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Why Borrower Financial Analysis Slows Down the Credit Process

In many banks, borrower financial analysis remains the real bottleneck of the corporate credit process: Excel, fragmented spreadsheets, manual work and non-reproducible results. A look at the root cause and the way out.

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What really sits behind «a slow credit process»

When a bank complains that the corporate credit process is slow, the first suggestion is usually to automate approvals or roll out a new LOS platform. But when you look closer, it turns out that most of the delay comes neither from approvals nor from the platform — it comes from the preparation of borrower financial analysis. The analyst waits for reporting, pulls it into Excel, retypes the numbers, reconciles with prior periods, hunts for explanations of odd swings, writes the conclusion. All of this happens before the deal even reaches the committee.

This means that projects aimed at speeding up the credit process which do not touch the quality and discipline of working with borrower financial reporting usually deliver limited effect. The bottleneck simply moves — it does not disappear.

A less visible consequence — erosion of decision quality

There is another consequence that people talk about less often. When analysis is heavy manual work, analysts inevitably start saving time: they reuse templates from previous periods, copy conclusion wording, skip questions to the borrower, avoid re-checking suspicious figures. From the outside, decision quality looks the same as before. From the inside, it is eroding — and the erosion is hard to measure until the portfolio starts showing problems.

So the conversation about borrower financial analysis is not only about speed. It is about the quality of decisions and about how much the bank can really trust itself when it says «we have checked».

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If you sense that the corporate credit process is slowing down precisely at the financial analysis stage, it makes sense to address the root cause rather than the symptoms. A walk-through of one pilot borrower segment usually highlights concrete points of lost time and quality — and gives a starting point without launching a large programme.

How this shows up in real life

In most banks the picture looks roughly the same. A credit analyst receives a package of borrower financial statements — sometimes in PDF, sometimes in Excel, sometimes as scans of paper forms. Then the analyst manually retypes the numbers into a working spreadsheet, checks for consistency, calculates the standard set of ratios, draws conclusions and prepares a write-up. Each major deal takes between a day and several days; strategic clients take weeks. By the time the credit committee meets, the spreadsheet is closed, the PDF is signed, and there is no live dialogue with the data. If the committee asks a sensitivity question, the analyst walks out to recalculate.

Why the bank ended up here

The bank did not create this situation on purpose. It happened historically. At first, borrower data was limited and Excel was enough. Then deal volumes grew, the variety of reporting formats widened, calculation methodologies multiplied, analysts were added. Each one built a spreadsheet in their own style. Credit leadership left this area alone for a long time because from the outside everything looked fine — loans were being issued, the committee was meeting, reports were being prepared. The root cause is that financial analysis lives not in a system but in the skills of specific people. That makes the process non-portable, unpredictable and expensive.

What teams usually try — and why it does not fix it

  • A universal BI platform is purchased, and the bank tries to move calculations into it — but data still comes from Excel, and the gain is modest
  • Document management is introduced to store reporting — helpful, but it does not solve the structured-data problem
  • An internal «unified analysis» Excel template is drafted — six months later every analyst has their own version
  • IT is asked to «automate it» — IT delivers a form into which the numbers still have to be retyped
  • OCR of financial statements is piloted — accuracy is disappointing and the bank falls back to manual entry

What type of solution is actually needed

What is needed is not another application, but a structural shift: borrower financial reporting must become data, not an attachment. That means a unified metric model from which all analysis templates are derived; disciplined collection of reporting in a consistent structure; built-in consistency checks; a library of analytical scenarios; and access to live numbers right at the credit committee. It does not have to be a large project — you can start with one borrower segment and one reporting form. But you have to start by changing the status of the data itself.

What to check before starting

  • Where does borrower financial reporting actually come from — channels, formats, frequency, owners
  • How many different Excel templates for financial analysis live in the bank in parallel and how far they have diverged
  • How much time the preparation of one analysis takes on average — and how much of that time goes into retyping data
  • Which questions the credit committee asks most often — and why they cannot be answered in the room
  • How often contradictions appear between numbers in different borrower documents — and how they are resolved today

How to move step by step

  1. Agree inside the bank on a unified metric model and on which reporting forms take precedence
  2. Lock in a minimum standard for structured reporting collection in a pilot borrower segment
  3. Build the first contour in which borrower data is entered once and then reused by every analyst
  4. Add basic built-in consistency checks on the reporting data
  5. Bring live numbers directly into committee preparation — so sensitivity questions become a matter of minutes, not days
  6. Scale the approach to other segments only after the pilot has produced clear effect
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