How Fintech Companies Are Using AI and Machine Learning to Create an Alternative Lending Score

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Alternative credit scoring models and the lenders adopting them are making serious inroads into segments of the market that were once considered largely impenetrable or too difficult to secure. Developments in AI/ML and innovations in the use of data outside the predefined list of common lending practices have made this possible.


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

This approach has proven to be a game changer in the “new to credit” segments. For some of the early movers in the lending space targeting the lower end of the MSME sectors where underwriting data and credit history records are quite thin, alternative AI/ML-based credit scoring models are increasingly integral to lending processes and will be a key differentiator in the future.

Conventional credit scoring methods followed by lending institutions rely on sufficient credit history (credit bureau data), formal bank and accounting records, tax filing information for several years, etc. Alternative credit scoring models, on the other hand, use data other than the types listed above. Fintech companies that lend in markets where credit history, bank records, and tax filing records, etc., are not available, in particular rely on these 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, some similar types of indicators derived from satellite imagery data, other industry-level economic trends location are widely used in alternative credit scoring. AI/ML models. Another type of data that AI/ML algorithms (e.g. some variants of deep learning models) have found to be very useful is business image data (e.g. stock of goods, space of store, store facade and location-street, etc. ). Also, a modern alternative approach to AI/ML-based credit scoring uses authorized mobile data (SMS data of transactions, informal accounting data from mobile apps, for example) using some expression-based methods regular and/or NLP followed by ML modelling. An important aspect of the alternative credit scoring approach is that this approach uses the alternative data, as well as any limited available bank data or even any small credit history (“thin file”) that may be available in certain scenarios.

As noted, the alternative credit scoring approach not only uses unconventional data, but the data types are also of a wide variety (images, texts, as well as numeric data). This makes a specific kind of computational and data mining techniques and AI/ML algorithms needed to ingest and use most of these alternative data types (like images, SMS scrapes, etc.) that don’t would not have been suitable for traditional methods of data analysis. Carefully developed and rigorously tested ML models using such comprehensive data from multiple sources, are able to predict credit risk with high accuracy. This enables fintech companies to fill the critical data gap by replacing conventional credit scoring with AI/ML-based credit scoring models using alternative data.

The alternative credit scoring approach enables the scope of lending to be significantly expanded to include a significant portion of underserved segments, thereby improving revenue with appropriate credit risk management and pricing for lenders, while addressing 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. Pioneers who adopted AI/ML earlier than others will have a major advantage in this space due to their greatly evolved AI/ML practices and the rich and curated internal alternative data they have accumulated as well as a deeper understanding of the markets.

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