It is a well-known fact that the impact of technology is overestimated in short-term and under estimated in long term. This is probably no where more true than financial services. The changes happening in this space ranging from payments to credit underwriting to cryptocurrency is phenomenal. In this series I will try to outline the trends and my thoughts of how this is going to play out. Let us start with loans and under writing.
It is a well-known fact that the impact of technology is overestimated in short-term and under estimated in long term. This is probably no where more true than financial services. The changes happening in this space ranging from payments to credit underwriting to cryptocurrency is phenomenal.
There are two major trends in this area. One is disintermediation. P2P lending is growing at a significant pace. It is not only funding simple personal loans but more complex mortgage and commercial loans. These platforms significantly reduces the cost of disintermediation and as a result both the individual lenders and borrowers are getting price advantages. It has become so lucrative that there are institutional funds that are now investing in P2P platforms. There are broadly three types of lending that are covered in these platforms:
1. Unsecured P2P lending: Companies like Lending Club (recently IPO’d at over 2b), funding circle etc. are facilitating the P2P personal loans.
2. Secured P2P lending: Companies like eMoneyUnion (I am an investor) are pushing the boundaries on mortgage-backed P2P lending. This helps access to higher-risk borrowers.
3. Commercial P2P lending: Companies like RealCrowd, Realty Mogul are facilitating loans for investment in commercial/residential real estates.
The obvious question at this stage is how are these platforms managing risks and what will happen during an economic downturn. Some companies are developing credit models to solve this problem. Two such models are:
1. Social graph based model: Companies like Vouch (I am an investor) are using social graphs to decide the credit worthiness of individuals. They are building a credit rating model using the network of individuals, interaction patterns and the number of people in the social network who will vouch for the borrower to decide credit scores.
2. Big data based model: Companies like Inventure are building credit model based on data analysis generated by the behaviors of borrowers. For example, they will allow a borrower to download an app which will track the behavior of the user and build a a credit model around that. If the borrower is talking with her relative longer, that means she has a strong relationship and scores higher.
These are some of the fascinating things happening in the lending space of finance. Two other areas that I will cover in future are payments and cryptocurrency.
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