How Fintech Companies Are Using AI, Machine Learning To Create Alternative Lending Score Fintech companies lending in markets where adequate credit history, banking records, and tax-filing records, etc., are not available, especially rely on such alternative lending scores for their underwriting

By Tejamoy Ghosh

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Alternative credit scoring models and the lenders embracing them are making serious inroads into the sections of the market which were considered largely impenetrable or too difficult to underwrite. Developments in AI/ML and innovations in utilizing data outside the preset list of mainstream lending practices have made this possible.

Some of the visionary players in the market segments where inadequate data is a major impediment to underwriting and hence lending, are making great use of alternative credit scoring models using AI/ML on non-conventional data to profile and evaluate customers. These models often combine elements of different computer vision algorithms (for image segmentation, object detection), geospatial analysis, and NLP methods for information extraction from textual data.

This approach has turned out to be a game changer in "new-to-credit" segments. For some of the early movers in the lending space targeting the lower section of the MSME sectors where mainstream underwriting data and credit history files are quite thin, the AI/ML driven alternative credit scoring models are becoming increasingly integral to the lending processes and will be a key differentiator in the future.

Conventional methods for credit scoring followed by lending institutions rely on sufficient credit history (credit bureau data), formal banking and accounting records, tax return filing information for several years, etc. Alternative credit scoring models, on the other hand, use data other than the kinds listed above. Fintech companies lending in markets where adequate credit history, banking records, and tax-filing records, etc., are not available, especially rely on such alternative lending scores for their underwriting.

These alternative credit scoring models use data such as geolocation-based data on several economic, demographic, and risk indicators, certain similar kinds of indicators derived from satellite image data, other location-level sectoral economic trends are being used quite extensively in alternative credit scoring AI/ML models. Another type of data that AI/ML algorithms (for example, certain variants of deep learning models) are proving to be quite useful are business image data (for example, stock of goods, store space, store frontage and location-street, etc.). Also, modern alternative approach to AI/ML driven credit scoring make use of permitted mobile data (transactions SMS data, informal accounting data from mobile apps, for example) using certain regular expression based and/or NLP methods followed by ML modeling. One important aspect of alternative credit scoring approach is that this approach makes use of the alternative data, along with any limited banking data available or even any little credit history ('thin file') that may be available in some scenarios.

As we noted, the alternative credit scoring approach not only uses non-conventional data, but the data types are also of a wide variety (images, texts, along with numeric data). This makes specific kind of computing and data extraction techniques and AI/ML algorithms necessary to ingest and utilize most of these types of alternative data (like images, SMS scrapes, etc.) which wouldn't have been amenable to traditional data analysis methods. Carefully developed and rigorously tested ML models using such comprehensive data from multiple sources, are capable of highly accurate credit risk prediction. This enables fintech firms to address the critical data gap by substituting conventional credit scoring with AI/Ml based credit scoring models using alternative data.

The alternative credit scoring approach allows expanding the scope of lending manyfold to include a significant portion of underserved segments, thus enhancing revenue with appropriate credit risk management and pricing for the lenders as well as catering to the social cause of financial inclusion.

AI/ML solutions enabling such alternative credit risk modeling are also going to be a critical factor in bringing (almost) fully digital lending products to hitherto unexplored segments. The early movers who adopted AI/ML sooner than the rest, will have a major advantage in that space owing to their significantly evolved AI/ML practices and rich, organized internal alternative data they accumulated along with deeper understanding of the markets.

Tejamoy Ghosh

Head Data Science and Artificial Intelligence, Aye Finance

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