streamlining entry and exit strategies
Spatial data of high-risk (negative) areas was only available till pin code level and lacked granularity. This data was primarily acquired from the collections team by their experience of EMI collections in areas, and from industry and government bureaus. However, this data was not collated, verified, refined, or centralized; which reduced efficiency while approving loans. The sheer number of text addresses that needed to be translated to latitude hampered productivity and turnover time. Not just the number of records, but also the hap-hazard formatting of Indian addresses posed a challenge to get accurate results.
Using our proprietary geocoding engine, Geocoder, we converted Indian text address to latitude-longitude in record time. Geocoder is uniquely designed to deal with the unformatted and unorganized nature of Indian addresses. It works by segmenting text addresses into fundamental components and performing a hierarchical matching going down from pin code level. We geocoded high risk areas pan-India and corroborated with other risk indicators to build a comprehensive risk map indicator.
This provided a micro-level data on riskier areas pan-India. There was a significant reduction in processing time for the conversion of text addresses to a spatial database. An increase in accuracy and precision while translating text addresses. Seamless integration of database into a web-portal enabled a centralization of data and increased accessibility. Intelligent searching and querying of granular data on the portal enabled quicker and smarter risk assessment for loan approvals.
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