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Digitising the five Cs of credit

The “five Cs of credit” have stood the test of time for decades as basic guidelines for how lenders assess credit. Whether you’re assessing individual or commercial borrowers, these core criteria of capacity, character, collateral, capital and conditions are just as relevant today as they were 20 or even 30 years ago. But do the five Cs need modernising in the digital age? Let’s look at each in turn and discover the potential positive impacts of digitisation.


In every credit assessment, the lender needs to know the borrower’s ability to repay the loan while also meeting all other statutory and operational commitments, such as tax and payments to suppliers. This capacity is normally assessed by reviewing financial statements, tax returns or other sources of income and expense, as well as significant changes in assets and liabilities.

By digitising the assessment of capacity, you could take advantage of a combination of internal and external data, including bank account transactions (via open banking APIs), merchant activity and cloud accounting data, to build a real-time view of a borrower’s cashflow. Then you could calculate the firm’s or individual’s “debt capacity” – a measure of how much debt load they can safely take on without jeopardising the business. 

With digital tools, lenders can even forecast short-term changes in capacity by gaining direct access to the borrower's financial statements in a cloud accounting platform – or using transactional data from their core banking system. Automatically detecting, for example, a late payment from a customer, or an early payment to a supplier, might trigger a re-assessment of capacity and push real-time offers to the borrower of short-term cash-flow financing.


Traditionally, lenders have largely judged the character of a borrower by performing credit bureau searches on both the borrower and related parties, such as directors and beneficial owners. They must also make AML and KYC checks against various blacklists and other datasets, all of which can now be performed in real time using APIs. 

Looking ahead, digital tools could help you broaden the character assessment of a borrower to incorporate other available data such as social media and news or sentiment information. 

For example, a small business credit assessment could aggregate and analyse customer review data from search engines and review websites, using AI technologies such as natural language processing and sentiment analysis to identify key themes or trends. As a result, you might observe a business' declining customer service levels, high return and refund rates or issues with product quality. 


In many instances, the “second way out” for lenders is to liquidate collateral. So, you need to apply a reasonable amount of rigor to analysing that collateral – its quality, value, liquidity, secondary market and so on. But equally important is the “collateral perfection process” that shows whether you can rely on the collateral in a worst-case scenario. 

Some countries and regions have already digitised collateral transfer processes such as electronic titles and registry. Regardless, it pays to look at the overall collateral life cycle and see if there are other opportunities to digitise and streamline activities, reduce internal costs and improve the borrower experience. 

For example, you could give self-service loan applicants the option to locate the physical property they are looking to purchase from a real-estate website. This will improve the application experience and simplify information capture by prepopulating data fields for the borrower. Geotagging of property can also help lenders identify comparable collateral to assist with appraisal benchmarking and even concentration risk assessment.


Early in my former career as a banker, I remember we always used to ask if the borrower had “skin in the game.” In other words, were customers contributing their own equity to the transaction and not solely relying on the bank's capital to fund it? 

In loans secured by collateral, this kind of assessment is normally made using loan-to-value ratio calculations and similar. But as borrowing structures become more complex, with multiple borrowers, guarantees or collateral pledged from third parties, the assessment gets harder – particularly if collateral is already pledged to other lenders with a more senior ranking in the event of default. 

Lenders must therefore be able to perform capital calculations in real time, using up-to-date valuation and appraisal data and loan balances – and not relying on spreadsheets or siloed tools. Furthermore, you should incorporate the calculations into your early-warning systems to flag any severe capital deterioration as soon as possible.


Last but not least, conditions will typically mean the loan terms and conditions that help lenders mitigate some element of their risk. They could be in the form of conditions precedent or subsequent, covenants or other terms that form part of the loan agreement. 

The tracking and monitoring of these conditions should all take place in a loan management system that can automatically test covenants against the latest available financial data. Ideally, the system will not only flag breaches but also calculate – and trigger warnings of – continued decline in available headroom.

The road ahead

As lenders advance along their digitisation journey, focussing on the use of data to automate credit activities, the end-to-end process can be re-imagined:

  • Information Gathering – Focussing on auto-populated, pre-screened and benchmarked data to enhance the borrower experience and improve data quality.
  • Credit Assessment – Use of more frequent datasets (transaction or accounting data) that is automatically assessed, rated and decisioned.
  • Credit Presentation & Decision – To streamline processing of borrowers who are not eligible for automated decisions, credit presentations should be pre-populated with automated guidance and focus on key pain-points of the borrower that resulted in a failed auto-decision.
  • Servicing – Automated tracking and servicing of digital loans.
  • Monitoring – Centralised data with real-time dashboards and reporting in addition to automated checks against recurring covenants, conditions and other early-warning triggers.

The lenders who succeed in this journey will leverage both traditional and alternate data, technology and bring a renewed focus on automation and speed-of-decision to facilitate an efficient digital lending process.


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Matt Riggall

Matt Riggall

Head of Commercial Lending Vertical, Cap. Markets


Member since

28 Feb



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