Lending has been integral part of our Society. All of us, directly or indirectly are tangled to some kind of lending. The oldest form of lending was in place thousands of years back where Borrower would exchange Goods or Services. When the Money system came in place, Lending become more mature with Savings society and Fund Society establishments. Today we can find many forms of Lending like Personal, Property and Business etc after the formal Banking setup.
Lending has become quite advanced now and has basic prerequisite, A Financial History. While more than two-thirds of the adult population has access to Banking and mobile money accounts, still close to 1.7 Billion adults across the globe remain unbanked. < 50% of this unbanked population can be found in Africa and Asian countries. Majority of this are not part of the formal financial services but that doesn’t mean they don’t use financial services. They practice unregulated financial services such as private lending, Pawnbroker etc which is not reliable and very expensive.
Further, Developing nations are struggling to provide jobs to its population, specifically the uneducated ones and hence promoting Entrepreneurship to create micro enterprises which indirectly produces Jobs and helps towards a balanced economic society. The financial institutes being the backbone of this ecosystem and giving financial aid to Micro enterprises to start, has to take huge amount of Risk due to the nature of Lending. Most of the micro enterprises doesn’t have any credible financial history yet in need of financial aid to grow. Here, Technology has to play a big part to transform the entire Lending process seamlessly with proper customer and risk assessment. Usually, these micro enterprises can produce large unstructured data during the lending application such as Personal Details, Movable and Immovable assets, business invoices etc. These data are not authenticated hence not all of them can be considered for a secured Loan. However, Machine Learning can play a vital role and can assist to do Risk and threat assessment on this data. Machine learning, which can handle a wider variety of data types in large volumes and analyze it quickly, makes it possible to add other types of data to the mix for decision making – data from social media activity, mobile phone usage and other unstructured data types. AI/ML based systems can be designed to consume such kind of data and predict consumer credit worthiness and risk involved for lending. Such systems does self-learning and keeps updating their algorithms so with time it becomes more Robust and Risk free. To give you a quick example, some fintech in Africa are offering loan based on Airtime Recharge history. Few are collecting data of merchants and small street vendors like Stock, Location, Products etc and giving micro loans.
Similarly, such systems can also be used for large or complex lending. Think of a scenario – SME lending for Business where Business entity has to produce lot of collaterals and data. All these data goes through lot of processes with human intervention and it takes considerable amount of time to process. Now, with the help of digital solutions, the system has capabilities to extract data from Papers using OCR tools and then doing optimization and data enrichment do get meaningful data only to be processed. In case of manual process, chances are high that some human error might occur and impact the risk whereas in case of Digital system any error or risk can be highlights automatically during the application processing.
In the credit market for SMEs particularly in emerging markets, credit scores and ratings are largely absent. Historically this has made it very difficult for lenders to transact efficiently in this market. This is now changing. The evolution of AI to include technologies like natural language processing (NLP) and sophisticated machine learning that can analyze immense volumes of unstructured data and make sense of them, creates an unprecedented opportunity to deliver the same benefits to the SME credit market that have been achieved in the consumer and corporate markets lending markets. AI technologies have the power to unlock Trillions of USD of debt every year that currently doesn’t get to creditworthy SMEs because of the cost and time involved in underwriting these loans is too high. To conclude, AI/ML based digital systems will be the future of lending. Gradually most of the financial institutes would move towards such systems and do risk free Lending.