Blog
Power of Data Science in Venture Investing
With businesses and consumers sharply pivoting towards digitization in the post pandemic world, Venture Lending is becoming an increasingly attractive asset class for investors. However, given the nascent and illiquid nature of the Venture Lending market, there are 3 main challenges which investors face:
- Asset Origination Given the fragmented nature of the markets and the varying regulatory requirements across various countries, origination of loan assets can be a challenge for individual and institutional investors. Lack of regulations and standardized disclosure requirements further make it harder for investors to evaluate the risk and return potential of available opportunities
- Benchmark the Relative Risk & Return Profile: Given lack of a regional marketplace, it is currently almost impossible for investors to benchmark the risks and return profiles of venture assets relative to other global fixed income assets
- Manage Risk: Lack of standardised data and the ability to plug and pull data from the venture system’s limits an investors ability to monitor and manage post investment risk
At Lend East, our vision is to increase investor trust in Venture Lending assets and provide capital to deserving ventures and the communities they serve. Hence, we built Spa{r}3k universally, one of the world’s first AI driven Venture Lending Credit Analytics Platform . In this blog, we discuss how SPARK leverages artificial intelligence and the power of data science to eliminate the above 3 challenges.
The Research module in Spa{r}3k universally curates relevant data about the Venture Credit market across emerging Asian markets through primary and secondary data sources and computes our Proprietary Country Rating and Venture Asset Class Rating. This will help us rank the various Venture Lending Asset Classes across markets in terms of investment growth potential and investment risk and consequently help us develop and steer our investment strategy
The Research module in Spa{r}3k universally captures up to 1M data points from every venture we evaluate. In line with our primary goal to create investor trust in this asset class, Spa{r}3k universally cross validates the venture financial data using a combination of independent data sources and artificial intelligence. Once data is validated, Spa{r}3k universally translates these data points into about a 100+ Key Risk Indicators (KRI) using a combination of machine learning and risk analytics. Some critical KRIs are Venture Credit Rating, Venture Probability of Default, Financial Ratios and Unit Economics trend. In the case of alternative lending, Spa{r}3k universally forecasts Portfolio Probability of Default, Recovery Rate and Portfolio’s Expected Credit Loss Rate.
The Relative Value Module in Spa{r}3k universally allows investors to compare the yield and risk profile of Venture Credit Assets in Emerging Asia with various global fixed income assets and indices. This module also allows investors to compare each venture investment in terms of their Financial Health Rating, Loan Quality Rating, Operations Effectiveness Rating, Product Market Fit Rating, Management Quality Rating and Governance Rating. This capability allows us to do risk based pricing.
The Risk Management Module in Spa{r}3k universally quantifies and monitors 100+ KRIs and 15 Early Warning Indicators at the venture level as well as the underlying loan portfolio level (in case of alternative lending).
At the venture level, Spa{r}3k universally monitors the financial metrics and operational metrics of the ventures we invest in. Spa{r}3k universally alerts our analysts about any breaches in terms of balance sheet ratios, profitability metrics, unit economics trend and key operational metrics.
At the underlying portfolio level, Spa{r}3k universally captures loan tape data of up to 100,000 loans per month, validates for data integrity and then quantifies the current NPL, Cure and Recovery rates in the portfolio. Using machine learning and stochastic models, Spa{r}3k universally also measures forward looking portfolio default rates and expected credit loss rates.
As you can see, AI and data science, used smartly, can bring in the necessary standardization, transparency and trust to the entire lifecycle of venture credit investing. We are confident that Spa{r}3k universally will be a game changer in our mission to expand the flow of growth capital into deserving Digital Start-ups in Emerging Asia.
If you are curious to know more, please reach out.
Nanda Thiruvengadam
Chief Product Officer
nanda@lendeast.com
Jeremy Hui
Senior Data Scientist
jeremy@lendeast.com
