# The rise of the machines vs the human touch Author: Pal Sinha, Barnali Author URL: https://financedigest.com/author/pal-sinha-barnali Published: 2018-01-30 Category: TECHNOLOGY Category URL: https://financedigest.com/category/technology Meta Title: The Future of Automation and AI in Alternative Finance Meta Description: Discover the impact of algorithms and AI on credit processes in the finance sector. Explore the unique benefits of both automation and human judgement at Tags: featured Tag URLs: featured (https://financedigest.com/tag/featured) URL: https://financedigest.com/the-rise-of-the-machines-vs-the-human-touchhtml ![undefined](https://prod.superblogcdn.com/site_cuid_cm5qst7v3003gwirgwqtxn8i8/images/fd300118-13-1736843028895-compressed.jpg) ![Ravi Anand, MD at ThinCats](https://prod.superblogcdn.com/site_cuid_cm5qst7v3003gwirgwqtxn8i8/images/ravi-anand-1736843028877-compressed.jpg) Ravi Anand, MD at ThinCats **_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 consideration? Algorithmic and manual lending processes have distinctive and often complementary characteristics. At ThinCats, we believe both are necessary to correctly evaluate a [business loan](https://www.financedigest.com/best-small-business-loans-speaks-to-fundbox.html "Best Small Business Loans Speaks to Fundbox"). Algorithmic lending has become a gamechanger. 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](https://www.financedigest.com/data-time-for-some-levelling-up.html "Data – Time for some “Levelling Up”"), 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](https://www.financedigest.com/uk-government-to-cap-energy-bills-for-businesses-bloomberg.html "UK government to cap energy bills for businesses – Bloomberg") based on tradelines that have been updated in the previous three months. At ThinCats, we offer our investors an extra layer of support in their lending decisions, by providing [quantitative analysis](https://www.financedigest.com/gift-packaging-market-quantitative-market-analysis-current-and-future-trends.html "Gift Packaging Market: Quantitative Market Analysis, Current and Future Trends") of not only a business’ credit worthiness but also the strength of its security. Algorithmic [lending works](https://www.financedigest.com/how-does-hard-money-lending-work.html "How Does Hard Money Lending Work?") 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](https://www.financedigest.com/top-benefits-of-invoice-discounting-for-small-businesses.html "Top Benefits of Invoice Discounting for Small Businesses"). 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](https://www.financedigest.com/to-get-employees-back-in-the-office-business-leaders-must-do-their-homework.html "To get employees back in the office, business leaders must do their homework"). Only a human can distinguish between the qualitative factors that make businesses distinct. Likewise, putting the right covenants in [place for specific businesses](https://www.financedigest.com/esg-investment-making-your-business-a-more-sustainable-place.html "ESG investment: Making your business a more sustainable place") 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](https://www.financedigest.com/are-businesses-using-cybersecurity-as-a-scapegoat-for-a-desperate-return-to-office-working.html "Are Businesses Using Cybersecurity As A Scapegoat For A Desperate Return To Office Working?") in construction, for example. Assume they have indistinguishable financial metrics and similarly experienced [boards of directors](https://www.financedigest.com/impact-com-announces-appointment-of-ning-wang-to-board-of-directors.html "impact.com Announces Appointment of Ning Wang to Board 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](https://www.financedigest.com/automated-liquid-handling-systems-market-global-leading-companies-analysis-revenue-trends-and-forecasts-2027.html "Automated Liquid Handling Systems Market Global Leading Companies Analysis, Revenue, Trends and Forecasts 2027") crisis. Likewise, the question “what’s the [money for?” currently](https://www.financedigest.com/the-problem-with-current-anti-money-laundering-processes-a-lack-of-context.html "The problem with current Anti-Money Laundering processes: A lack of context") 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’sstock 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](https://www.financedigest.com/audit-finds-holes-in-ecbs-management-of-bank-credit-risk.html "Audit finds holes in ECB’s management of bank credit risk") team. The sophistication of algorithmic lending is increasing all the time and will be further improved by the advent of [open banking](https://www.financedigest.com/open-banking-as-a-means-to-enhance-social-commerce-security.html "Open banking as a means to enhance social commerce security"), 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. [www.thincats.com](http://www.thincats.com) --- This blog is powered by Superblog. Visit https://superblog.ai to know more. ---