Artificial intelligence (AI), along with credit analysis, is increasingly becoming an essential part of the fintech sector, especially commercial lending.
AI and large-scale data analysis can help bridge the gap between fully automated and fully manual credit assessment, allowing an efficient, semi-automated process while still preserving some of the customisation necessary to address the complex needs of borrowers trying to scale their businesses.
However, there are challenges to successful adoption of AI and advanced analytical approaches to credit, which include the lack of sufficient data to fit or train models and therefore gain confidence in their accuracy.
If one is mindful of these difficulties, AI, machine learning and large-scale data analysis can be useful tools to improve the efficiency of complex fundamental credit analysis.
A hybrid approach
A symbiosis between computer and human will play a vital part in the future of fintech lenders.
While there is not enough data to produce a general model that would accurately assess all corporate credit analysis cases, doing this manually requires the credit analyst to perform a very large number of tasks — some of which can be automated.
This hybrid approach is a pragmatic compromise, where computers perform various tasks to allow the analyst to be more efficient, but the analyst remains in the driving seat and is able to train the models and direct and shape the final outputs to ensure they are coherent and understandable.
This allows human judgement to always have an influence on the outcome and helps ensure understandability of outputs.
Fintech lenders will also need to apply forewarning capabilities moving forward. An example of this is the “Covid Vulnerability Rating” (CVR) Framework, which integrates over 133 Covid-19 stress scenarios with regional overlays.
It incorporates assumptions for impacts on key financial metrics such as revenue, operating costs, working capital.
The framework enables commercial lenders to re-underwrite loans and bring consistency to their credit approach through the crisis, running risk analysis on a consistent basis.
This framework helps lenders undertake portfolio diagnostics, rating loans based on their vulnerability to the new economic environment.
The ratings are based on multiple factors including liquidity, debt capacity, funding gap and profitability, and can be dynamically customised to reflect the lender’s credit risk criteria and appetite.
The fintech sector is revolutionising financial services and democratising industries that have remained unchanged and unchallenged for decades.
Fintech businesses are leveraging new technologies such as machine learning and cloud to create products and services that have the potential to transform the lives of billions of people globally.
So it should come as no surprise that there is a huge amount of emerging and promising career options in the country’s fintech sector, irrespective of whether you are starting out in the industry or looking out for more senior positions.
While there are numerous fintech companies looking for talent, the key is to focus on opportunities with businesses whose mission you believe in.
Look into what fintech companies are profitable (unfortunately, the list is not very long) as this is a key indicator of the strength of the business and whether it is able to turn a potential economic threat into a significant opportunity.
(The author is country co-head at an international bank)