The financial ecosystem in India has witnessed a shift in trends, especially as technology and its applications in finance have continued to develop. Personal finance has attained a higher priority as people realised the importance of financial planning and protection during the pandemic. This shift in the mindset led to many experimenting with digital offerings at a time when physical interactions were restricted. And it instilled the perspective of investing money for a better future.
But as Warren Buffet puts it, investing is “simple but not easy.” It requires perseverance to yield fruit in the long run. India has come a long way when it comes to investing, as people have become more aware of and can understand financial concepts. The same is true when it comes to Peer-to-Peer (P2P) lending. Since its introduction in India, followed by the regularisation by RBI, it has received sizable interest from investors over the past several years. P2P lending is widespread partly because it offers excellent interest rates while being a non-market-linked financial instrument.
When P2P platforms began operations in India, they used conventional lending methods and internal credit scoring models. They analysed borrowers’ credit scores using traditional algorithms, and the initial rates were relatively high. Over the years, these platforms have studied investors’ behaviour and how integrating artificial intelligence (AI) can support the expansion of Peer-to-Peer lending.
Technology has simplified everything. P2P lending has grown simpler since the emergence of Artificial Intelligence. Machine Learning algorithms can automatically recognise the rules and anticipate market volatility for improved decision-making. When the stakes are high, and we do not have much time, AI and ML focus on algorithms that aid in data-driven decision-making. The increased use of AI and ML in P2P lending is being fueled by improvements in efficiency, accuracy, investment velocity, cloud computing, and data accessibility. –
Here are some of the important features of AI and ML that are helping the P2P lending sector.
Risk analysis using a complex trading behaviour-system
Traders looking to invest in the market must assess the risk and decide where to invest. AI helps in detecting potential problems in advance, making borrower selection safer. They evaluate borrower profiles using complex digital footprint data that is different from what is used in conventional credit scoring systems. With sophisticated investing behaviours and strategies, AI and ML empower customers to analyse risk and approve profiles without human prejudice. By providing comprehensive and granular customer profiles, its scoring systems enable granting more secure loans to applicants with no credit history or those who are new to credit and retain the individual’s profitability.
Power of clean data
At times, data can be scattered and inaccurate. AI helps collate data and cleans it. It ensures a significant value addition for the borrower selection base analysis by collecting clean data, processing it, and organising it to forecast projections using pattern recognition. It analyses borrowers’ behaviour patterns and provides the following:
Valuable information regarding their complex and ever-changing practices.
Going beyond simple behaviour monitoring.
Making it easier for the investor to make the right choice.
AI for ‘hyper-diversification’
Investors can remain relaxed as AI tool assists in hyper-diversifying their investments among the large cluster of borrowers. Hyper-diversification mitigates the risk and thereby allows investors to enjoy the best returns on their investment. Better return on investments leads to building trust among investors.
Risk performance booster
Feature selection and deep learning are AI features that help improve risk performance concerning P2P lending. Lending platforms use it to engage with their investors to understand their expectations. It has seen an improvement in the risk performance of the platform, leading to a better return on investment.
With improved technology, where hyper-diversity is as low as Rupee 1, the default rate is reduced to the bare minimum. As a result, investors may expect risk-adjusted returns systematically primarily due to breakthrough artificial intelligence technology. The tectonic shift from the lower rate to returns of up to 10-12% is magical, and we are witnessing it.