A financial model can contain many sensitivities without helping anyone make a better decision. Rajeev Khandelwal’s documented structured-finance background provides context for turning that analysis into useful commercial work. The purpose is to understand what changes when assumptions move. A lower revenue figure matters because it affects cash, obligations and available choices, not simply because it produces a different colour in a spreadsheet table.
Choose the real drivers
The most important variables depend on the business. A processor may be sensitive to input cost and recovery, while a service company may depend on utilisation and collection timing. The model should test those drivers rather than apply the same generic sales reduction everywhere. This requires operating knowledge, because the variables that matter commercially may not be the ones easiest to change in a financial workbook prepared far from the business.
Combine plausible pressures
Risks often interact. Lower demand can coincide with longer customer credit and reduced supplier flexibility. Testing each item separately may understate the combined funding need. Scenarios should therefore reflect believable operating conditions rather than arbitrary worst cases. The aim is to understand the path by which pressure reaches cash and identify the point at which ordinary management responses are no longer sufficient to preserve the planned financing position.
Connect results with actions
A useful downside case identifies what management would do and when. Options might include slowing expansion, reducing inventory or arranging additional support. Those actions have limits and consequences that should also be modelled. A response is not credible merely because it restores the spreadsheet balance; it needs an accountable owner, realistic timing and consistency with customer commitments, operational requirements and the financing terms already agreed with other parties.
Make uncertainty actionable
Credit Connect’s debt and project-finance offerings connect this subject to Rajeev’s wider advisory landscape. Sensitivity analysis earns its place when it informs capital size, reserves or operating choices. It should reveal which assumptions deserve monitoring and which events require a decision. That approach gives investors and management a shared understanding of resilience, making the model a tool for navigating change rather than a polished statement of one preferred future.
