ML in Lending Business

CreLytics
CreLytics
Jun 7, 2019 · 2 min read
Photo by Luca Bravo on Unsplash

Lending has become quite advanced now and has an essential 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. Less than 50% of this unbanked population can be found in Africa and Asian countries. Think of a scenario — SME lending for Business where the Business entity has to produce a lot of collaterals and data. All these data go through a lot of processes with human intervention, and it takes a 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 the case of the manual process, the chances are high that some human error might occur and impact the risk, whereas, in case of Digital system, any failure or uncertainty can be highlighted automatically during the application processing. Machine learning algorithms gather data at a large scale, consolidate this data and identify patterns in this data from its lending history. This helps in developing a robust scoring module. Based on these patterns, it judges whether a borrower is creditworthy or not and regulates the process accordingly. So AI may help you make credit decisions at scale, but it is Machine Learning that enables you to improve your algorithms and ensure that you are one step ahead of the industry in understanding credit and market patterns. 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. Reach out to team@crelytics.io to understand how ML can help your business.

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