynamics — are calculated automatically. To support accurate delta calculations, a prior-quarter indicator mechanism is built in: the system pulls the previous reporting period's value and computes the change without analyst involvement. The forecast value for the next quarter is derived from a growth coefficient based on accumulated trend data.

Results: What Changed — for Relationship Managers and for Leadership

For Credit Relationship Managers The day-to-day operational picture has changed fundamentally:

Financial indicators are calculated automatically with each new reporting period — managers do not enter figures manually. Assets, liabilities, trends, and forecasts are visible in a single Creatio window within the context of the specific application. If data requires adjustment, the manager edits the balance value and the system recalculates everything downstream. Preparing materials for the credit committee no longer takes hours — the information is already structured in the system. Every change is logged. Audit trail replaces the black box of a shared spreadsheet.

For Leadership: COO, Risk Management, CRO For management, credit analysis automation is not only about speed. It is about process control and decision quality:

Operational risk decreases: a formula error can no longer pass undetected through multiple hierarchy levels and reach the committee unnoticed. The Head of Risk can review the current state of the application portfolio and the financial profile of any borrower at any time — without requesting reports from analysts. Standardization: all applications are calculated using a single methodology. There are no "custom" spreadsheet versions authored by individual managers. Scale without headcount growth: as application volume increases, the system handles more — the analyst team does not need to grow proportionally. The credit committee receives data in a standardized format that shows not just the figure, but its trend, forecast, and edit history.

"Data Management makes it possible to implement any financial analytical logic on top of Creatio — without writing code. The banking implementation confirmed this: a complex hierarchical model with custom time periods, automatic delta calculations, and formula error protection can be fully reproduced on the platform. For leadership, this means the calculation methodology is locked into the system — and no longer depends on which analyst is in the office today." — Vladyslav Lytvynchuk, R&D Leader, Sales'Up [DRAFT — subject to approval]

"Banking is one of the most demanding environments for Data Management. Every formula needs to be traceable, every edit logged, every coefficient reproducible. We implemented this in Creatio while preserving full flexibility for the client: the methodology can be updated without a developer." — Alex Andronik, CEO, Sales'Up [DRAFT — subject to approval]

The Next Step: AI Layer on Top of the Analytical Core

The current implementation delivers automated calculation and structured analytics. But the Creatio platform and the Data Management architecture already support adding AI functionality on top of this foundation — without rebuilding what is already in place. Four directions that map directly onto the implemented structure:

Early Warning System. An AI model analyzes balance trends, inter-quarter deltas, and behavioral patterns in borrower data — and automatically raises a risk flag before the manager opens the application. Not a cell color in a spreadsheet. A proactive system alert.

Automated Borrower Classification. Based on accumulated portfolio analytics, the system can automatically assign a credit rating at each financial data update — without manual scoring calculation.

AI-Assisted Committee Preparation. Creatio AI can generate text-based conclusions from structured data. The manager receives a ready draft credit memo based on the application's financial indicators — and edits rather than writes from scratch.

Portfolio-Level Predictive Analytics. The aggregated view across all borrowers enables portfolio analytics: which segments are deteriorating, where risk concentration is growing, what the expected portfolio quality looks like over a one- or two-quarter horizon.

According to Gartner, by 2026 more than 75% of banks that have implemented automated analytics platforms will begin using generative AI to automatically draft credit conclusions and reports. Banks that automate data collection and structuring today are building the foundation for this transition — without requiring a separate large-scale project later.

75%+ of banks by 2026 will adopt generative AI for automated credit memo drafting — contingent on having a structured analytics platform in place Gartner, Future of Banking Technology

About Sales'Up Data Management for Creatio

Data Management for Creatio is a Sales'Up product for building complex analytical and financial logic directly on the Creatio platform. It allows teams to configure hierarchical planning models, conditional formulas, multi-period reporting, and bidirectional synchronization with external systems — without writing code. The product is deployed across multiple industries: in pharma, for budget planning and financial tracking of service requests (Viseven case); in banking, for credit analysis automation; in distribution, for managing financial indicators across counterparty portfolios. Sales'Up is a certified Creatio partner delivering platform implementations and developing proprietary products for the Creatio Marketplace across 25+ countries. The team includes certified Creatio analysts and industry specialists with domain expertise in banking, FMCG, agriculture, and pharma.

Looking to automate credit analysis at your bank or financial institution? Request a demo — we will show how Data Management addresses your specific use case.