The rise of the machines vs the human touch

Thursday 25 January 2018

Alternative Lending

Ravi Anand, MD at ThinCats offers his thoughts and expertise on automation and artificial intelligence in the alternative finance sphere.

Ravi Anand, MD at ThinCats offers his thoughts and expertise on automation and artificial intelligence in the alternative finance sphere.

Amid current fascination with driverless cars, it’s worth considering how far we are from a driverless
credit process, as it were – what do algorithms and AI have to offer, and what still requires human

Algorithmic and manual lending processes have distinctive and often complementary characteristics.
At ThinCats, we believe both are necessary to correctly evaluate a business loan.

Algorithmic lending has become a game changer. It allows a vast amount of data to be analysed,
ranked and rated far more quickly than humans are capable of. The richness of such data is
increasing all the time, as is its immediacy. For instance, we use (among many other inputs) ‘days
beyond terms’ or DBT data, which measures the number of days beyond the contractual due date
that a business pays its bills based on tradelines that have been updated in the previous three

At ThinCats, we offer our investors an extra layer of support in their lending decisions, by providing
quantitative analysis of not only a business’ credit worthiness, but also the strength of its security.
Algorithmic lending works particularly well if one is able to aggregate more data on individual and
similar loans. This suits those making smaller loans to many borrowers. However, when it comes to
larger loans, there is less granularity – fewer data points from which to extrapolate.

For example, in September, ThinCats made a £6.7m loan to Chelsea Yacht & Boat Company. There
are much fewer loans of this magnitude and complexity made, and doubtless a vanishingly small
number with comparable characteristics to this business. This is where manual underwriting
capability is indispensable. Algorithms can remove cognitive biases from the lending process;
nevertheless, sometimes you need that human slant.

Algorithms have the ability to process vast amounts of data - but they can only learn from the data
that they are provided with and in the manner in which they are programmed. In the absence of
data, only a human can (so far) nose around the office or factory floor and get a feel for a business.
Only a human can distinguish between the qualitative factors that make businesses distinct.
Likewise, putting the right covenants in place for specific businesses remains down to human
experience and judgement, which is why quality secured lending still needs to draw on human
underwriting skills not required by unsecured lenders.

Take two businesses working in construction, for example. Assume they have indistinguishable
financial metrics and similarly experienced boards of directors – except they do qualitatively
different things. Business A digs the foundations, while business B installs the windows. To an
algorithm, they may look the same, but to a credit analyst, there is at least one important
distinction: the firm digging the holes gets paid first. That should make it lower risk in the event of,
say, a downturn leading to a liquidity crisis. Likewise, the question “what’s the money for?” currently
remains difficult to capture in data terms, but remains crucial in determining many loans.

ThinCats recently agreed a loan to south coast jewellers W. Bruford. In terms of judging the value of
security, it is normal to estimate the value of stock to be about 30% of cost in a stressed situation.

However, much of W. Bruford’s stock comes from either Rolex or Pandora, and both of these
suppliers operate a buy-back at cost policy, which is difficult to capture in terms of data. Knowing
that made the loan a more attractive, lower risk proposition for our credit team.

The sophistication of algorithmic lending is increasing all the time and will be further improved by
the advent of open banking, which will significantly expand the data available on which to make
decisions. For the foreseeable future, however, the combination of manual and algorithmic analysis,
especially with larger and more sophisticated deals, provides the level of service both our lenders
and borrowers require.

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